Commonsense Properties from Query Logs and Question Answering Forums

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 Quasi-modo, 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.
Complete list of metadatas

Cited literature [42 references]  Display  Hide  Download
Contributor : Julien Romero <>
Submitted on : Tuesday, June 18, 2019 - 11:01:27 AM
Last modification on : Monday, July 8, 2019 - 2:59:27 PM


Files produced by the author(s)


  • HAL Id : hal-02158602, version 1
  • ARXIV : 1905.10989



Julien Romero, Simon Razniewski, Gerhard Weikum, Jeff Z. Pan, Archit Sakhadeo, et al.. Commonsense Properties from Query Logs and Question Answering Forums. 2019. ⟨hal-02158602⟩



Record views


Files downloads