Entity suggestion with conceptual explanation

Yi Zhang, Yanghua Xiao, Seung Won Hwang, Haixun Wang, X. Sean Wang, Wei Wang

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

6 Citations (Scopus)


Entity Suggestion with Conceptual Explanation (ESC) refers to a type of entity acquisition query in which a user provides a set of example entities as the query and obtains in return not only some related entities but also concepts which can best explain the query and the result. ESC is useful in many applications such as related-entity recommendation and query expansion. Many example based entity suggestion solutions are available in existing literatures. However, they are generally not aware of the concepts of query entities thus cannot be used for conceptual explanation. In this paper, we propose two probabilistic entity suggestion models and their computation solutions. Our models and solutions fully take advantage of the large scale taxonomies which consist of isA relations between entities and concepts. With our models and solutions, we can not only find the best entities to suggest but also derive the best concepts to explain the suggestion. Extensive evaluations on real data sets justify the accuracy of our models and the efficiency of our solutions.

Original languageEnglish
Title of host publication26th International Joint Conference on Artificial Intelligence, IJCAI 2017
EditorsCarles Sierra
PublisherInternational Joint Conferences on Artificial Intelligence
Number of pages7
ISBN (Electronic)9780999241103
Publication statusPublished - 2017
Event26th International Joint Conference on Artificial Intelligence, IJCAI 2017 - Melbourne, Australia
Duration: 2017 Aug 192017 Aug 25

Publication series

NameIJCAI International Joint Conference on Artificial Intelligence
ISSN (Print)1045-0823


Other26th International Joint Conference on Artificial Intelligence, IJCAI 2017

Bibliographical note

Funding Information:
‡Hwang’s work was supported by IITP grant funded by the Korea government (MSIP) (No.2014-0-00147, Basic Software Research in Human-level Lifelong Machine Learning (Machine Learning Center)).

Funding Information:
†Corresponding author. Xiao’s work was supported by National Key Basic Research Program of China (No.2015CB358800), the National NSFC (No.61472085, U1509213), Shanghai Municipal Science and Technology Commission foundation key project (No.15JC1400900), Shanghai Municipal Science and Technology project (No. 16511102102, No.16JC1420401) and Xiaoi Research.

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence


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