{"type":"ScholarlyArticle","title":"Relatedness-based Multi-Entity Summarization","authors":[{"name":"Kalpa Gunaratna","slug":"kalpa-gunaratna","given":"Kalpa","family":"Gunaratna","affiliation":"Kno.e.sis , Wright State University , Dayton OH , USA"},{"name":"Amir Hossein Yazdavar","slug":"amir-hossein-yazdavar","given":"Amir Hossein","family":"Yazdavar","affiliation":"Kno.e.sis , Wright State University , Dayton OH , USA"},{"name":"Krishnaprasad Thirunarayan","slug":"krishnaprasad-thirunarayan","given":"Krishnaprasad","family":"Thirunarayan","affiliation":"Kno.e.sis , Wright State University , Dayton OH , USA"},{"name":"Amit Sheth","slug":"amit-sheth","given":"Amit","family":"Sheth","affiliation":"Kno.e.sis , Wright State University , Dayton OH , USA"},{"name":"Gong Cheng","slug":"gong-cheng","given":"Gong","family":"Cheng","affiliation":"National Key Laboratory for Novel Software Technology , Nanjing University , China"}],"abstract":"Representing world knowledge in a machine processable format is important as entities and their descriptions have fueled tremendous growth in knowledge-rich information processing platforms, services, and systems. Prominent applications of knowledge graphs include search engines (e.g., Google Search and Microsoft Bing), email clients (e.g., Gmail), and intelligent personal assistants (e.g., Google Now, Amazon Echo, and Apple's Siri). In this paper, we present an approach that can summarize facts about a collection of entities by analyzing their relatedness in preference to summarizing each entity in isolation. Specifically, we generate informative entity summaries by selecting: (i) inter-entity facts  that are similar  and (ii) intra-entity facts that are important and diverse. We employ a constrained knapsack problem solving approach to efficiently compute entity summaries. We perform both qualitative and quantitative experiments and demonstrate that our approach yields promising results compared to two other stand-alone state-of-the-art entity summarization approaches.","doi":"10.24963/ijcai.2017/147","url":"https://ijcai.org/proceedings/2017/147","pdf":"https://ijcai.org/proceedings/2017/0147.pdf","markdown":"https://ijcai.org/proceedings/2017/147.md","bibtex":"https://ijcai.org/proceedings/2017/bibtex/147","volume":{"slug":"2017","title":"Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2017","url":"https://ijcai.org/proceedings/2017/","year":2017,"location":"Melbourne, Australia","dates":"19-25 August 2017","editors":"Carles Sierra","isbn":"978-0-9992411-0-3","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2017"},"section":"Knowledge Representation, Reasoning, and Logic","track":"Main track","number":147,"pages":{"first":1060,"last":1066},"keywords":["Natural Language Processing: Information Retrieval","Knowledge Representation, Reasoning, and Logic: Knowledge Representation Languages"],"videos":[],"references":[{"position":1,"raw":"[Auer et al., 2013] Sören Auer, Jens Lehmann, Axel- Cyrille Ngonga Ngomo, and Amrapali Zaveri. 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