Mining Summaries for Knowledge Graph Search

Querying heterogeneous and large-scale knowledge graphs is expensive. This paper studies a graph summarization framework to facilitate knowledge graph search. (1) We introduce a class of reduced summaries . Characterized by approximate graph pattern matching, these summaries are capable of summarizi...

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Published in:	IEEE transactions on knowledge and data engineering Vol. 30; no. 10; pp. 1887 - 1900
Main Authors:	Song, Qi, Wu, Yinghui, Lin, Peng, Dong, Luna Xin, Sun, Hui
Format:	Journal Article
Language:	English
Published:	New York IEEE 01.10.2018 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	Algorithms Data mining Feasibility studies Graph matching Graph summarization Graphs Knowledge based systems Motion pictures parallel algorithm Parallel algorithms Pattern matching pattern mining Query processing Scalability Summaries Time factors
ISSN:	1041-4347, 1558-2191
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Abstract	Querying heterogeneous and large-scale knowledge graphs is expensive. This paper studies a graph summarization framework to facilitate knowledge graph search. (1) We introduce a class of reduced summaries . Characterized by approximate graph pattern matching, these summaries are capable of summarizing entities in terms of their neighborhood similarity up to a certain hop, using small and informative graph patterns. (2) We study a diversified graph summarization problem. Given a knowledge graph, it is to discover top-<inline-formula> <tex-math notation="LaTeX">k</tex-math> <inline-graphic xlink:href="song-ieq1-2807442.gif"/> </inline-formula> summaries that maximize a bi-criteria function, characterized by both informativeness and diversity. We show that diversified summarization is feasible for large graphs, by developing both sequential and parallel summarization algorithms. (a) We show that there exists a 2-approximation algorithm to discover diversified summaries. We further develop an anytime sequential algorithm which discovers summaries under resource constraints. (b) We present a new parallel algorithm with quality guarantees. The algorithm is parallel scalable, which ensures its feasibility in distributed graphs. (3) We also develop a summary-based query evaluation scheme, which only refers to a small number of summaries. Using real-world knowledge graphs, we experimentally verify the effectiveness and efficiency of our summarization algorithms, and query processing using summaries.
AbstractList	Querying heterogeneous and large-scale knowledge graphs is expensive. This paper studies a graph summarization framework to facilitate knowledge graph search. (1) We introduce a class of reduced summaries. Characterized by approximate graph pattern matching, these summaries are capable of summarizing entities in terms of their neighborhood similarity up to a certain hop, using small and informative graph patterns. (2) We study a diversified graph summarization problem. Given a knowledge graph, it is to discover top-k summaries that maximize a bi-criteria function, characterized by both informativeness and diversity. We show that diversified summarization is feasible for large graphs, by developing both sequential and parallel summarization algorithms. (a) We show that there exists a 2-approximation algorithm to discover diversified summaries. We further develop an anytime sequential algorithm which discovers summaries under resource constraints. (b) We present a new parallel algorithm with quality guarantees. The algorithm is parallel scalable, which ensures its feasibility in distributed graphs. (3) We also develop a summary-based query evaluation scheme, which only refers to a small number of summaries. Using real-world knowledge graphs, we experimentally verify the effectiveness and efficiency of our summarization algorithms, and query processing using summaries. Querying heterogeneous and large-scale knowledge graphs is expensive. This paper studies a graph summarization framework to facilitate knowledge graph search. (1) We introduce a class of reduced summaries . Characterized by approximate graph pattern matching, these summaries are capable of summarizing entities in terms of their neighborhood similarity up to a certain hop, using small and informative graph patterns. (2) We study a diversified graph summarization problem. Given a knowledge graph, it is to discover top-<inline-formula> <tex-math notation="LaTeX">k</tex-math> <inline-graphic xlink:href="song-ieq1-2807442.gif"/> </inline-formula> summaries that maximize a bi-criteria function, characterized by both informativeness and diversity. We show that diversified summarization is feasible for large graphs, by developing both sequential and parallel summarization algorithms. (a) We show that there exists a 2-approximation algorithm to discover diversified summaries. We further develop an anytime sequential algorithm which discovers summaries under resource constraints. (b) We present a new parallel algorithm with quality guarantees. The algorithm is parallel scalable, which ensures its feasibility in distributed graphs. (3) We also develop a summary-based query evaluation scheme, which only refers to a small number of summaries. Using real-world knowledge graphs, we experimentally verify the effectiveness and efficiency of our summarization algorithms, and query processing using summaries.
Author	Wu, Yinghui Song, Qi Lin, Peng Dong, Luna Xin Sun, Hui
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SubjectTerms	Algorithms Data mining Feasibility studies Graph matching Graph summarization Graphs Knowledge based systems Motion pictures parallel algorithm Parallel algorithms Pattern matching pattern mining Query processing Scalability Summaries Time factors
Title	Mining Summaries for Knowledge Graph Search
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