01731nas a2200217 4500008004100000245004900041210004700090260003100137300000900168490001400177520111200191653001501303653002501318653002001343100002101363700002801384700003201412700001601444700001601460856003701476 2017 eng d00aRelatedness-based Multi-Entity Summarization0 aRelatednessbased MultiEntity Summarization aSydney, Australiac08/2017 a10600 vVol. 20173 aRepresenting 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-ofthe-art entity summarization approaches.10aclustering10aEntity Summarization10aknowledge graph1 aGunaratna, Kalpa1 aYazdavar, Amir, Hossein1 aThirunarayan, Krishnaprasad1 aSheth, Amit1 aCheng, Gong uhttp://www.knoesis.org/node/2875