Commonsense Properties from Query Logs and Question Answering Forums

Julien Romero, Simon Razniewski, Koninika Pal, Jeff Z. Pan, Archit Sakhadeo, Gerhard Weikum

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Commonsense knowledge about object properties, human behavior and general concepts is crucial for robust AI applications. However, automatic acquisition of this knowledge is challenging because of sparseness and bias in online sources. This paper presents Quasimodo, a methodology and tool suite for distilling commonsense properties from non-standard web sources. We devise novel ways of tapping into search-engine query logs and QA forums, and combining the resulting candidate assertions with statistical cues from encyclopedias, books and image tags in a corroboration step. Unlike prior work on commonsense knowledge bases, Quasimodo focuses on salient properties that are typically associated with certain objects or concepts. Extensive evaluations, including extrinsic use-case studies, show that Quasimodo provides better coverage than state-of-the-art baselines with comparable quality.
Original languageEnglish
Title of host publicationProceedings of the 28th ACM International Conference on Information and Knowledge Management
Place of PublicationNew York, NY, USA
PublisherAssociation for Computing Machinery (ACM)
Pages1411–1420
Number of pages10
ISBN (Print)9781450369763
DOIs
Publication statusPublished - 3 Nov 2019

Keywords

  • commonsense knowledge acquisition
  • information extraction
  • web mining

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