Projektbeschreibung
Robuste Logik für semantische Suchmaschinen
Eine Suche nach Informationen im Internet ist nicht immer einfach. denn oft müssen unpassende Suchergebnisse durchforstet werden. Ein Problem der derzeitigen semantischen Suchmaschinen besteht darin, dass sie auf unrealistischen Annahme beruhen, dass alle wahren Fakten über einen bestimmten Bereich explizit in ihrer Wissensdatenbank oder im Internet aufgeführt sind. Relevante Informationen sind meist über mehrere inkonsistente Wissensdatenbanken verteilt, und die Domänentheorien sind selten vollständig. Ziel des vom Europäischen Forschungsrat finanzierten Projekts FLEXILOG ist es, semantische Suchmaschinen zu verbessern, indem eine Familie von Logiken für robuste Schlussfolgerungen mit realem Wissen eingeführt wird. Das Projektteam wird Vektorraumdarstellungen von Begriffen der natürlichen Sprache verwenden, um die Plausibilität logischer Modelle zu bewerten und so verschiedene Formen des logischen Denkens zu ermöglichen.
Ziel
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.
Wissenschaftliches Gebiet
- natural sciencescomputer and information sciencesknowledge engineeringontology
- natural sciencesmathematicsapplied mathematicsstatistics and probabilitybayesian statistics
- natural sciencescomputer and information sciencesdata sciencenatural language processing
- natural sciencescomputer and information sciencesartificial intelligencemachine learning
- natural sciencescomputer and information sciencesartificial intelligencecomputational intelligence
Programm/Programme
Thema/Themen
Finanzierungsplan
ERC-STG - Starting GrantGastgebende Einrichtung
CF24 0DE Cardiff
Vereinigtes Königreich