{"type":"ScholarlyArticle","title":"Enriching Documents with Compact, Representative, Relevant Knowledge Graphs","authors":[{"name":"Shuxin Li","slug":"shuxin-li","given":"Shuxin","family":"Li","affiliation":"National Key Laboratory for Novel Software Technology , Nanjing University , China"},{"name":"Zixian Huang","slug":"zixian-huang","given":"Zixian","family":"Huang","affiliation":"National Key Laboratory for Novel Software Technology , Nanjing University , China"},{"name":"Gong Cheng","slug":"gong-cheng","given":"Gong","family":"Cheng","affiliation":"National Key Laboratory for Novel Software Technology , Nanjing University , China"},{"name":"Evgeny Kharlamov","slug":"evgeny-kharlamov","given":"Evgeny","family":"Kharlamov","affiliation":"Bosch Center for Artificial Intelligence , Robert Bosch GmbH , Germany; Department of Informatics , University of Oslo , Norway"},{"name":"Kalpa Gunaratna","slug":"kalpa-gunaratna","given":"Kalpa","family":"Gunaratna","affiliation":"Samsung Research America , Mountain View CA , USA"}],"abstract":"A prominent application of knowledge graph (KG) is document enrichment. Existing methods identify mentions of entities in a background KG and enrich documents with entity types and direct relations. We compute an entity relation subgraph (ERG) that can more expressively represent indirect relations among a set of mentioned entities. To find compact, representative, and relevant ERGs for effective enrichment, we propose an efficient best-first search algorithm to solve a new combinatorial optimization problem that achieves a trade-off between representativeness and compactness, and then we exploit ontological knowledge to rank ERGs by entity-based document-KG and intra-KG relevance. Extensive experiments and user studies show the promising performance of our approach.","doi":"10.24963/ijcai.2020/242","url":"https://ijcai.org/proceedings/2020/242","pdf":"https://ijcai.org/proceedings/2020/0242.pdf","markdown":"https://ijcai.org/proceedings/2020/242.md","bibtex":"https://ijcai.org/proceedings/2020/bibtex/242","volume":{"slug":"2020","title":"Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2020","url":"https://ijcai.org/proceedings/2020/","year":2020,"location":"Yokohama","dates":"January 2021 (postponed from 11-17 July 2020)","editors":"Christian Bessiere","isbn":"978-0-9992411-6-5","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2020"},"section":"Knowledge Representation and Reasoning","track":"Main track","number":242,"pages":{"first":1748,"last":1754},"keywords":["Data Mining: Mining Graphs, Semi Structured Data, Complex Data","Knowledge Representation and Reasoning: Semantic Web"],"videos":[{"label":"Video","url":"https://videos.ijcai20.org/24216/file_24216.m3u8","length_sec":252,"provider":"hls"}],"references":[{"position":1,"raw":"[Akiba et al., 2013] Takuya Akiba, Yoichi Iwata, and Yuichi Yoshida. 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