Commonsense knowledge about part-whole relations (e.g., screen partOf notebook) is important for interpreting user input in web search and question answering, or for object detection in images. Prior work on knowledge base construction has compiled part-whole assertions, but with substantial limitations: i) semantically different kinds of part-whole relations are conflated into a single generic relation, ii) the arguments of a part-whole assertion are merely words with ambiguous meaning, iii) the assertions lack additional attributes like visibility (e.g., a nose is visible but a kidney is not) and cardinality information (e.g., a bird has two legs while a spider eight), iv) limited coverage of only tens of thousands of assertions. This paper presents a new method for automatically acquiring part-whole commonsense from Web contents and image tags at an unprecedented scale, yielding many millions of assertions, while specifically addressing the four shortcomings of prior work. Our method combines pattern-based information extraction methods with logical reasoning. We carefully distinguish different relations: physicalPartOf, memberOf, substanceOf. We consistently map the arguments of all assertions onto WordNet senses, eliminating the ambiguity of wordlevel assertions. We identify whether the parts can be visually perceived, and infer cardinalities for the assertions. The resulting commonsense knowledge base has very high quality and high coverage, with an accuracy of 89% determined by extensive sampling, and is publicly available.
|Title of host publication||30th AAAI Conference on Artificial Intelligence, AAAI 2016|
|Number of pages||8|
|Publication status||Published - 2016|
|Event||30th AAAI Conference on Artificial Intelligence, AAAI 2016 - Phoenix, United States|
Duration: 12 Feb 2016 → 17 Feb 2016
|Conference||30th AAAI Conference on Artificial Intelligence, AAAI 2016|
|Period||12/02/16 → 17/02/16|