Keyword-Based Knowledge Graph Exploration Based on Quadratic Group Steiner Trees

Exploring complex structured knowledge graphs (KGs) is challenging for non-experts as it requires knowledge of query languages and the underlying structure of the KGs. Keyword-based exploration is a convenient paradigm, and computing a group Steiner tree (GST) as an answer is a popular implementation. Recent studies suggested improving the cohesiveness of an answer where entities have small semantic distances from each other. However, how to efficiently compute such an answer is open. In this paper, to model cohesiveness in a generalized way, the quadratic group Steiner tree problem (QGSTP) is formulated where the cost function extends GST with quadratic terms representing semantic distances. For QGSTP we design a branch-and-bound best-first (BF) algorithm where we exploit combinatorial methods to estimate lower bounds for costs. This exact algorithm shows practical performance on medium-sized KGs.

[1]  Jaroslaw Szlichta,et al.  Robust keyword search in large attributed graphs , 2020, Information Retrieval Journal.

[2]  Gong Cheng,et al.  GREASE: A Generative Model for Relevance Search over Knowledge Graphs , 2019, WSDM.

[3]  Gong Cheng.  Relationship search over knowledge graphs , 2020, SIGWEB Newsl..

[4]  Dimitri Theodoratos,et al.  Incorporating Cohesiveness into Keyword Search on Linked Data , 2015, WISE.

[5]  Kristof Van Laerhoven,et al.  ISWC 2020 , 2021, IEEE Pervasive Computing.

[6]  Alfred C. Weaver,et al.  Ieee Transactions on Knowledge and Data Engineering 1 an Empirical Performance Evaluation of Relational Keyword Search Techniques , 2022 .

[7]  P. Gács,et al.  Algorithms , 1992 .

[8]  Edmund Ihler,et al.  The Complexity of Approximating the Class Steiner Tree Problem , 1991, WG.

[9]  Yuzhong Qu,et al.  HIEDS: A Generic and Efficient Approach to Hierarchical Dataset Summarization , 2016, IJCAI.

[10]  Evgeny Kharlamov,et al.  Efficient Computation of Semantically Cohesive Subgraphs for Keyword-Based Knowledge Graph Exploration , 2021, WWW.

[11]  Jeff Z. Pan,et al.  Relevance Search over Schema-Rich Knowledge Graphs , 2019, WSDM.

[12]  Haofen Wang,et al.  Top-k Exploration of Query Candidates for Efficient Keyword Search on Graph-Shaped (RDF) Data , 2009, 2009 IEEE 25th International Conference on Data Engineering.

[13]  Gong Cheng,et al.  Relaxing Relationship Queries on Graph Data , 2020, J. Web Semant..

[14]  R. Ravi,et al.  A nearly best-possible approximation algorithm for node-weighted Steiner trees , 1993, IPCO.

[15]  Shan Wang,et al.  Finding Top-k Min-Cost Connected Trees in Databases , 2007, 2007 IEEE 23rd International Conference on Data Engineering.

[16]  Evgeny Kharlamov,et al.  Towards a semantic keyword search over industrial knowledge graphs (extended abstract) , 2017, 2017 IEEE International Conference on Big Data (Big Data).

[17]  Wentao Ding,et al.  Generating Illustrative Snippets for Open Data on the Web , 2017, WSDM.

[19]  Krisztian Balog,et al.  DBpedia-Entity v2: A Test Collection for Entity Search , 2017, SIGIR.

[20]  Ke Zhang,et al.  Generating Compact and Relaxable Answers to Keyword Queries over Knowledge Graphs , 2020, SEMWEB.

[21]  Feifei Li,et al.  Scalable Keyword Search on Large RDF Data , 2014, IEEE Transactions on Knowledge and Data Engineering.

[22]  G. G. Stokes.  "J." , 1890, The New Yale Book of Quotations.

[23]  S. Sudarshan,et al.  Bidirectional Expansion For Keyword Search on Graph Databases , 2005, VLDB.

[24]  Aijun An,et al.  Keyword Search in Graphs: Finding r-cliques , 2011, Proc. VLDB Endow..

[25]  Lingling Zhang,et al.  SPARQA: Skeleton-based Semantic Parsing for Complex Questions over Knowledge Bases , 2020, AAAI.

[26]  Anthony K. H. Tung,et al.  An Efficient Parallel Keyword Search Engine on Knowledge Graphs , 2019, 2019 IEEE 35th International Conference on Data Engineering (ICDE).

[27]  Evgeny Kharlamov,et al.  Keyword Search over Knowledge Graphs via Static and Dynamic Hub Labelings , 2020, WWW.

[28]  Yuzhong Qu,et al.  An Empirical Evaluation of Techniques for Ranking Semantic Associations , 2017, IEEE Transactions on Knowledge and Data Engineering.

[29]  Jeffrey Xu Yu,et al.  Efficient and Progressive Group Steiner Tree Search , 2016, SIGMOD Conference.