FactForge (formerly LDSR) 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.

FactForge enables users to easily identify resources in the Linking Open Data (LOD) cloud. It provides efficient mechanism to query data from multiple datasets and sources, considering their semantics. FactForge is designed also as a use-case for large-scale reasoning and data integration.

FactForge includes several of the most central datasets of LOD. OWLIM semantic repository is used to load the data and "materialize" the facts that could be inferred from it. It is probably the largest and most heterogeneous body of general factual knowledge that was ever used for logical inference. Read more: presentation and brochure.

The Data

FactForge has the following characteristics:

• Datasets included: DBpedia, Freebase, Geonames, UMBEL, WordNet, CIA World Factbook, Lingvoj, MusicBrainz (RDF from Zitgist).
• Ontologies: several schemata used in the datasets are also loaded into FactForge: Dublin Core, SKOS, RSS, FOAF.
• Size: 1.2B explicit plus 0.8B inferred statements are indexed; there are 10B different retrievable statements.
• Inference: materialization is performed with respect to the semantics of RDFS, extended with incomplete support for OWL Lite.

Access: Public Service at  http://factforge.net

The data is accessible through a web use interface at http://factforge.net, which allows:

• RDF Search - retrieve ranked list of URIs related to literals, which contain specific keywords
• Exploration - traversing the data, one resource at a time 
  For instance, one can "browse" Madrid with its DBpedia URI, <http://dbpedia.org/resource/Madrid> or dbpedia:Madrid
• Evaluation of queries in SPARQL and other languages. 
  For instance, to obtain a list of entertainers born in Bavaria one can use the following SPARQL query:

  (...add prefixes here...)SELECT * WHERE { 
   ?Person dbp-ont:birthPlace [geo-ont:parentFeature dbpedia:Bavaria] ; 
   rdf:type opencyc:Entertainer ; 
   om:hasRDFRank ?RR . 
  } ORDER BY DESC(?RR) LIMIT 100 

  This is an example of a structured query, the evaluation of which involves data from 4 datasets and interpretation of the semantics of several schemata (i.e. reasoning). Within few seconds it returns results ranked by PageRank (in the RDF graph). OpenCyc's vocabulary is used, although the dataset itself is not loaded in FactForge (it is only referred from UMBEL). Lou Bega appears on the top of the list, because of the data from MusicBrainz, although the latter dataset is not explicitly referred in the query.

A public SPARQL end-point is available at http://factforge.net/sparql, allowing FactForge to be used as a query evaluation web service.

Credits and References

"Linked data" represents a set of principles for publishing of structured data they can be explored and navigated in a manner analogous to the HTML WWW. The linked data concept is an enabling factor for the realization of the Semantic Web as a global web of structured data around the Linking Open Data initiative.

FactForge is developed as an evaluation case in the European research project LarKC. The development of OWLIM, as well as other relevant technology and know-how, has been supported by several projects within programs FP5, FP6, and FP7 of the European Commission: RASCALLI, TAO, TripCom, SEKT, On-To-Knowledge.

The Linked Life Data service is similar to FactForge. It represents a reason-able view towards the life science part of LOD, including UniProt, GeneOntology, and more than 20 other datasets. FactForge and LLD are based on the same technology: Forest semantic web fronts-ends and OWLIM semantic repository. With its 5 billion explicit statements, LLD is probably the largest body of non-synthetic knowledge that was used for inference.

Notes and Disclaimers

FactForge is an experimental project from Ontotext. The access to this demonstration service is free of charge. Ontotext does not provide any guarantees for quality, availability, or fitness for particular purpose. FactForge is far from perfect. Here are few comments on known shortcomings and development plans:

• add more datasets and ontologies
• provide better exploration interfaces
• establish a regular update procedure (currently, FactForge is updated once per month on average)