Yahoo Researcher Seeks to Combine Semantic Search Methods
Yahoo researcher Peter Mika has written up an extensive article on semantic search. First he talks about the limitations to syntax-based search:
Mika says there are two approaches to semantic search: Natural Language Processing (NLP) and the Semantic Web.
Natural Language Processing “builds on the automatic analysis of text.” Semantic search company hakia is an example of natural language processing. Interestingly, hakia uses Yahoo search technology, including the recently announced Yahoo’s BOSS (Build Your own Search Service). Powerset, which was recently acquired by Microsoft, is another example of NLP. These NLP semantic search providers “extract entities from text, disambiguate them against large-scale background knowledge sources (PowerSet uses Freebase, Hakia has its own ontology), and then record the relationships as found in the text.” Users can query by asking full questions, though many still use keywords.
Semantic Web “aims to make the web more easily searchable by allowing publishers to expose their metadata.” Mika says most publishers are willing to share their data if it results in increased traffic. Plus, semantic web allows publishers to avoid costs and quality issues associated with NLP. But last year, Yahoo researcher Mor Naaman declared the Semantic Web dead. Naaman’s reasoning was the limitation of microformats, but Mika says that the new RDFa standard would have greater capabilities.
What Mika wants to do is to integrate the best of NLP and semantic web. He says Yahoo’s SearchMonkey platform allows for this integration to occur.
To dig into all the technical nitty gritty, check out Mika’s full article, “Semantic Search Arrives at the Web.”