Querying Linked Data Based on Hierarchical Multi-Hop Ranking Model

doi:10.1007/s12204-018-1976-z

Abstract

Abstract: How to query Linked Data effectively is a challenge due to its heterogeneous datasets. There are three types of heterogeneities, i.e., different structures representing entities, different predicates with the same meaning and different literal formats used in objects. Approaches based on ontology mapping or Information Retrieval (IR) cannot deal with all types of heterogeneities. Facing these limitations, we propose a hierarchical multi-hop language model (HMPM). It discriminates among three types of predicates, descriptive predicates, out-associated predicates and in-associated predicates, and generates multi-hop models for them respectively. All predicates’ similarities between the query and entity are organized into a hierarchy, with predicate types on the first level and predicates of this type on the second level. All candidates are ranked in ascending order. We evaluated HMPM in three datasets, DBpedia, LinkedMDB and Yago. The results of experiments show that the effectiveness and generality of HMPM outperform the existing approaches.

Key words: hierarchical multi-hop ranking model (HMPM)| Linked Data| language model

摘要： How to query Linked Data effectively is a challenge due to its heterogeneous datasets. There are three types of heterogeneities, i.e., different structures representing entities, different predicates with the same meaning and different literal formats used in objects. Approaches based on ontology mapping or Information Retrieval (IR) cannot deal with all types of heterogeneities. Facing these limitations, we propose a hierarchical multi-hop language model (HMPM). It discriminates among three types of predicates, descriptive predicates, out-associated predicates and in-associated predicates, and generates multi-hop models for them respectively. All predicates’ similarities between the query and entity are organized into a hierarchy, with predicate types on the first level and predicates of this type on the second level. All candidates are ranked in ascending order. We evaluated HMPM in three datasets, DBpedia, LinkedMDB and Yago. The results of experiments show that the effectiveness and generality of HMPM outperform the existing approaches.

关键词: hierarchical multi-hop ranking model (HMPM)| Linked Data| language model

CLC Number:

TP 391

LI Junxian (李俊娴), WANG Wei (汪卫), WANG Jingjing (王晶晶). Querying Linked Data Based on Hierarchical Multi-Hop Ranking Model[J]. Journal of Shanghai Jiao Tong University (Science), 2018, 23(4): 568-.

References 16

[1]	HARTIG O, BIZER C, FREYTAG J C. Executing SPARQL queries over the web of Linked Data [C]//Proceedings of the 8th International SemanticWeb Conference. Chantilly, VA, USA: Springer, 2009:293-309.
[2]	LADWIG G, TRAN T. Linked Data query processing strategies [C]//Proceedings of the 9th International SemanticWeb Conference. Shanghai, China: Springer,2010: 453-469.
[3]	HARTIG O. Zero-knowledge query planning for an iterator implementation of link traversal based query execution [C]//Proceedings of the 8th Extended SemanticWeb Conference. Heraklion, Crete, Greece: Springer,2011: 154-169.
[4]	HARTH A, HOSE K, KARNSTEDT M, et al. Datasummaries for on-demand queries over Linked Data[C]//Proceedings of the 19th International Conference on World Wide Web. Raleigh, NC, USA: DBLP, 2010:411-420.
[5]	PHAM M D, BONCZ P. Exploiting emergent schemasto make RDF systems more efficient [C]//Proceedingsof the 15th International Sematic Web Conference.Kobe, Japan: Springer, 2016: 463-479.
[6]	RAHM E, BERNSTEIN P A. A survey of approachesto automatic schema matching [J]. The InternationalJournal on Very Large Data Bases, 2001, 10(4): 334-350.
[7]	DOAN A H, HALEVY A Y. Semantic-integration research in the database community: A brief survey [J].American Association for Artificial Intelligence, 2005,26(1): 83-94.
[8]	DUAN S, FOKOUE A, SRINIVAS K. One size doesnot fit all: Customizing ontology alignment using userfeedback [C]// Proceedings of the 10th InternationalSemantic Web Conference. Shanghai, China: Springer, 2010: 177-192.
[9]	HU W, QU Y Z. Falcon-AO: A practical ontology matching system [J]. Web Semantics: Science, Servicesand Agents on the World Wide Web, 2008, 6(3):237-239.
[10]	ZHOU X, GAUGAZ J, BALKE W T, et al. Query relaxation using malleable schemas [C]//Proceedingsof the 2007 ACM SIGMOD International Conferenceon Management of Data. Beijing, China: ACM, 2007:545-556.
[11]	ELBASSUONI S, RAMANATH M, SCHENKEL R, etal. Language-model-based ranking for queries on RDFgraphs[C]//Proceedings of the 18th ACM Conferenceon International and Knowledge Management. HongKong, China: ACM, 2009: 977-986.
[12]	NEUMAYER R, BALOG K, N?RV?AG K. On themodeling of entities for ad-hoc entity search in the webof data [C]//Proceedings of the 34th European Conferenceon IR Research. Barcelona, Spain: Springer,2012: 133-145.
[13]	HERZIG D M, TRAN T. Heterogeneous web datasearch using relevance-based on the fly data integration[C]//Proceedings of the 21st International Conferenceon World Wide Web. Lyon, France: ACM, 2012: 141-150.
[14]	PONTE J M, CROFT W B. A language modeling approachto information retrieval [C]//Proceedings of the21st Annual International ACM SIGIR Conference onResearch and Development in Information Retrieval.Melbourne, Australia: ACM, 1998: 275-281.
[15]	OGILVIE P, CALLAN J. Hierarchical language modelsfor XML component retrieval [C]//Proceedings ofthe 3rd International Conference on Initiative for theEvaluation of XML Retrieval. Dagstuhl Castle, Germany:Springer, 2004: 224-237.
[16]	BLEI D M, NG A Y, JORAN M I. Latent dirichletallocation [J]. Journal of Machine Learning Research,2003, 3: 993-1022.