Knowledge Discovery Opportunties

The reality of Big Data allows querying from massive repositories of potentially interrelated facts. Unfortunately, as noted in prior Ontology Summits (cf, OntologySummit2009 Communique) and Ontology Summit 2016 CommuniqueOntologies within Semantic Interoperability Ecosystems representing this information in rich formation to make it useful knowledge is a formidable challenge. One interesting thrust to transform source material (typically natural language text) into a knowledge graph form.

A knowledge graph is a structure where entities are graph nodes, categories are word labels associated with each node, and relations are directed edges between the nodes. A knowledge graph is thus one simplified version of an ontology and something less formal than Sowa's conceptual graphs. Such efforts to build even this simple structure require resolving entity identification and entity relationships. There is a degree of uncertain and noise in and about such relationships targeted in these extractions as well as the need to infer missing information, and determining which candidate facts should be included into a knowledge graph as part of the identification process.

Challenges

One approach is to:

1. associate extraction confidences along with candidate facts
2. to identify co-referent entities, and
3. incorporate ontological constraints.

All of these are challenging and one approach relies on probabilistic soft logic (PSL), a recently introduced probabilistic modeling framework which easily scales to millions of facts such as demonstrated with extractions from the NELL project containing over 1M extractions and 70K ontological relations. [Pujara et al, 2013]

Ram will provide more ...

References

http://ontolog.cim3.net/wiki/OntologySummit2009_Communique.html https://s3.amazonaws.com/ontologforum/OntologySummit2016/2016-06-02_Communique/OntologySummit2016Communique_v1.2.pdf Pujara, Jay, et al. "Knowledge graph identification." (2013): 542.