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    <title>Pinboard (rybesh)</title>
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    <description>recent bookmarks from rybesh</description>
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  </channel><item rdf:about="https://github.com/unum-cloud/usearch">
    <title>unum-cloud/USearch: Fast Open-Source Search &amp; Clustering engine × for Vectors &amp; Arbitrary Objects × in C++, C, Python, JavaScript, Rust, Java, Objective-C, Swift, C#, GoLang, and Wolfram 🔍</title>
    <dc:date>2025-11-13T19:53:54+00:00</dc:date>
    <link>https://github.com/unum-cloud/usearch</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[Smaller & Faster Single-File Similarity Search & Clustering Engine for Vectors & 🔜 Texts]]></description>
<dc:subject>clustering search database vectors</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:rybesh/b:3b2a6b016f20/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:clustering"/>
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	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:database"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:vectors"/>
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<item rdf:about="https://www.sqlite.org/fts5.html">
    <title>SQLite FTS5 Extension</title>
    <dc:date>2025-08-25T16:06:51+00:00</dc:date>
    <link>https://www.sqlite.org/fts5.html</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[FTS5 is an SQLite virtual table module that provides full-text search functionality to database applications. In their most elementary form, full-text search engines allow the user to efficiently search a large collection of documents for the subset that contain one or more instances of a search term. The search functionality provided to world wide web users by Google is, among other things, a full-text search engine, as it allows users to search for all documents on the web that contain, for example, the term "fts5".

]]></description>
<dc:subject>sqlite database search</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:rybesh/b:1b1689513688/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:sqlite"/>
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</item>
<item rdf:about="https://simonwillison.net/2023/Jan/13/semantic-search-answers/">
    <title>How to implement Q&amp;A against your documentation with GPT3, embeddings and Datasette</title>
    <dc:date>2023-01-16T20:35:22+00:00</dc:date>
    <link>https://simonwillison.net/2023/Jan/13/semantic-search-answers/</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[If you’ve spent any time with GPT-3 or ChatGPT, you’ve likely thought about how useful it would be if you could point them at a specific, current collection of text or documentation and have it use that as part of its input for answering questions.

It turns out there is a neat trick for doing exactly that.]]></description>
<dc:subject>semantic search conversation query periodo</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:rybesh/b:3ddae0513476/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:semantic"/>
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</item>
<item rdf:about="https://www.bellingcat.com/resources/2022/08/09/using-new-tech-to-investigate-old-photographs/">
    <title>Using New Tech to Investigate Old Photographs - bellingcat</title>
    <dc:date>2022-08-11T14:41:30+00:00</dc:date>
    <link>https://www.bellingcat.com/resources/2022/08/09/using-new-tech-to-investigate-old-photographs/</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[New tools and a wealth of online data make it easier to navigate more than 150 years of evolving landscapes, cities, buildings and street names. Reverse image searches, Google Lens, digitised newspapers, heritage and auctioneering websites, AI colourisation and tools such as Peakvisor can help add valuable information and understanding to historic art collections.

These tools and methods allowed us to pinpoint the location of several of these photographs. Here’s how we did it — and how you could, too.]]></description>
<dc:subject>search mapping image recognition inls620</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:rybesh/b:113693e17cce/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:search"/>
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	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:image"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:recognition"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:inls620"/>
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</item>
<item rdf:about="https://saigoneer.com/saigon-environment/25331-pure-passion-turns-dark-the-unexpected-dangers-of-the-wild-turtle-trade?fbclid=IwAR3NY5AGWRgliqlB210kIBEh46-_4KfnpyWWCHMc-VMKohc3k1dPIBxKvLE&amp;_branch_match_id=833804353814260420&amp;_branch_referrer=H4sIAAAAAAAAA8soKSkottLXL07PK9JLztfPtnQM9XJPLi8sTAIA0jFTMRsAAAA%3D">
    <title>Pure Passion Turns Dark: The Unexpected Dangers of the Wild Turtle Trade - Saigoneer</title>
    <dc:date>2022-05-31T19:01:35+00:00</dc:date>
    <link>https://saigoneer.com/saigon-environment/25331-pure-passion-turns-dark-the-unexpected-dangers-of-the-wild-turtle-trade?fbclid=IwAR3NY5AGWRgliqlB210kIBEh46-_4KfnpyWWCHMc-VMKohc3k1dPIBxKvLE&amp;_branch_match_id=833804353814260420&amp;_branch_referrer=H4sIAAAAAAAAA8soKSkottLXL07PK9JLztfPtnQM9XJPLi8sTAIA0jFTMRsAAAA%3D</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[Many of these forums claim to exist just to share pet-keeping experience and knowledge about ornamental turtles, but there are sadly no — or very few — posts concentrating on features, habitats, and requirements to take care of them. Finding information and images on these forums is not as simple as I thought it would be because posts related to buying and selling endangered animals use slang phrases and typographical tricks. Khánh says: "At first, I didn't know about these regulations, so my original posts were erased.”

Members of these groups strictly avoid typing out words such as “mua” (buy), “bán” (sell), “giá” (price), “thanh lý” (sale), etc. Instead, users add a period in between letters in order to avoid Facebook's censorship algorithm. Other tricks include using the rice emoji to indicate price (“lúa” is a slang word for money) and translating species names into English, such as "3G" for snail-eating turtles (rùa Ba Gờ) and "núi gold" for elongated tortoises (rùa Núi Vàng).]]></description>
<dc:subject>language search semiotics</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:rybesh/b:bf4e91d19036/</dc:identifier>
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</item>
<item rdf:about="https://markusstrasser.org/p/bcd8bded-7136-4bb4-8f97-e8a3a7b6d926/">
    <title>The Business of Extracting Knowledge from Academic Publications</title>
    <dc:date>2021-12-05T13:42:39+00:00</dc:date>
    <link>https://markusstrasser.org/p/bcd8bded-7136-4bb4-8f97-e8a3a7b6d926/</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[TL;DR: I worked on biomedical literature search, discovery and recommender web applications for many months and concluded that extracting, structuring or synthesizing "insights" from academic publications (papers) or building knowledge bases from a domain corpus of literature has negligible value in industry.]]></description>
<dc:subject>search recommendation selection nlp scholarlycommunication</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:rybesh/b:6b242f135bf3/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:search"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:recommendation"/>
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<item rdf:about="https://github.com/nextapps-de/flexsearch">
    <title>nextapps-de/flexsearch: Next-Generation full text search library for Browser and Node.js</title>
    <dc:date>2019-02-09T22:45:22+00:00</dc:date>
    <link>https://github.com/nextapps-de/flexsearch</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[Web's fastest and most memory-flexible full-text search library with zero dependencies.

When it comes to raw search speed FlexSearch outperforms every single searching library out there and also provides flexible search capabilities like multi-word matching, phonetic transformations or partial matching. Depending on the used options it also provides the most memory-efficient index. Keep in mind that updating and/or removing existing items from the index has a significant cost. When your index needs to be updated very often then BulkSearch may be a better choice. FlexSearch also provides you a non-blocking asynchronous processing model as well as web workers to perform any updates or queries on the index in parallel through dedicated balanced threads.]]></description>
<dc:subject>javascript search</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:rybesh/b:6063d9ac25f8/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:javascript"/>
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</item>
<item rdf:about="http://olasearch.com/articles/what-is-search-experience">
    <title>What is search experience? - Ola Search</title>
    <dc:date>2017-01-24T14:17:34+00:00</dc:date>
    <link>http://olasearch.com/articles/what-is-search-experience</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[An experience is what a person goes through and remembers. A search experience, therefore, is a person's feelings and memories of using search. People embrace pleasant experiences and reject awful ones. Experiences, however, are hard to design. They depend on many factors. The first step is to uncover and unpack them.]]></description>
<dc:subject>search design inls201</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:rybesh/b:12c839912d3a/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:search"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:design"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:inls201"/>
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</item>
<item rdf:about="https://commoncrawl.org/">
    <title>Common Crawl</title>
    <dc:date>2016-02-18T01:01:29+00:00</dc:date>
    <link>https://commoncrawl.org/</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[We build and maintain an open repository of web crawl data that can be accessed and analyzed by anyone.]]></description>
<dc:subject>web data search</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:rybesh/b:3352316c6e59/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:web"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:data"/>
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</item>
<item rdf:about="http://www.gryffin.com/guide-to-google-search-operators">
    <title>Comprehensive Guide to Using Google Search Operators</title>
    <dc:date>2016-01-24T22:44:54+00:00</dc:date>
    <link>http://www.gryffin.com/guide-to-google-search-operators</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[Now that we’ve seen an example of search operators in action and we know the mindset we must have to approach Google searching (start with the end in mind). Let’s take a more specific look at what search operators do.

Essentially, search operators sculpt your results to satisfy your query. You are giving Google more information about what you want, or don’t want in your return results.]]></description>
<dc:subject>search interface inls201</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:rybesh/b:fa1814b77b1b/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:search"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:interface"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:inls201"/>
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</item>
<item rdf:about="http://www.google.com/insidesearch/howsearchworks/thestory/">
    <title>How Search Works - The Story – Inside Search – Google</title>
    <dc:date>2015-12-09T15:43:12+00:00</dc:date>
    <link>http://www.google.com/insidesearch/howsearchworks/thestory/</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[A silent interactive Javascript cartoon Google released depicting its search algorithm in 2013, entitled “How Search Works: From Algorithms to Answers” (an excerpt is in Figure 8). The cartoon’s general structure is adapted from a 2010 Google video, also entitled How Search Works, which built upon the previously popularized algorithm “PageRank.” In the 2013 cartoon, algorithms are defined as “programs and formulas to deliver the best results possible.” The cartoon depicts the search algorithm as a vertical assembly line. At the top of the frame, the crawler algorithm’s progress across the Web is represented by a moving yellow line, which bounces from document to document toward the bottom of the page. It eventually stops at a massive blue filing cabinet identified as “the index,” containing drawers filled with cat videos and physics papers. In the next scene, the user’s action of typing a keyword – or perhaps an unspecified action, this is not clear – is embodied by the yellow line, which wraps around six circular algorithms that “get to work” on the query. They smash it with a small press, sparkle near it, and rotate through sequences of shapes separated by the “equal” or “not equal” symbol, suggesting both mathematical formulae and slot machines. http://median.newmediacaucus.org/art-infrastructures-information/seeing-the-sort-the-aesthetic-and-industrial-defense-of-the-algorithm/]]></description>
<dc:subject>google search algorithms visualization marketing datastudies inls201 teaching</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:rybesh/b:c6ab603d6753/</dc:identifier>
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	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:marketing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:datastudies"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:inls201"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:teaching"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://infovis.fh-potsdam.de/ddb/">
    <title>Deutsche Digitale Bibliothek visualisiert</title>
    <dc:date>2015-07-14T23:29:12+00:00</dc:date>
    <link>http://infovis.fh-potsdam.de/ddb/</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[The German Digital Library makes a variety of objects of the digital cultural heritage of Germany's cultural and research institutions accessible. This project attempts to make the size of this portfolio visible and tangible by means of interactive visualizations. The resulting views are experimental reports on the rough temporal and spatial distribution of objects and their associated topics, people and organizations.]]></description>
<dc:subject>library search interface visualization</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:rybesh/b:2ae8072ede3c/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:library"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:search"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:interface"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:visualization"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://mariandoerk.de/fluidviews/">
    <title>Marian Dörk – Fluid Views</title>
    <dc:date>2015-07-14T23:28:01+00:00</dc:date>
    <link>http://mariandoerk.de/fluidviews/</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[Fluid Views is a web-based search environment designed to bridge overview and detail by integrating dynamic queries, semantic zooming, and dual layers. The most common form of search results is long ranked and paginated lists, which are seldom examined beyond the top ten items. To support more exploratory forms of information seeking, we bring together the notion of relevance with the power of visual encoding. In Fluid Views, results portray relevance via size and detail in a dynamic top layer and semantic similarity via position on a base map. We designed Fluid Views with temporal, spatial, and content-defined base maps for both textual and visual resources, and tested our prototype system on books, blogs, and photos.]]></description>
<dc:subject>search interface visualization</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:rybesh/b:c3fb62b73d51/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:search"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:interface"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:visualization"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://stanford.library.usyd.edu.au/entries/ethics-search/">
    <title>Search Engines and Ethics (Stanford Encyclopedia of Philosophy)</title>
    <dc:date>2014-12-02T16:12:01+00:00</dc:date>
    <link>http://stanford.library.usyd.edu.au/entries/ethics-search/</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[What is an Internet search engine? Why are search engines problematic from an ethical perspective? ]]></description>
<dc:subject>IR search ethics bias inls201</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:rybesh/b:16a3bbcc9b77/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:IR"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:search"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:ethics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:bias"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:inls201"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://github.com/larsga/Duke">
    <title>larsga/Duke</title>
    <dc:date>2014-09-09T02:40:22+00:00</dc:date>
    <link>https://github.com/larsga/Duke</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[Duke is a fast and flexible deduplication (or entity resolution, or record linkage) engine written in Java on top of Lucene. The latest version is 1.2 (see ReleaseNotes).

Duke can find duplicate customer records, or other kinds of records in your database. Or you can use it to connect records in one data set with other records representing the same thing in another data set. Duke has sophisticated comparators that can handle spelling differences, numbers, geopositions, and more. Using a probabilistic model Duke can handle noisy data with good accuracy.]]></description>
<dc:subject>lucene reconciliation search cleaning</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:rybesh/b:b022cef10de5/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:lucene"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:reconciliation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:search"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:cleaning"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://github.com/dfahlander/Dexie.js/wiki/Dexie.js">
    <title>Dexie.js · dfahlander/Dexie.js Wiki</title>
    <dc:date>2014-06-19T17:16:51+00:00</dc:date>
    <link>https://github.com/dfahlander/Dexie.js/wiki/Dexie.js</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[minimalistic and bullet proof indexedDB library.]]></description>
<dc:subject>indexeddb search browser storage javascript</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:rybesh/b:f38b08f21ed9/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:indexeddb"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:search"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:browser"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:storage"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:javascript"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://hacks.mozilla.org/2014/06/breaking-the-borders-of-indexeddb/">
    <title>Breaking the Borders of IndexedDB ✩ Mozilla Hacks – the Web developer blog</title>
    <dc:date>2014-06-19T17:15:35+00:00</dc:date>
    <link>https://hacks.mozilla.org/2014/06/breaking-the-borders-of-indexeddb/</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[Some cool IndexedDB queries that aren’t ‘possible’ out of the box unless you add some ‘tricks’.]]></description>
<dc:subject>indexeddb search browser storage javascript</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:rybesh/b:6f8e555255fb/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:indexeddb"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:search"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:browser"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:storage"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:javascript"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://nytimes.github.io/pourover/">
    <title>The PourOver Book · PourOver</title>
    <dc:date>2014-04-17T20:15:06+00:00</dc:date>
    <link>http://nytimes.github.io/pourover/</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[PourOver is a library for fast filtering and sorting of large collections—think 100,000s of items—in the browser.]]></description>
<dc:subject>javascript search browsing</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:rybesh/b:bc84419d3388/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:javascript"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:search"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:browsing"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://github.com/javasoze/clue">
    <title>javasoze/clue</title>
    <dc:date>2013-11-18T12:37:33+00:00</dc:date>
    <link>https://github.com/javasoze/clue</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[When working with Lucene, it is often useful to inspect an index.

Luke is awesome, but often times it is not feasible to inspect an index on a remote machine using a GUI. That's where Clue comes in. You can ssh into your production box and inspect your index using your favorite shell.

Another important feature for Clue is the ability to interact with other Unix commands via piping, e.g. grep, more etc.]]></description>
<dc:subject>lucene elasticsearch search debugging tools</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:rybesh/b:24dd31a2c936/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:lucene"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:elasticsearch"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:search"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:debugging"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:tools"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://luxdb.org/">
    <title>Lux: The XML Search Engine</title>
    <dc:date>2013-11-14T16:55:57+00:00</dc:date>
    <link>http://luxdb.org/</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[Lux is an open source XML search engine formed by fusing two excellent technologies: the Apache Lucene/Solr search index and the Saxon XQuery/XSLT processor.]]></description>
<dc:subject>xml search tools</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:rybesh/b:217181998c01/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:xml"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:search"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:tools"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://github.com/fergiemcdowall/search-index">
    <title>fergiemcdowall/search-index</title>
    <dc:date>2013-07-07T22:29:51+00:00</dc:date>
    <link>https://github.com/fergiemcdowall/search-index</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[Search-index is a search index module for Node.js. Think "node version of Lucene, but much simpler".

Search-index is built with the soooperfast levelUP module, and the very useful Natural module.

The Point of Search-Index is to simplify set up and operation of an search engine. Search-index is essentially free from configuration- the index is dynamic and morphs into the structure that you require automatically, based on the documents that it is fed.

Search-index is in an alpha stage- meaning that it has been known to work quite well, but edge cases and portability may be challenging. Query-result is robust and sometimes indexing requires hand-holding. ]]></description>
<dc:subject>nodejs search indexing</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:rybesh/b:9f8903ce6963/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:nodejs"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:search"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:indexing"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://dl.acm.org/citation.cfm?id=2232859">
    <title>Event-centric search and exploration in document collections</title>
    <dc:date>2013-06-03T19:18:43+00:00</dc:date>
    <link>http://dl.acm.org/citation.cfm?id=2232859</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[Textual data ranging from corpora of digitized historic documents to large collections of news feeds provide a rich source for temporal and geographic information. Such types of information have recently gained a lot of interest in support of different search and exploration tasks, e.g., by organizing news along a timeline or placing the origin of documents on a map. However, for this, temporal and geographic information embedded in documents is often considered in isolation. We claim that through combining such information into (chronologically ordered) event-like features interesting and meaningful search and exploration tasks are possible. In this paper, we present a framework for the extraction, exploration, and visualization of event information in document collections. For this, one has to identify and combine temporal and geographic expressions from documents, thus enriching a document collection by a set of normalized events. Traditional search queries then can be enriched by conditions on the events relevant to the search subject. Most important for our event-centric approach is that a search result consists of a sequence of events relevant to the search terms and not just a document hit-list. Such events can originate from different documents and can be further explored, in particular events relevant to a search query can be ordered chronologically. We demonstrate the utility of our framework by different (multilingual) search and exploration scenarios using a Wikipedia corpus.]]></description>
<dc:subject>historical research search events contours</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:rybesh/b:7f965df23b8f/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:historical"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:research"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:search"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:events"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:contours"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1207.2615">
    <title>[1207.2615] Broccoli: Semantic Full-Text Search at your Fingertips</title>
    <dc:date>2013-04-22T16:56:20+00:00</dc:date>
    <link>http://arxiv.org/abs/1207.2615</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[We present Broccoli, a fast and easy-to-use search engine for what we call semantic full-text search. Semantic full-text search combines the capabilities of standard full-text search and ontology search. The search operates on four kinds of objects: ordinary words (e.g., edible), classes (e.g., plants), instances (e.g., Broccoli), and relations (e.g., occurs-with or native-to). Queries are trees, where nodes are arbitrary bags of these objects, and arcs are relations. The user interface guides the user in incrementally constructing such trees by instant (search-as-you-type) suggestions of words, classes, instances, or relations that lead to good hits. Both standard full-text search and pure ontology search are included as special cases. In this paper, we describe the query language of Broccoli, a new kind of index that enables fast processing of queries from that language as well as fast query suggestion, the natural language processing required, and the user interface. We evaluated query times and result quality on the full version of the EnglishWikipedia (32 GB XML dump) combined with the YAGO ontology (26 million facts).]]></description>
<dc:subject>semweb graph search linkeddata interface</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:rybesh/b:ac7a9f687da9/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:semweb"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:graph"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:search"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:linkeddata"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:interface"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://lunrjs.com/">
    <title>lunr.js - A bit like Solr, but much smaller and not as bright</title>
    <dc:date>2013-03-04T21:17:16+00:00</dc:date>
    <link>http://lunrjs.com/</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[lunr.js is a simple full text search engine for your client side applications. It is designed to be small, yet full featured, enabling you to provide a great search experience without the need for external, server side, search services.]]></description>
<dc:subject>javascript search</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:rybesh/b:a605ad8a873f/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:javascript"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:search"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://github.com/linkedin/cleo">
    <title>linkedin/cleo · GitHub</title>
    <dc:date>2013-02-20T15:40:16+00:00</dc:date>
    <link>https://github.com/linkedin/cleo</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[Cleo is a flexible software library for enabling rapid development of partial, out-of-order and real-time typeahead search.]]></description>
<dc:subject>javascript search interface</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:rybesh/b:9ce67805c16f/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:javascript"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:search"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:interface"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://github.com/twitter/typeahead.js">
    <title>twitter/typeahead.js · GitHub</title>
    <dc:date>2013-02-20T15:39:47+00:00</dc:date>
    <link>https://github.com/twitter/typeahead.js</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[typeahead.js is a fast and fully-featured autocomplete library.]]></description>
<dc:subject>javascript search interface</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:rybesh/b:80eb73f4fcc6/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:javascript"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:search"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:interface"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://searchengineland.com/facebook-search-not-google-search-145124">
    <title>How The New Facebook Search Is Different &amp; Unique From Google Search</title>
    <dc:date>2013-01-16T17:04:21+00:00</dc:date>
    <link>http://searchengineland.com/facebook-search-not-google-search-145124</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[With a typical Google search, the objects we search for are web pages, with the connections (or graph) that help determine the pages that rise to the top primarily being links from across the web. Links, simple form, are like votes, helping Google decide which are the most popular pages to show for a particular topic.

With Facebook Graph Search, the objects we search for aren’t web pages but instead virtual representations of real world objects: people, places and things. The connections are primarily Facebook Likes. Did such-and-such a person like a particular photo? A particular doctor? A particular restaurant? Those likes are the ties that bind the information in Facebook together.]]></description>
<dc:subject>graph search inls520 inls201</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:rybesh/b:9c683399f73f/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:graph"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:search"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:inls520"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:inls201"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://snowball.tartarus.org/">
    <title>Snowball</title>
    <dc:date>2013-01-08T14:02:32+00:00</dc:date>
    <link>http://snowball.tartarus.org/</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[Snowball is a small string processing language designed for creating stemming algorithms for use in Information Retrieval.]]></description>
<dc:subject>nlp search algorithms</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:rybesh/b:a9bbef8cb92a/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:nlp"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:search"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:algorithms"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.hutime.org/">
    <title>HuTime - Time Information System</title>
    <dc:date>2012-12-09T22:41:33+00:00</dc:date>
    <link>http://www.hutime.org/</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[This website distributes software, data, and documents designed to help users visualize and analyze various types of temporal information (time data and other information related to time). The following studies are based on the research results of projects promoted by the Humanities GIS Research Group.]]></description>
<dc:subject>temporal time timeline search visualization digitalhumanities</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:rybesh/b:b7581e775de2/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:temporal"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:time"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:timeline"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:search"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:visualization"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:digitalhumanities"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://blog.orange11.nl/2012/09/25/whats-so-cool-about-elasticsearch/">
    <title>What’s so cool about elasticsearch? « Orange11 Blog / Orange11: Enterprise Java, Open Source, software solutions, Amsterdam</title>
    <dc:date>2012-10-05T23:52:36+00:00</dc:date>
    <link>http://blog.orange11.nl/2012/09/25/whats-so-cool-about-elasticsearch/</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[You usually end up needing to search on different fields with different weights, by applying some filters or conditions to boost document rating based on the value of some pre-defined fields, facebook likes, recent documents, facets,highlighting and so on. All of this can be done through a single query and there is a need to express this complexity: the elasticsearch query DSL is the answer.]]></description>
<dc:subject>elasticsearch search IR inls520 faceted facets analysis classification</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:rybesh/b:f02522710f57/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:elasticsearch"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:search"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:IR"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:inls520"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:faceted"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:facets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:analysis"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:classification"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://lingpipe-blog.com/2012/07/24/using-luke-the-lucene-index-browser-to-develop-search-queries/">
    <title>Using Luke the Lucene Index Browser to develop Search Queries « LingPipe Blog</title>
    <dc:date>2012-07-25T00:23:18+00:00</dc:date>
    <link>http://lingpipe-blog.com/2012/07/24/using-luke-the-lucene-index-browser-to-develop-search-queries/</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[Luke is a GUI tool written in Java that allows you to browse the contents of a Lucene index, examine individual documents, and run queries over the index. Whether you’re developing with PyLucene, Lucene.NET, or Lucene Core, Luke is your friend.]]></description>
<dc:subject>lucene search tools</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:rybesh/b:3cf0438966c7/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:lucene"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:search"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:tools"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://okfnlabs.org/facetview/">
    <title>FacetView</title>
    <dc:date>2012-06-20T18:25:08+00:00</dc:date>
    <link>http://okfnlabs.org/facetview/</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[FacetView is a pure javascript frontend for ElasticSearch or SOLR search indices.

It lets you easily embed a faceted browser and search front end into any web page. It also provides a micro-framework you can build on when creating user interfaces to SOLR and ElasticSearch.]]></description>
<dc:subject>jquery faceted search elasticsearch</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:rybesh/b:9de8f4a172de/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:jquery"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:faceted"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:search"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:elasticsearch"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.oclc.org/research/publications/library/2009/2009-06.pdf">
    <title>The Metadata is the Interface: Better Description for Better Discovery of Archives and Special Collections, Synthesized from User Studies</title>
    <dc:date>2012-05-17T20:42:52+00:00</dc:date>
    <link>http://www.oclc.org/research/publications/library/2009/2009-06.pdf</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[This essay—part  of  a  series  of  OCLC  Research  projects  to  mobilize  unique  materials synthesizes evidence of what descriptive information people say they need for research.]]></description>
<dc:subject>userresearch metadata interface search specialcollections archives</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:rybesh/b:894eb86083b6/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:userresearch"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:metadata"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:interface"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:search"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:specialcollections"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:archives"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.jstor.org/stable/10.1086/663350">
    <title>JSTOR: The Journal of Modern History, Vol. 84, No. 1 (March 2012), pp. 116-144</title>
    <dc:date>2012-05-11T20:34:03+00:00</dc:date>
    <link>http://www.jstor.org/stable/10.1086/663350</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[ by using multiple databases and keyword variants, the historian may gain confidence in a particular chronological intervention. Large databases, the result of scanned microfilm collections or mass digitization initiatives across multiple libraries, provide enough texts to bridge generation and genre, incorporating authors from a variety of backgrounds. Sheer number of texts is important here: ECCO indexes 200,000 works from eighteenth- and nineteenth-century Britain with 33 million pages of text; Google Books Search has 42 million books from all periods. If the historian’s goal is to show a shift in common word usage, the size of a database is more important than its genre specificity; in the case examined in the present article, for instance, Google Book Search and ECCO were superior to the available poetry databases. Iterative visitation of multiple databases provided another potential source of richness for extracting meaning from these tools.]]></description>
<dc:subject>textanalysis search digitalhumanities</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:rybesh/b:23f9249eb078/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:textanalysis"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:search"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:digitalhumanities"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.fancyhands.com/">
    <title>Personal Assistants for Everyone - Fancy Hands</title>
    <dc:date>2012-02-27T16:10:19+00:00</dc:date>
    <link>http://www.fancyhands.com/</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[Fancy Hands is a team of personal assistants ready to work for you right now. You should focus on what's important, let us focus on the rest.]]></description>
<dc:subject>search research IR</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:rybesh/b:c8c9c763878e/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:search"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:research"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:IR"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://github.com/mattweber/elasticsearch-mocksolrplugin">
    <title>mattweber/elasticsearch-mocksolrplugin - GitHub</title>
    <dc:date>2011-12-13T13:45:44+00:00</dc:date>
    <link>https://github.com/mattweber/elasticsearch-mocksolrplugin</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[This plugin will allow you to use tools that were built to interact with Solr with ElasticSearch.]]></description>
<dc:subject>solr search tools</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:rybesh/b:e7b48df782f7/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:solr"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:search"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:tools"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.people.fas.harvard.edu/~sstephen/papers/RacialAnimusAndVotingSethStephensDavidowitz.pdf">
    <title>The Eﬀects of Racial Animus on Voting: Evidence Using Google Search Data</title>
    <dc:date>2011-11-26T22:53:45+00:00</dc:date>
    <link>http://www.people.fas.harvard.edu/~sstephen/papers/RacialAnimusAndVotingSethStephensDavidowitz.pdf</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[Traditional surveys struggle to capture socially unacceptable attitudes such as racial
animus. This paper uses Google searches including racially charged language as a proxy
for a local area’s racial animus. I use the Google-search proxy, available for roughly
200 media markets in the United States, to reassess the impact of racial attitudes on
voting for a black candidate in the United States. I compare an area’s racially charged
search volume to its votes for Barack Obama, the 2008 black Democratic presidential
candidate, controlling for its votes for John Kerry, the 2004 white Democratic presidential candidate. Other studies using a similar empirical speciﬁcation and standard
state-level survey measures of racial attitudes yield little evidence that racial animus
had a major impact in recent U.S. elections. Using the Google-search proxy, I ﬁnd
signiﬁcant and robust eﬀects in the 2008 presidential election. The estimates imply
that racial animus in the United States cost Obama three to ﬁve percentage points in
the national popular vote in the 2008 election.]]></description>
<dc:subject>statistics socialscience methods search</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:rybesh/b:5519f14cffd2/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:socialscience"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:methods"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:search"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://documentcloud.github.com/visualsearch/">
    <title>DocumentCloud's VisualSearch.js</title>
    <dc:date>2011-11-09T18:18:32+00:00</dc:date>
    <link>http://documentcloud.github.com/visualsearch/</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[VisualSearch.js enhances ordinary search boxes with the ability to autocomplete faceted search queries. Specify the facets for completion, along with the completable values for any facet. You can retrieve the search query as a structured object, so you don't have to parse the query string yourself.]]></description>
<dc:subject>faceted search javascript</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:rybesh/b:7609ea1a8234/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:faceted"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:search"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:javascript"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.w3.org/2001/sw/sweo/public/UseCases/Volkswagen/">
    <title>Case Study: Contextual Search for Volkswagen and the Automotive Industry</title>
    <dc:date>2011-10-13T15:37:59+00:00</dc:date>
    <link>http://www.w3.org/2001/sw/sweo/public/UseCases/Volkswagen/</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[In summary the key benefits of using Semantic Web technology for Volkswagen were as follows:

A standardised interface to data and content, accessible to developers with different skillsets, using different technologies within and without the organisation.
Separation of concerns between information and application, both logically and physically.
Increases value, reusability and accessibility of data.
Very powerful federation features.
Adoption and use didn't necessitate process or change management. It could be leveraged at any stage within the product lifecycle painlessly and gracefully, both internally and externally.]]></description>
<dc:subject>semweb linkeddata search inls520 metadata</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:rybesh/b:cc8d694c7d75/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:semweb"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:linkeddata"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:search"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:inls520"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:metadata"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://sappingattention.blogspot.com/2011/09/bookworm-and-library-search.html">
    <title>Sapping Attention: Bookworm and library search</title>
    <dc:date>2011-09-30T15:16:35+00:00</dc:date>
    <link>http://sappingattention.blogspot.com/2011/09/bookworm-and-library-search.html</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[4) Organize the library according to your personal principles, and browse it from arbitrary points.

This is where we need to go. Bookworm presents one set of ways for reordering the library based on the principle that language is constrained by the fields of its utterance--geographical (publication place), disciplinary (LC classification), temporal (publication year), even autobiographical (author age). The line chart that a search creates is a representation of overall trends; but it is also, taken point by point, an enormous collection of books. If you search for a term by author age and publication place, Bookworm is reordering the collection of the Open Library (a lot of it, anyway) into chunks divided by author age and place, showing you information about each one of those chunks, and inviting you to dive into a particular one to find the books matching your term.]]></description>
<dc:subject>search organization inls520</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:rybesh/b:2f1eab842af5/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:search"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:organization"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:inls520"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://blekko.com/webgrep?status=completed">
    <title>Grep the Web</title>
    <dc:date>2011-09-26T16:13:46+00:00</dc:date>
    <link>http://blekko.com/webgrep?status=completed</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[Submit a series of strings or patterns and we will show you the urls on which they appear (in rank order).]]></description>
<dc:subject>search tools</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:rybesh/b:6461d884ca80/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:search"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:tools"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://github.com/rgrove/node-elastical/">
    <title>rgrove/node-elastical - GitHub</title>
    <dc:date>2011-09-06T23:38:02+00:00</dc:date>
    <link>https://github.com/rgrove/node-elastical/</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[Node.js client for the ElasticSearch REST API.]]></description>
<dc:subject>nodejs elasticsearch search</dc:subject>
<dc:source>https://twitter.com/</dc:source>
<dc:identifier>https://pinboard.in/u:rybesh/b:cb9f8b678d6b/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:nodejs"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:elasticsearch"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:search"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.elasticsearch.org/tutorials/2010/08/01/couchb-integration.html">
    <title>elasticsearch - tutorials - CouchDB Integration</title>
    <dc:date>2011-08-15T17:23:32+00:00</dc:date>
    <link>http://www.elasticsearch.org/tutorials/2010/08/01/couchb-integration.html</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[This tutorial explains the process of setting up ElasticSearch to automatically index data
in CouchDB and make it search-able.]]></description>
<dc:subject>couchdb elasticsearch search</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:rybesh/b:c5a2936d1992/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:couchdb"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:elasticsearch"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:search"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://eirikb.github.com/nipster/">
    <title>Nipster!</title>
    <dc:date>2011-08-10T21:51:41+00:00</dc:date>
    <link>http://eirikb.github.com/nipster/</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[npm registry search using github stats for ranking.]]></description>
<dc:subject>nodejs npm search</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:rybesh/b:58c634328c45/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:nodejs"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:npm"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:search"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.elasticsearch.org/guide/reference/mapping/attachment-type.html">
    <title>elasticsearch - guide - Attachment Type</title>
    <dc:date>2011-08-04T23:45:55+00:00</dc:date>
    <link>http://www.elasticsearch.org/guide/reference/mapping/attachment-type.html</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[he attachment type allows to index different “attachment” type field (encoded as base64), for example, microsoft office formats, open document formats, ePub, HTML, and so on (full list can be found here).]]></description>
<dc:subject>elasticsearch search reference pdf</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:rybesh/b:4d2549b27275/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:elasticsearch"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:search"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:reference"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:pdf"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.elasticsearch.org/guide/reference/api/search/facets/">
    <title>elasticsearch - guide - Search API - Facets</title>
    <dc:date>2011-07-20T14:49:25+00:00</dc:date>
    <link>http://www.elasticsearch.org/guide/reference/api/search/facets/</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[Facets provide aggregated data based on a search query. In the simple case, a facet can return facet counts for various facet values for a specific field. ElasticSearch supports more advanced facet implementations, such as statistical or date histogram facets.]]></description>
<dc:subject>faceted search api howto facets analysis elasticsearch inls520 IR</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:rybesh/b:e6f705102128/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:faceted"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:search"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:api"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:howto"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:facets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:analysis"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:elasticsearch"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:inls520"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:IR"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.elasticsearch.org/tutorials/2011/07/18/attachment-type-in-action.html">
    <title>elasticsearch - tutorials - Attachment Type in Action</title>
    <dc:date>2011-07-18T20:54:13+00:00</dc:date>
    <link>http://www.elasticsearch.org/tutorials/2011/07/18/attachment-type-in-action.html</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[This tutorial will walk you through basic attachment type setup and use in search including highighting. (How to use elasticsearch to index PDFs and other file types.)]]></description>
<dc:subject>indexing search howto pdf</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:rybesh/b:2cc7e577a91f/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:indexing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:search"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:howto"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:pdf"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://tika.apache.org/">
    <title>Apache Tika - Apache Tika</title>
    <dc:date>2011-07-18T20:49:29+00:00</dc:date>
    <link>http://tika.apache.org/</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[The Apache Tika™ toolkit detects and extracts metadata and structured text content from various documents using existing parser libraries.]]></description>
<dc:subject>lucene metadata search pdf</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:rybesh/b:4b3f5ce13dd2/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:lucene"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:metadata"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:search"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:pdf"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://lingpipe-blog.com/2011/05/27/price-is-right-binary-search-suffix-array-document/">
    <title>Price-is-Right Binary Search (for Suffix Arrays of Documents) « LingPipe Blog</title>
    <dc:date>2011-06-01T15:53:12+00:00</dc:date>
    <link>http://lingpipe-blog.com/2011/05/27/price-is-right-binary-search-suffix-array-document/</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[Suffix arrays are useful if you’re looking for anything from plagiarized passages in a pile of writing assignments, cut-and-paste code blocks in a large project, or just commonly repeated phrases on Twitter.]]></description>
<dc:subject>search textanalysis textmining</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:rybesh/b:0c2e4071747c/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:search"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:textanalysis"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:textmining"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://pypi.python.org/pypi/python-Levenshtein/">
    <title>Python Package Index : python-Levenshtein 0.10.2</title>
    <dc:date>2011-05-17T04:03:40+00:00</dc:date>
    <link>http://pypi.python.org/pypi/python-Levenshtein/</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[Python extension computing string distances and similarities.

]]></description>
<dc:subject>python textanalysis search</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:rybesh/b:13682adb90b1/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:python"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:textanalysis"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:search"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.fxpal.com/?p=RevertedIndexing">
    <title>Reverted Indexing</title>
    <dc:date>2011-03-29T13:46:43+00:00</dc:date>
    <link>http://www.fxpal.com/?p=RevertedIndexing</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[Traditional interactive information retrieval systems function by creating inverted lists, or term indexes. For every term in the vocabulary, a list is created that contains the documents in which that term occurs and its frequency within each document. Retrieval algorithms then use these term frequencies alongside other collection statistics to identify matching documents for a query.

Term-based search, however, is just one example of interactive information seeking. Other examples include offering suggestions of documents similar to ones already found, or identifying effective query expansion terms that the user might wish to use. More generally, these fall into several categories: query term suggestion, relevance feedback, and pseudo-relevance feedback.

We can combine the inverted index with the notion of retrievability to create an efficient query expansion algorithm that is useful for a number of applications, such as query expansion and relevance (and pseudo-relevance) feedback. We call this kind of index a reverted index because rather than mapping terms onto documents, it maps document ids onto queries that retrieved the associated documents.]]></description>
<dc:subject>IR tools search lucene</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:rybesh/b:6f83b764587c/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:IR"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:tools"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:search"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:lucene"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://livingknowledge-project.eu/">
    <title>Living Knowledge : Home</title>
    <dc:date>2011-03-18T21:41:46+00:00</dc:date>
    <link>http://livingknowledge-project.eu/</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[Knowledge and its articulations are strongly influenced by diversity in, e.g., cultural backgrounds, schools of thought, geographical contexts. Judgements, assessments and opinions, which play a crucial role in many areas of democratic societies, including politics and economics, reflect this diversity in perspective and goals. For the information on the Web (including, e.g., news and blogs) diversity - implied by the ever increasing multitude of information providers - is the reason for diverging viewpoints and conflicts. Time and evolution add a further dimension making diversity an intrinsic and unavoidable property of knowledge.]]></description>
<dc:subject>news search research time knowledge europe</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:rybesh/b:5db54ea3f801/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:news"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:search"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:research"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:time"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:knowledge"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:europe"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://fbmya01.barcelonamedia.org:8080/future/">
    <title>Time Explorer</title>
    <dc:date>2011-03-18T21:40:51+00:00</dc:date>
    <link>http://fbmya01.barcelonamedia.org:8080/future/</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[Welcome to the Time Explorer, an application designed for analyzing how news changes over time. Time Explorer extends upon current time-based systems in many important ways. First, Time Explorer is designed to help users discover how entities such as people and locations associated with a query change over time. Second, by searching on time expressions extracted automatically from text, the application allows the user to explore not only how topics evolved the past, but also how they will continue to evolve in the future.]]></description>
<dc:subject>time history news search interface</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:rybesh/b:7fb6645c108c/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:time"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:history"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:news"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:search"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:interface"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.elasticsearch.org/">
    <title>elasticsearch - - Open Source, Distributed, RESTful, Search Engine</title>
    <dc:date>2011-02-08T15:20:33+00:00</dc:date>
    <link>http://www.elasticsearch.org/</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[It is an Open Source (Apache 2), Distributed, RESTful, Search Engine built on top of Lucene.]]></description>
<dc:subject>search ir tools rest java json</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:rybesh/b:0dc9ec797630/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:search"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:ir"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:tools"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:rest"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:java"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:json"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://googlewebmastercentral.blogspot.com/2009/10/proposal-for-making-ajax-crawlable.html">
    <title>Official Google Webmaster Central Blog: A proposal for making AJAX crawlable</title>
    <dc:date>2011-01-01T19:15:29+00:00</dc:date>
    <link>http://googlewebmastercentral.blogspot.com/2009/10/proposal-for-making-ajax-crawlable.html</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[Proposed standards for making AJAX-based websites crawlable.]]></description>
<dc:subject>google ajax javascript search</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:rybesh/b:e04e51f56c40/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:google"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:ajax"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:javascript"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:search"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://factforge.net/">
    <title>FactForge.net</title>
    <dc:date>2010-10-25T15:06:11+00:00</dc:date>
    <link>http://factforge.net/</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[FactForge represents a reason-able view to the web of data. It aims to allow users to find resources and facts based on the semantics of the data, like web search engines index WWW pages and facilitate their usage.]]></description>
<dc:subject>semweb facts search</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:rybesh/b:4e646c1fbdee/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:semweb"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:facts"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:search"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://libraryjuicepress.com/blog/?p=2404">
    <title>Library Juice » A Google trick for staying ahead of AI</title>
    <dc:date>2010-09-19T18:55:45+00:00</dc:date>
    <link>http://libraryjuicepress.com/blog/?p=2404</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[Increasing use of AI means smarter-than-average searchers constantly need to learn tricks in order to counteract the AI that assumes a user base of average consumers.]]></description>
<dc:subject>search interface Information_Ethics Technology</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:rybesh/b:b081b05497a6/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:search"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:interface"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:Information_Ethics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:Technology"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://metaoptimize.com/qa">
    <title>Training Examples Q&amp;A - machine learning, natural language processing, artificial intelligence, text analysis, information retrieval, search, data mining, statistical modeling, and data visualization</title>
    <dc:date>2010-06-30T00:53:11+00:00</dc:date>
    <link>http://metaoptimize.com/qa</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[Where data geeks ask and answer questions on machine learning, natural language processing, artificial intelligence, text analysis, information retrieval, search, data mining, statistical modeling, and data visualization!]]></description>
<dc:subject>ai machinelearning nlp textanalysis ir datamining search statistics infoviz reference</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:rybesh/b:8360af74d3ea/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:ai"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:machinelearning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:nlp"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:textanalysis"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:ir"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:datamining"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:search"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:infoviz"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:reference"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://turnguard.com/tuqs/NONE/about">
    <title>TuQS</title>
    <dc:date>2010-03-22T16:27:51+00:00</dc:date>
    <link>http://turnguard.com/tuqs/NONE/about</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[Turnguard's QuadStore is the first draft of an own implementation of a QuadStore with main focus on data-retrieval speed. Implements full-text search.]]></description>
<dc:subject>triplestore search database semweb tools</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:rybesh/b:acce3a5e649d/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:triplestore"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:search"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:database"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:semweb"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:tools"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://weblogs.swarthmore.edu/burke/2009/10/15/digital-search-ii-a-user-perspective-on-database-design/">
    <title>Digital Search II: A User Perspective on Database Design « Easily Distracted</title>
    <dc:date>2009-12-01T19:06:45+00:00</dc:date>
    <link>http://weblogs.swarthmore.edu/burke/2009/10/15/digital-search-ii-a-user-perspective-on-database-design/</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA["Rather than moving towards amalgamation and interoperability across databases, you really get the sense that everybody’s been busy grabbing at whatever piles of text they can lay their hands on, building the biggest little mudhill they can manage to put up, and then building walls around it. There are interstitial services that help a user 'jump' from one little fragmented collection to another and portals that aim to be a 'top level' to return to, sure, but we should be doing better by now."
]]></description>
<dc:subject>search database interface scholarship library context contextfinder usability</dc:subject>
<dc:identifier>https://pinboard.in/u:rybesh/b:6a6df57bd3e9/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:search"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:database"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:interface"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:scholarship"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:library"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:context"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:contextfinder"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:usability"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://weblogs.swarthmore.edu/burke/2009/11/30/anatomy-of-a-search/">
    <title>Anatomy of a Search « Easily Distracted</title>
    <dc:date>2009-12-01T18:07:15+00:00</dc:date>
    <link>http://weblogs.swarthmore.edu/burke/2009/11/30/anatomy-of-a-search/</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA["Over Thanksgiving weekend, I had a great search experience that I think is worth laying out here, because it captures three of the key dimensions of digital search."
]]></description>
<dc:subject>search strategy history scholarship</dc:subject>
<dc:identifier>https://pinboard.in/u:rybesh/b:593159a81270/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:search"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:strategy"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:history"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:scholarship"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.google.com/support/webmasters/bin/topic.py?topic=21997">
    <title>Structured data (rich snippets) - Webmasters/Site owners Help</title>
    <dc:date>2009-05-13T16:27:07+00:00</dc:date>
    <link>http://www.google.com/support/webmasters/bin/topic.py?topic=21997</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[What Google does with embedded metadata.
]]></description>
<dc:subject>google microformats rdfa markup search semweb metadata</dc:subject>
<dc:identifier>https://pinboard.in/u:rybesh/b:f211e6f4e499/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:google"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:microformats"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:rdfa"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:markup"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:search"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:semweb"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:metadata"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://fgiasson.com/blog/index.php/2009/04/29/rdf-aggregates-and-full-text-search-on-steroids-with-solr/">
    <title>RDF Aggregates and Full Text Search</title>
    <dc:date>2009-05-04T16:21:58+00:00</dc:date>
    <link>http://fgiasson.com/blog/index.php/2009/04/29/rdf-aggregates-and-full-text-search-on-steroids-with-solr/</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[Perform full text searches, filtered by types that are inferred.
]]></description>
<dc:subject>rdf database search semweb tools howto</dc:subject>
<dc:identifier>https://pinboard.in/u:rybesh/b:a2aa683b9d8c/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:rdf"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:database"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:search"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:semweb"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:tools"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:howto"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://haystacksearch.org/">
    <title>Haystack - Search for Django</title>
    <dc:date>2009-04-17T21:59:31+00:00</dc:date>
    <link>http://haystacksearch.org/</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[Haystack provides modular search for Django. It features a unified, familiar API that allows you to plug in different search backends (such as Solr, Whoosh, etc.) without having to modify your code.
]]></description>
<dc:subject>django python search framework searchengine tools</dc:subject>
<dc:identifier>https://pinboard.in/u:rybesh/b:16ca9fb14153/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:django"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:python"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:search"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:framework"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:searchengine"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:tools"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://irgupf.com/2009/04/09/retrievability/">
    <title>Information Retrieval Gupf » Retrievability</title>
    <dc:date>2009-04-09T17:17:22+00:00</dc:date>
    <link>http://irgupf.com/2009/04/09/retrievability/</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[Popularity bias (”the inherent democratic nature of the web”) might actually prevent more information from ever being seen, because it never appears at the top of anyone’s query list.
]]></description>
<dc:subject>IR critique search analysis</dc:subject>
<dc:identifier>https://pinboard.in/u:rybesh/b:d7854e7e5416/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:IR"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:critique"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:search"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:analysis"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.imgseek.net/imgSeekCmd.html">
    <title>imgSeekCmd User Guide</title>
    <dc:date>2009-04-09T16:18:20+00:00</dc:date>
    <link>http://www.imgseek.net/imgSeekCmd.html</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[Content-based image search. The searching algorithm makes use of multiresolution wavelet decomposition of the query and database images.
]]></description>
<dc:subject>image search tools contentanalysis python</dc:subject>
<dc:identifier>https://pinboard.in/u:rybesh/b:aae078239220/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:image"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:search"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:tools"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:contentanalysis"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:python"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.cuil.com/info/blog/2009/03/31/launching-timelines">
    <title>Cuil - Cuil Blog: Launching Timelines</title>
    <dc:date>2009-04-02T03:03:42+00:00</dc:date>
    <link>http://www.cuil.com/info/blog/2009/03/31/launching-timelines</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[We make it easy to explore the events in the timeline, just move your mouse over an event and a pop-up will appear with a longer description and a link to search for related pages. Beyond people, timelines can be a useful tool for displaying information about a period in history, such as the Great Depression. Or a famous sports arena, like Madison Square Garden. Or, say, the highest bridge in the world, the Millau Viaduct.
]]></description>
<dc:subject>events timeline infoviz search interface</dc:subject>
<dc:identifier>https://pinboard.in/u:rybesh/b:51351bc3411c/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:events"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:timeline"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:infoviz"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:search"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:interface"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://code.flickr.com/blog/2009/03/18/building-fast-client-side-searches/">
    <title>Code: Flickr Developer Blog » Building Fast Client-side Searches</title>
    <dc:date>2009-03-25T18:14:49+00:00</dc:date>
    <link>http://code.flickr.com/blog/2009/03/18/building-fast-client-side-searches/</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[Fetching the data using a dynamically generated script tag... the difference in performance was shocking.
]]></description>
<dc:subject>search javascript performance webservices ajax ui json</dc:subject>
<dc:identifier>https://pinboard.in/u:rybesh/b:9ad9a6719dfc/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:search"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:javascript"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:performance"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:webservices"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:ajax"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:ui"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:json"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://lethain.com/entry/2009/feb/25/django-springsteen-and-distributed-search/">
    <title>django-springsteen and Distributed Search @ Irrational Exuberance</title>
    <dc:date>2009-02-26T02:05:31+00:00</dc:date>
    <link>http://lethain.com/entry/2009/feb/25/django-springsteen-and-distributed-search/</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[Provides a trivial wrapper for Yahoo! BOSS, but goes further and provides a simple framework for building distributed search networks.
]]></description>
<dc:subject>search python django yahoo distributed</dc:subject>
<dc:identifier>https://pinboard.in/u:rybesh/b:b69264e89905/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:search"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:python"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:django"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:yahoo"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:distributed"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.loc.gov/standards/sru/oasis.html">
    <title>SRU/CQL Standardization in OASIS</title>
    <dc:date>2009-01-15T16:54:50+00:00</dc:date>
    <link>http://www.loc.gov/standards/sru/oasis.html</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[The premise behind dynamic bindings is that any search engine, even one that existed prior to development of the standard, need only to provide a dynamic binding -  a self-description. It need make no other changes in order to be accessible. A client will be able to access any search engine that provides a description, if only it implements the capability to read and interpret the description and use it to formulate a request (including a query) and interpret the response.
]]></description>
<dc:subject>metadata search standards webservices IR</dc:subject>
<dc:identifier>https://pinboard.in/u:rybesh/b:199acae26fb8/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:metadata"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:search"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:standards"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:webservices"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:IR"/>
</rdf:Bag></taxo:topics>
</item>
</rdf:RDF>