PmSPARQL: Extended SPARQL for Multi-paradigm Path Extraction
In the last few years, the Semantic Web gained scientific acceptance as a means of relationships identification in knowledge base, widely known by semantic association. Query about complex relationships between entities is a strong requirement for many applications in analytical domains. In bioinformatics for example, it is critical to extract exchanges between proteins. Currently, the widely known result of such queries is to provide paths between connected entities from data graph. However, they do not always give good results while facing the user need by the best association or a set of limited best association, because they only consider all existing paths but ignore the path evaluation. In this paper, we present an approach for supporting association discovery queries. Our proposal includes (i) a query language PmSPRQL which provides a multiparadigm query expressions for association extraction and (ii) some quantification measures making easy the process of association ranking. The originality of our proposal is demonstrated by a performance evaluation of our approach on real world datasets.
[1] B. Aleman-Meza, C. Halaschek, B. Arpinar, and A. Sheth, Contextaware
semantic association ranking. In First International Workshop
on Semantic Web and Databases,in First International Workshop on
Semantic Web and Databases, Berlin, Germany, September 2003, p.
33-50.
[2] K. Anyanwu, A. Maduko, and A. Sheth, Sem-rank: Ranking complex
relationship search results on the semantic web, in International World
Wide Web Conference, 14, ACM, Chiba, Japan, 2005, p. 117-127.
[3] B. Aleman-Meza, C. Halaschek-Wiener, I. Budak Arpinar, C. Ramakrishnan,
and A. Sheth, Ranking complex relationships on the semantic
web, in IEEE Internet Computing, 03, 2005, p. 37-44.
[4] B. Aleman-Meza, P. Burns, M. Eavenson, D. Palaniswami, and A. Sheth,
An ontological approach to the document access problem of insider
threat. IEEE International Conference on Intelligence and Security
Informatics. Atlanta, Georgia, USA, 2005, p. 486-491.
[5] K. Anyanwu and A. Sheth, The rho operator: Computing and ranking
semantic associations in the semantic web. SIGMOD Record, 2002.
[6] A. Seaborne, RDQL A Query Language for RDF, WWWConsortium,
Member Submission SUBM-RDQL-20040109, 2004.
[7] E. Prud-hommeaux and A. Seaborne, SPARQL:Query Language for
RDF, 2005.
[8] A. Kemafor, M. Angela, and S. Amit, SPARQ2L: Towards support for
subgraph extraction queries in rdf databases, in WWW 2007, Banff,
Alberta, Canada, 2007, p. 797- 806.
[9] J. Krys and Maciej.J, SARQLeR: Extended sparql for semantic association
discovery, in 4 th European Semantic Web Conference. Innsbruck,
Austria, 2007.
[10] A. Helenius and M. Aebi, Roles of n-linked glycans in the endoplasmic
reticulum, in Annual Review of Biochemistry, 73 , 2004, p. 1019-1049.
[11] H. Donninger, T. Bonome, M. Radonovich, Pise-Masison, C. A., J. H.
Brady, J.and Shih, J. Barrett, and M. J. Birrer, Whole genome expression
profiling of advance stage papillary serous ovarian cancer reveals
activated pathways. Oncogene 23, 8065, 8077 (2004).
[12] T. Miki, S. Nomura, and T. Ishida, Semantic web link analysis to
discover social relationships in academic communities. Symposium on
Applications and the Internet, 2005.
[13] A. Sheth, B. Aleman-Meza, I. Arpinar1, C. Halaschek, C. Ramakrishnan1,
C. Bertram, Y. Warke, D. Avant, F. S. Arpinar, K. Anyanwu,
and K. K., Semantic association identification and knowledge discovery
for national security applications. Special Issue of Journal of Database
Management on Database Technology for Enhancing National Security,
L. Zhou and W. Kim (Eds.) 16, 33-53 (2005).
[14] S. Mukherjea and B. Bamba, Biopatentminer: An information retrieval
system for biomedical patents. Thirtieth International Conference on
Very Large Data Bases. VLDB, Toronto, Canada, 2004, p. 1066-1077.
[15] I. Arpinar, A. Sheth, C. Ramakrishnan, E. Usery, M. Azami, and M.
Kwan, Geospatial ontology development and semantic analytics, in
Handbook of Geographic Information Science., 4, edited by J. P. Wilson
and A. S. F. E. vol 10. Blackwell Publishing, 2004.
[16] S. Lin and H. Chalupsky, Unsupervised link discovery in multirelational
data via rarity analysis. ICDM 2003, 2003, p. 171-178.
[17] M. Janik and K. Kochut, A work-bench rdf store and high performance
memory system for semantic association discovery. 4th International
Semantic Web Conference. Galway, Ireland, 2005.
[18] W. Milnor, C. Ramakrishnan, M. Perry, A. Sheth, J. Miller, and K.
Kochut, Discovering informative subgraphs in rdf graphs. Technical report,
LSDIS Lab, Computer Science,University of Georgia, CS Technical
Report 05-001.
[19] V. Paliwal, N. R. Adam, H. Xiong, and C. Bornhovd, Web service
discovery via semantic association ranking and hyperclique pattern
discovery, in wi, IEEE/WIC/ACM ,IEEE Computer Society, 2006, p.
649-652.
[20] H.-J. Chu and R. Chow, Reaching semantic interoperability through
semantic association of domain standards, in 11th IEEE International
Workshop on Future Trends of Distributed Computing Systems (FTDCS07),
ISSN:1701-0483, 0-7695-2810-4, IEEE Computer Society,
Washington, DC, USA, 2007.
[21] I. Cruz, A. Mendelzon, and P. Wood, A graphical query language
supporting recursion. in acm sigmod international conference on management
of data, in ACM SIGMOD International Conference on Management
of Data, San Francisco, California, United States, 1987, p. 323-
330.
[22] I. Cruz, A. Mendelzon, and P. Wood, G+: Recursive queries without
recursion. 2nd International Conference on Expert Database Systems,
1988, p. 355-368.
[23] M. Consens and A. Mendelzon, Graphlog: a visual formalism for real
life recursion. ACM Symposium On Principles of Database Systems.
1990, p. 404-416.
[24] J. Broekstra and A. Kampman, SERQL: A second generation rdf query
language. In SWAD-Europe Workshop on Semantic Web Storage and
Retrieval. SWAD-Europe Workshop on Semantic Web Storage and
Retrieval, 2003.
[25] M. Sintek and S. Decker, Triple - an rdf query, inference, and transformation
language. In Deductive Databases and Knowledge Management.
Tokyo, Japan, 2001.
[26] U. Ogbuji, RDF Query using Versa Thinking XML: Basic XML and RDF
techniques for knowledge management, Part 6, 10 April 2002.
[27] A.Souzis, RxPath specification proposal. http://rx4rdf. liminalzone.
org/RxPathSpec., 2004.
[28] L. Sam, L. yang, L. Jianrong, C. Friedman, and Y. Lussier, Triple -
an rdf query, inference, and transformation language. In 12me Pacific
Symposium on Biocomputing. 2007, p. 76-87.
[29] T. Samir and I. Budak Arpinar, Ontology evaluation and ranking
using ontoqa. The first IEEE International Conference on Semantic
Computing. Irvine, California, USA, September 17-19, 2007, p. 185-
192.
[30] C. Gutierrez, C. Hurtado, and A. Mendelzon., Foundations of Semantic
Web Databases. Foundations of Semantic Web Databases. In PODS
2004, p. 95106., 2004.
[31] D. Marin, Rdf formalization. Technical report, Santiago de Chile.
TR/DCC-2006-8. http://www.dcc.uchile.cl/ cgutierr/ftp/draltan.pdf.
[32] P. Jorge, A. Marcelo, and G. Claudio, Semantics and complexity of
sparql. International Semantic Web Conference. Athens, GA, US, 2006.
[33] J. Lim, ADOdb Library for PHP, http://php.weblogs.com/ADODB.,
2007.
[1] B. Aleman-Meza, C. Halaschek, B. Arpinar, and A. Sheth, Contextaware
semantic association ranking. In First International Workshop
on Semantic Web and Databases,in First International Workshop on
Semantic Web and Databases, Berlin, Germany, September 2003, p.
33-50.
[2] K. Anyanwu, A. Maduko, and A. Sheth, Sem-rank: Ranking complex
relationship search results on the semantic web, in International World
Wide Web Conference, 14, ACM, Chiba, Japan, 2005, p. 117-127.
[3] B. Aleman-Meza, C. Halaschek-Wiener, I. Budak Arpinar, C. Ramakrishnan,
and A. Sheth, Ranking complex relationships on the semantic
web, in IEEE Internet Computing, 03, 2005, p. 37-44.
[4] B. Aleman-Meza, P. Burns, M. Eavenson, D. Palaniswami, and A. Sheth,
An ontological approach to the document access problem of insider
threat. IEEE International Conference on Intelligence and Security
Informatics. Atlanta, Georgia, USA, 2005, p. 486-491.
[5] K. Anyanwu and A. Sheth, The rho operator: Computing and ranking
semantic associations in the semantic web. SIGMOD Record, 2002.
[6] A. Seaborne, RDQL A Query Language for RDF, WWWConsortium,
Member Submission SUBM-RDQL-20040109, 2004.
[7] E. Prud-hommeaux and A. Seaborne, SPARQL:Query Language for
RDF, 2005.
[8] A. Kemafor, M. Angela, and S. Amit, SPARQ2L: Towards support for
subgraph extraction queries in rdf databases, in WWW 2007, Banff,
Alberta, Canada, 2007, p. 797- 806.
[9] J. Krys and Maciej.J, SARQLeR: Extended sparql for semantic association
discovery, in 4 th European Semantic Web Conference. Innsbruck,
Austria, 2007.
[10] A. Helenius and M. Aebi, Roles of n-linked glycans in the endoplasmic
reticulum, in Annual Review of Biochemistry, 73 , 2004, p. 1019-1049.
[11] H. Donninger, T. Bonome, M. Radonovich, Pise-Masison, C. A., J. H.
Brady, J.and Shih, J. Barrett, and M. J. Birrer, Whole genome expression
profiling of advance stage papillary serous ovarian cancer reveals
activated pathways. Oncogene 23, 8065, 8077 (2004).
[12] T. Miki, S. Nomura, and T. Ishida, Semantic web link analysis to
discover social relationships in academic communities. Symposium on
Applications and the Internet, 2005.
[13] A. Sheth, B. Aleman-Meza, I. Arpinar1, C. Halaschek, C. Ramakrishnan1,
C. Bertram, Y. Warke, D. Avant, F. S. Arpinar, K. Anyanwu,
and K. K., Semantic association identification and knowledge discovery
for national security applications. Special Issue of Journal of Database
Management on Database Technology for Enhancing National Security,
L. Zhou and W. Kim (Eds.) 16, 33-53 (2005).
[14] S. Mukherjea and B. Bamba, Biopatentminer: An information retrieval
system for biomedical patents. Thirtieth International Conference on
Very Large Data Bases. VLDB, Toronto, Canada, 2004, p. 1066-1077.
[15] I. Arpinar, A. Sheth, C. Ramakrishnan, E. Usery, M. Azami, and M.
Kwan, Geospatial ontology development and semantic analytics, in
Handbook of Geographic Information Science., 4, edited by J. P. Wilson
and A. S. F. E. vol 10. Blackwell Publishing, 2004.
[16] S. Lin and H. Chalupsky, Unsupervised link discovery in multirelational
data via rarity analysis. ICDM 2003, 2003, p. 171-178.
[17] M. Janik and K. Kochut, A work-bench rdf store and high performance
memory system for semantic association discovery. 4th International
Semantic Web Conference. Galway, Ireland, 2005.
[18] W. Milnor, C. Ramakrishnan, M. Perry, A. Sheth, J. Miller, and K.
Kochut, Discovering informative subgraphs in rdf graphs. Technical report,
LSDIS Lab, Computer Science,University of Georgia, CS Technical
Report 05-001.
[19] V. Paliwal, N. R. Adam, H. Xiong, and C. Bornhovd, Web service
discovery via semantic association ranking and hyperclique pattern
discovery, in wi, IEEE/WIC/ACM ,IEEE Computer Society, 2006, p.
649-652.
[20] H.-J. Chu and R. Chow, Reaching semantic interoperability through
semantic association of domain standards, in 11th IEEE International
Workshop on Future Trends of Distributed Computing Systems (FTDCS07),
ISSN:1701-0483, 0-7695-2810-4, IEEE Computer Society,
Washington, DC, USA, 2007.
[21] I. Cruz, A. Mendelzon, and P. Wood, A graphical query language
supporting recursion. in acm sigmod international conference on management
of data, in ACM SIGMOD International Conference on Management
of Data, San Francisco, California, United States, 1987, p. 323-
330.
[22] I. Cruz, A. Mendelzon, and P. Wood, G+: Recursive queries without
recursion. 2nd International Conference on Expert Database Systems,
1988, p. 355-368.
[23] M. Consens and A. Mendelzon, Graphlog: a visual formalism for real
life recursion. ACM Symposium On Principles of Database Systems.
1990, p. 404-416.
[24] J. Broekstra and A. Kampman, SERQL: A second generation rdf query
language. In SWAD-Europe Workshop on Semantic Web Storage and
Retrieval. SWAD-Europe Workshop on Semantic Web Storage and
Retrieval, 2003.
[25] M. Sintek and S. Decker, Triple - an rdf query, inference, and transformation
language. In Deductive Databases and Knowledge Management.
Tokyo, Japan, 2001.
[26] U. Ogbuji, RDF Query using Versa Thinking XML: Basic XML and RDF
techniques for knowledge management, Part 6, 10 April 2002.
[27] A.Souzis, RxPath specification proposal. http://rx4rdf. liminalzone.
org/RxPathSpec., 2004.
[28] L. Sam, L. yang, L. Jianrong, C. Friedman, and Y. Lussier, Triple -
an rdf query, inference, and transformation language. In 12me Pacific
Symposium on Biocomputing. 2007, p. 76-87.
[29] T. Samir and I. Budak Arpinar, Ontology evaluation and ranking
using ontoqa. The first IEEE International Conference on Semantic
Computing. Irvine, California, USA, September 17-19, 2007, p. 185-
192.
[30] C. Gutierrez, C. Hurtado, and A. Mendelzon., Foundations of Semantic
Web Databases. Foundations of Semantic Web Databases. In PODS
2004, p. 95106., 2004.
[31] D. Marin, Rdf formalization. Technical report, Santiago de Chile.
TR/DCC-2006-8. http://www.dcc.uchile.cl/ cgutierr/ftp/draltan.pdf.
[32] P. Jorge, A. Marcelo, and G. Claudio, Semantics and complexity of
sparql. International Semantic Web Conference. Athens, GA, US, 2006.
[33] J. Lim, ADOdb Library for PHP, http://php.weblogs.com/ADODB.,
2007.
@article{"International Journal of Information, Control and Computer Sciences:57326", author = "Thabet Slimani and Boutheina Ben Yaghlane and Khaled Mellouli", title = "PmSPARQL: Extended SPARQL for Multi-paradigm Path Extraction", abstract = "In the last few years, the Semantic Web gained scientific acceptance as a means of relationships identification in knowledge base, widely known by semantic association. Query about complex relationships between entities is a strong requirement for many applications in analytical domains. In bioinformatics for example, it is critical to extract exchanges between proteins. Currently, the widely known result of such queries is to provide paths between connected entities from data graph. However, they do not always give good results while facing the user need by the best association or a set of limited best association, because they only consider all existing paths but ignore the path evaluation. In this paper, we present an approach for supporting association discovery queries. Our proposal includes (i) a query language PmSPRQL which provides a multiparadigm query expressions for association extraction and (ii) some quantification measures making easy the process of association ranking. The originality of our proposal is demonstrated by a performance evaluation of our approach on real world datasets.
", keywords = "Association extraction, query Language, relationships, knowledge base, multi-paradigm query.", volume = "2", number = "2", pages = "446-12", }
