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The Tabulator Extension is an extension for Firefox that provides a human-readable interface for linked data. It is based on the Tabulator, a web-based interface for browsing RDF. Using Tabulator’s outline mode, query views, and back-end code, the Tabulator Extension integrates the browsing of linked data directly into the Firefox browser, making for a more natural and seamless experience when browsing linked data on the Web.

A primary goal of the Tabulator Extension is to explore how linked data could be displayed in the next generation of Web browsers. The Tabulator aims to make linked data human-readable by taking a document and picking out the actual things that the document describes. The properties of these things are the displayed in a table, and then the links in that table can be followed to load more data about other things in other documents.

A link to the latest version of the extension can be found on the Tabulator Extension site: The Tabulator Extension. Moreover, Tabulator is now hosted on If you download and install from there, it will provide automatic updates through the Firefox Addon Manager.


Once the extension file is downloaded, it should automatically install. After restarting Firefox, all documents served as application/rdf+xml and text/n3 (and for a while legacy documents served as text/rdf+n3) will be automatically loaded in the Tabulator’s outline view. It may be necessary to disable other RDF-related extensions that could override the Tabulator’s capture of these documents.


For more information, read this article : Tabulator: Exploring and Analyzing linked data on the Semantic Web

This topic will be discussed in the Webinar on 5 March 2009. Actually, there are two well known technical issues when reasoning with ontologies that contain hundreds of thousands of classes/subclasses and where change happens frequently.


The first problem, materializing type information, takes far too much time. In some triple stores, materialization takes almost as long as loading the data. Once an ontology changes, the entire materialization process has to start over.

The second problem, optimizing a SPARQL engine for a reasoning triple store, is more challenging than just using SPARQL as a retrieval language. In a non-reasoning SPARQL engine, optimizing is relatively straightforward, applying the right hash and sort joins once given the statistics of the database when it reorders appropriately. However, when SPARQL is used on top of a reasoner, suddenly additional considerations are required. In practice, you only know the statistics of each clause after you have done the reasoning.

This Webinar will discuss a new solution that mitigates or nearly solves both problems. We will discuss some indexing techniques that do not require materialization and we will cover how an ordinary backtracking technique can be very fast with the right reordering.

Register for this webinar at: