Quasimodo KB

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.

 

Publication

  • Commonsense Properties from Query Logs and Question Answering Forums, Julien Romero, Simon Razniewski, Koninika Pal, Jeff Z. Pan, Archit Sakhadeo, Gerhard Weikum, arXiv [pdf]

Data and Code

Full data:

Format:

  • subject: The Subject of the triple
  • predicate: The Predicate of the triple
  • object: The object of the triple
  • modality: Modalities associated with the triples with their counts. TBC means the object can be further refined to the listed objects
  • is_negative: 1 if the statement was negated
  • score: Precision score from the supervised corroboration module (0=worst, 1=best)
  • sentences_source: Sentences from which the triple is extracted
  • modules_source: The modules which gave a scoring for this triple
  • tau_score: The typicality score
  • sigma_score: The saliency score

Simplified samples:

Code:

  • To be released