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Formal lexically informed logics for searching the web

Project description

Robust reasoning for semantic search engines

A simple search for information on the internet is not always simple. Often it involves sifting through irrelevant search results. One problem with current semantic search engines is that they rely on the unrealistic assumption that all true facts about a given domain are explicitly stated in their knowledge base or on the web. Relevant information is often distributed over several inconsistent knowledge bases, and domain theories are seldom complete. Funded by the European Research Council, the FLEXILOG project aims to improve semantic search engines by introducing a family of logics for robust reasoning with real-world knowledge. It will use vector-space representations of natural language terms to estimate the plausibility of logical models, enabling various forms of common-sense reasoning.

Objective

Semantic search engines use structured knowledge to improve traditional web search, e.g. by directly answering questions from users. Current approaches to semantic search rely on the unrealistic assumption that all true facts about a given domain are explicitly stated in their knowledge base or on the web. To reach their full potential, semantic search engines need the ability to reason about known facts. However, existing logics cannot adequately deal with the imperfect nature of knowledge from the web. One problem is that relevant information tends to be distributed over several heterogeneous knowledge bases that are inconsistent with each other. Moreover, domain theories are seldom complete, which means that a form of so-called plausible reasoning is needed. Finally, as relevant logical theories do not exist for many domains, reasoning may need to rely on imperfect probabilistic theories that have been learned from the web.

To overcome these challenges, FLEXILOG will introduce a family of logics for robust reasoning with messy real-world knowledge, based on vector-space representations of natural language terms (i.e. of lexical knowledge). In particular, we will use lexical knowledge to estimate the plausibility of logical models, using conceptual simplicity as a proxy for plausibility (i.e. Occam’s razor). This will enable us to implement various forms of commonsense reasoning, equipping classical logic with the ability to draw plausible conclusions based on regularities that are observed in a knowledge base. We will then generalise our approach to probabilistic logics, and show how we can use the resulting lexically informed probabilistic logics to learn accurate and comprehensive domain theories from the web. This project will enable a robust data-driven approach to logic-based semantic search, and more generally lead to fundamental progress in a variety of knowledge-intensive applications for which logical inference has traditionally been too brittle.

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Coordinator

CARDIFF UNIVERSITY
Net EU contribution
€ 1 451 656,00
Address
Newport road 30 36
CF24 0DE Cardiff
United Kingdom

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Region
Wales East Wales Cardiff and Vale of Glamorgan
Activity type
Higher or Secondary Education Establishments
Links
Other funding
€ 0,00

Beneficiaries (1)