Properties of SAIM

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  • (Semi-)Automatic Instance Matcher
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    User Interface

    A pre-alpha version of the SAIM user interface is available here. SAIM is localized (the screenshot shows it in a german browser) with english and german localization existing. If you want to provide an additional language just let us know and we can provide you with a resource file template.

    Veri Links (Game Based Link Verification)

    As a module of SAIM, we developed an interlinking game with a prupose (GWAP). By verifying links, you earn points, which you can use in the main game to defend against invadors (and save world - as usual). If you disagree with many other users, then you can also get penalties. In the admin console, arbitrary linksets can be uploaded. Depending on the data, facts about resources are displayed in lists or on a map. Try it: http://verilinks.aksw.org

    Developing SAIM using Eclipse

    1. Make sure you have Java 6, Maven 2 and Eclipse (preferably JEE) installed.
    2. Install the Subclipse plugin for Subversion support.
    3. Install the Eclipse m2e Maven plugin for Maven support and m2e extras "Maven SCM handler for Subclipse".
    4. "File >> New >> Other..." >> "Checkout Maven Projects from SCM"
    5. Set SCM URL type to "svn" and enter https://saim.svn.sourceforge.net/svnroot/saim/trunk as URL
    6. Choose "Finish"
abstract
  • The original intention behind SAIM (pronounced like “same”) was to provide an interface desgined to support instance matching on RDF data. Its main apllication was then intended to support the discovery of links between knowledge bases across the Linked Data Web with semi-automic approaches. In addition to providing achieving this goal by provinding interfaces that allow users either manually or semi-automatically generating class matchings, property matchings and link specifications, SAIM has been paired with LIMES and now provides an extended range of functionality. For example, it now includes interfaces the fully unsupervised learning of link specifications. In addition, LIMES' machine learning algorithms such as RAVEN (RApid actiVE liNking) and EAGLE (Efficient Active Learning of Link Specifications Using Genetic Programming) can be utilized in SAIM.
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  • SAIM
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