AQUAINT Mid-Year Workshop: Observations and Comments Jimmy Lin MIT Artificial Intelligence Laboratory.

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Presentation transcript:

AQUAINT Mid-Year Workshop: Observations and Comments Jimmy Lin MIT Artificial Intelligence Laboratory

QA and IR Question Answering as an extension of Information Retrieval. Document Retrieval Passage Retrieval Factoid Question Answering + NLP Technology

QA and Databases Question Answering as an extension of Database Systems. Question Answering as knowledge brokering. Why? Databases often provide better answers. Lots of valuable structured/semi-structured resources available on the Web. Rich body of literature to capitalize on.

Cross Fertilization Early “classic” works: BASEBALL [Green et al., 61] LIFER [Hendrix et al., 77] LUNAR [Woods et al., 72] Plenty of research on integration of semistructured data: ARANEUS [Atzeni et al., 1997] ARIADNE [Knoblock et al., 1999] Information Maniford [Kirk et al., 1995] TSIMMIS [Hammer et al., 1997]

Advantages for Analysts Multimedia access. Knowledge Discovery. Better control of source quality and verification. Uniform coverage of a domain.

START A natural language interface to heterogeneous data sources on the Web. [Katz 97; Katz 02]

START and Ominbase Omnibase is a “virtual” database system that integrates heterogeneous, semistructured data. START: natural language queries  structured Omnibase queries. Omnibase executes these queries by Fetching the relevant pages. Extracting the relevant fragments. START performs additional generation.

Current Focus Streamline knowledge integration: Better authoring tools. Smarter parsers. “Conceptual Segmentation.” Wrapper Induction via Machine Learning. Semantic Web: RDF is a rich source of metadata.

Combining the Approaches Complementary Approaches: QA as DB: suitable to handling database-like queries. QA as IR: general purpose solution. More Info: Boris Katz and Jimmy Lin. START, Omnibase, and Beyond. AQUAINT Mid-Year Workshop. Jimmy Lin. The Web as a Resource for Question Answering: Perspectives and Challenges. LREC’2002. Boris Katz et. al. Omnibase: Uniform Access to Heterogenous Data for Question Answering.