GQBE: Querying knowledge graphs by example entity tuples

We present GQBE, a system that presents a simple and intuitive mechanism to query large knowledge graphs. Answers to tasks such as “list university professors who have designed some programming languages and also won an award in Computer Science” are best found in knowledge graphs that record entities and their relationships. Real-world knowledge graphs are difficult to use due to their sheer size and complexity and the challenging task of writing complex structured graph queries. Toward better usability of query systems over knowledge graphs, GQBE allows users to query knowledge graphs by example entity tuples without writing complex queries. In this demo we present: 1) a detailed description of the various features and user-friendly GUI of GQBE, 2) a brief description of the system architecture, and 3) a demonstration scenario that we intend to show the audience.

[1]  Moshé M. Zloof.  Query by example , 1975, AFIPS '75.

[2]  Yizhou Sun,et al.  Query-driven discovery of semantically similar substructures in heterogeneous networks , 2012, KDD.

[3]  Sihem Amer-Yahia,et al.  Structure and Content Scoring for XML , 2005, VLDB.

[4]  Ramez Elmasri,et al.  Towards a Query-by-Example System for Knowledge Graphs , 2014, GRADES.

[5]  Haixun Wang,et al.  Probase: a probabilistic taxonomy for text understanding , 2012, SIGMOD Conference.

[6]  Hinrich Schütze,et al.  Introduction to information retrieval , 2008 .

[7]  Nan Li,et al.  Neighborhood based fast graph search in large networks , 2011, SIGMOD '11.

[8]  Jacob Cohen.  Statistical Power Analysis for the Behavioral Sciences , 1969, The SAGE Encyclopedia of Research Design.

[9]  Wolfgang Nejdl,et al.  FreeQ: an interactive query interface for freebase , 2012, WWW.

[10]  Ramez Elmasri,et al.  Querying Knowledge Graphs by Example Entity Tuples , 2013, IEEE Transactions on Knowledge and Data Engineering.

[11]  Jignesh M. Patel,et al.  TALE: A Tool for Approximate Large Graph Matching , 2008, 2008 IEEE 24th International Conference on Data Engineering.

[12]  Gerhard Weikum,et al.  MING: mining informative entity relationship subgraphs , 2009, CIKM.

[13]  Jeffrey Xu Yu,et al.  Finding information nebula over large networks , 2011, CIKM '11.

[14]  Marios D. Dikaiakos,et al.  A Query Formulation Language for the Data Web , 2012, IEEE Transactions on Knowledge and Data Engineering.

[15]  Praveen Paritosh,et al.  Freebase: a collaboratively created graph database for structuring human knowledge , 2008, SIGMOD Conference.

[16]  Xiang-Sun Zhang,et al.  Exploring the constrained maximum edge-weight connected graph problem , 2009 .

[17]  Christos Faloutsos,et al.  Fast discovery of connection subgraphs , 2004, KDD.

[18]  Daniel J. Abadi,et al.  Scalable Semantic Web Data Management Using Vertical Partitioning , 2007, VLDB.

[19]  Philip S. Yu,et al.  PathSim , 2011, Proc. VLDB Endow..

[20]  Cong Yu,et al.  REX: Explaining Relationships between Entity Pairs , 2011, Proc. VLDB Endow..

[21]  William W. Cohen,et al.  Language-Independent Set Expansion of Named Entities Using the Web , 2007, Seventh IEEE International Conference on Data Mining (ICDM 2007).

[22]  Junjie Yao,et al.  Keyword Query Reformulation on Structured Data , 2012, 2012 IEEE 28th International Conference on Data Engineering.

[23]  Themis Palpanas,et al.  Exemplar Queries: Give me an Example of What You Need , 2014, Proc. VLDB Endow..

[24]  Ramez Elmasri,et al.  Querying Knowledge Graphs by Example Entity Tuples , 2015, IEEE Trans. Knowl. Data Eng..

[25]  Eugene W. Myers,et al.  Finding All Spanning Trees of Directed and Undirected Graphs , 1978, SIAM J. Comput..

[26]  Christos Faloutsos,et al.  Fast best-effort pattern matching in large attributed graphs , 2007, KDD '07.

[27]  Fabian M. Suchanek,et al.  Yago: A Core of Semantic Knowledge Unifying WordNet and Wikipedia , 2007 .

[28]  Haixun Wang,et al.  Semantic queries by example , 2013, EDBT '13.

[29]  Jens Lehmann,et al.  DBpedia: A Nucleus for a Web of Open Data , 2007, ISWC/ASWC.

[30]  Ihab F. Ilyas,et al.  Expressive and flexible access to web-extracted data: a keyword-based structured query language , 2010, SIGMOD Conference.

[31]  S. F. Begum,et al.  Meta Path Based Top-K Similarity Join In Heterogeneous Information Networks , 2016 .

[32]  Adriane Chapman,et al.  Making database systems usable , 2007, SIGMOD '07.