MODERATOR:
Wen Ruan, TextWise

PANELISTS:
Ronald M. Kaplan, Powerset division of bing
Christian F. Hempelmann, RiverGlass, Inc.
Riza Berkan, hakia

Semantic Search technology in the Semantic Web community is often understood as retrieval of knowledge from tagged data such as RDF sources, which require substantial formatting and markup to realize. Understanding unstructured query and document text and conducting searches according to their meaning is another approach, exemplified by linguistically rooted semantic matching, ontological knowledge-based semantic interpretation, and statistically based semantic similarity search.

This panel will look at different ways to tackle semantic search as a problem of text understanding. Powerset division of bing's natural-language processing engine does deep syntactical analysis to determine the meaning of a query or a sentence. Hakia relies on a language-independent ontology model and an ontology-based English lexicon to translate text into a representation of its meaning. RiverGlass has developed an ontological semantic approach to search and text analytics, emphasizing the in-context, linguistic meaning of textual content in order to return truly relevant results in response to information requests. TextWise's Semantic Signature matching looks for similarities between a query and text at the topic level.