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...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:IEEE transactions on knowledge and data engineering Jg. 30; H. 10; S. 1887 - 1900
Hauptverfasser: Song, Qi, Wu, Yinghui, Lin, Peng, Dong, Luna Xin, Sun, Hui
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York IEEE 01.10.2018
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
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
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung: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.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1041-4347
1558-2191
DOI:10.1109/TKDE.2018.2807442