Abstract
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 language | English |
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| Title of host publication | 26th International Joint Conference on Artificial Intelligence, IJCAI 2017 |
| Editors | Carles Sierra |
| Publisher | International Joint Conferences on Artificial Intelligence |
| Pages | 4244-4250 |
| Number of pages | 7 |
| ISBN (Electronic) | 9780999241103 |
| Publication status | Published - 2017 |
| Event | 26th International Joint Conference on Artificial Intelligence, IJCAI 2017 - Melbourne, Australia Duration: 2017 Aug 19 → 2017 Aug 25 |
Publication series
| Name | IJCAI International Joint Conference on Artificial Intelligence |
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| ISSN (Print) | 1045-0823 |
Other
| Other | 26th International Joint Conference on Artificial Intelligence, IJCAI 2017 |
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| Country/Territory | Australia |
| City | Melbourne |
| Period | 17/8/19 → 17/8/25 |
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