Revisão Sistemática da Literatura sobre ranking de Relacionamentos na Web Semântica
Resumo
O ato de realizar pesquisas na Web tem sido o mesmo por anos. O usuário realiza uma consulta composta de termos, e o motor de busca é responsável por encontrar as melhores respostas àquela consulta. Frequentemente, existem informações subjetivas que o usuário n˜ao consegue transmitir em sua consulta, mas que ele espera que o motor de busca seja capaz de inferir. Isso leva a resultados que s˜ao relacionados à sua consulta, mas n˜ao aos seus interesses. Uma forma de mitigar esse problema foi a introdução da Web Semântica, que visa a permitir que os dados disponíveis na Web tenham um sentido, ou seja, uma semântica. Diversas abordagens de busca na Web Semântica têm sido propostas e implementadas nos últimos anos, bem como abordagens para classificação (ranking) de resultados. Esta revis˜ao sistemática da literatura tem por objetivo identificar as tendências na área de ranking de relacionamentos na Web Semântica. De um total de 1.194 artigos inicialmente retornados em nossa pesquisa, foram selecionados e analisados 10 estudos primários nesse tipo de ranking, dando-se especial atenção às características das técnicas adotadas e aos experimentos realizados. Observou-se ent˜ao que novas soluções promissoras envolvem o uso de algoritmos de aprendizado de máquina para realizar o ranking dos resultados das consultas.
Palavras-chave:
Web Semantica, Dados vinculados, Ranking de relações
Referências
B. Aleman-Meza, I. B. Arpinar, M. V. Nural, and A. P. Sheth. Ranking documents semantically using ontological relationships. In Proceedings of the 2010 IEEE Fourth International Conference on Semantic Computing, pages 299–304. IEEE, 2010.
B. Aleman-Meza, C. Halaschek-Wiener, A. Sheth, I. B. Arpinar, and C. Ramakrishnan. Ranking complex relationships on the semantic web. IEEE Internet Computing, pages 37–44, 2005.
K. Anyanwu, A. Maduko, and A. Sheth. Semrank: Ranking complex relation search results on the semantic web. In 14th International Conference on World Wide Web, pages 117–127, 2005.
K. Anyanwu and A. Sheth. ⇢-queries: Enabling querying for semantic associations on the semantic web. In Proceedings of the 12th International Conference on World Wide Web, pages 690–699, 2003.
T. Bernes-Lee. Linked data - design issues. http://www.w3.org/DesignIssues/LinkedData.html, 2006. Acessado em 31-01-2017.
S. Bhatia, A. Goel, E. Bowen, and A. Jain. Separating wheat from the cha↵ – a relationship ranking algorithm. The Semantic Web: ESWC 2016 Satellite Events, pages 79–83, 2016.
P. Castells, M. Fernandez, and D. Vallet. An adaptation of the vector-space model for ontology-based information retrieval. IEEE transactions on knowledge and data engineering, 19(2):261–272, 2007.
N. Chen and V. Prasanna. Rankbox: An adaptive ranking system for mining complex semantic relationships using user feedback. In Proceedings of the IEEE 13th International Conference on Information Reuse and Integration, pages 77–84, 2012.
N. Chen and V. K. Prasanna. Learning to rank complex semantic relationships. International Journal on Semantic Web and Information Systems, 8(4):1–19, Oct. 2012.
J. Ho↵art, S. Seufert, D. B. Nguyen, M. Theobald, and G. Weikum. Kore: Keyphrase overlap relatedness for entity disambiguation. In Proceedings of the 21st ACM International Conference on Information and Knowledge Management, pages 545–554. ACM, 2012.
A. Hogan, S. Decker, and A. Harth. Reconrank: A scalable ranking method for semantic web data with context. In Proceedings of second international workshop on scalable semantic web knowledge base systems, 2006.
V. Jindal, S. Bawa, and S. Batra. A review of ranking approaches for semantic search on web. Information Processing and Management, 50(146):416–425, 2014.
S. A. Kareem and P. M. Barnaghi. A context-aware ranking method for the complex relationships on the semantic web. In International Conference on Advanced Language Processing and Web Information Technology, pages 129–134. IEEE, 2007.
B. A. Kitchenham. Systematic review in software engineering: Where we are and where we should be going. In Proceedings of the 2Nd International Workshop on Evidential Assessment of Software Technologies, pages 1–2. ACM, 2012.
K. J. Kochut and M. Janik. Sparqler: Extended sparql for semantic association discovery. In E. Franconi, M. Kifer, and W. May, editors, Proceeding of the 4th European Semantic Web Conference, pages 145–159, 2007.
F. Lamberti, A. Sanna, and C. Demartini. A relation-based page rank algorithm for semantic web search engines. IEEE Transactions on Knowledge and Data Engineering, 21(1):123–136, 2009.
F. Lamberti, A. Sanna, and C. Demartini. A relation-based page rank algorithm for semantic web search engines. IEEE Transactions on Knowledge and Data Engineering, 21(1):123–136, 2009.
M. Lee and W. Kim. Semantic association search and rank method based on spreading activation for the semantic web. In 2009 IEEE International Conference on Industrial Engineering and Engineering Management, pages 1523–1527, 2009.
X. Ning, H. Jin, and H. Wu. Rss: A framework enabling ranked search on the semantic web. Information Processing & Management, 44(2):893 – 909, 2008.
A. J. Roa-Valverde and M.-A. Sicilia. A survey of approaches for ranking on the web of data. Information Retrieval, 17(4):295–325, 2014.
A. K. Thushar. An RDF approach for discovering the relevant semantic associations in a social network. In 16th International Conference on Advanced Computing and Communications, pages 214–220, 2008.
V.Derhami, J.Paksima, and H. Khajeh. Web pages ranking algorithm based on reinforcement learning and user feedback. Journal of AI and Data Mining, 3(2):157–168, 2015.
M.-e. Vidal, L. Rashid, L. Ibabez, J. Rivera, H. Rodrogiez, and E. Ruckhaus. A ranking-based approach to discover semantic association between linked data. In The 2nd international workshop on inductive reasoning and machine learning for the semantic web, volume 611, pages 18–29, 2010.
V. Viswanathan and K. Ilango. Ranking semantic relationships between two entities using personalization in context specification. Information Sciences, 207:35–49, 2012.
V. Viswanathan and I. Krishnamurthi. Ranking semantic associations between two entities – extended model. Intelligent Information and Database Systems: 4th Asian Conference, ACIIDS 2012, Kaohsiung, Taiwan, pages 152–162, 2012.
W. Wei, P. Barnaghi, and A. Bargiela. Rational research model for ranking semantic entities. Information Sciences, 181(13):2823 – 2840, 2011.
G. Wu, J. Li, L. Feng, and K. Wang. Identifying potentially important concepts and relations in an ontology. In Proceedings of the 7th International Semantic Web Conference, pages 33–49, 2008.
S. Yumusak, E. Dogdu, and H. Kodaz. A short survey of linked data ranking. In Proceedings of the 2014 ACM Southeast Regional Conference, pages 48:1–48:4. ACM, 2014.
B. Aleman-Meza, C. Halaschek-Wiener, A. Sheth, I. B. Arpinar, and C. Ramakrishnan. Ranking complex relationships on the semantic web. IEEE Internet Computing, pages 37–44, 2005.
K. Anyanwu, A. Maduko, and A. Sheth. Semrank: Ranking complex relation search results on the semantic web. In 14th International Conference on World Wide Web, pages 117–127, 2005.
K. Anyanwu and A. Sheth. ⇢-queries: Enabling querying for semantic associations on the semantic web. In Proceedings of the 12th International Conference on World Wide Web, pages 690–699, 2003.
T. Bernes-Lee. Linked data - design issues. http://www.w3.org/DesignIssues/LinkedData.html, 2006. Acessado em 31-01-2017.
S. Bhatia, A. Goel, E. Bowen, and A. Jain. Separating wheat from the cha↵ – a relationship ranking algorithm. The Semantic Web: ESWC 2016 Satellite Events, pages 79–83, 2016.
P. Castells, M. Fernandez, and D. Vallet. An adaptation of the vector-space model for ontology-based information retrieval. IEEE transactions on knowledge and data engineering, 19(2):261–272, 2007.
N. Chen and V. Prasanna. Rankbox: An adaptive ranking system for mining complex semantic relationships using user feedback. In Proceedings of the IEEE 13th International Conference on Information Reuse and Integration, pages 77–84, 2012.
N. Chen and V. K. Prasanna. Learning to rank complex semantic relationships. International Journal on Semantic Web and Information Systems, 8(4):1–19, Oct. 2012.
J. Ho↵art, S. Seufert, D. B. Nguyen, M. Theobald, and G. Weikum. Kore: Keyphrase overlap relatedness for entity disambiguation. In Proceedings of the 21st ACM International Conference on Information and Knowledge Management, pages 545–554. ACM, 2012.
A. Hogan, S. Decker, and A. Harth. Reconrank: A scalable ranking method for semantic web data with context. In Proceedings of second international workshop on scalable semantic web knowledge base systems, 2006.
V. Jindal, S. Bawa, and S. Batra. A review of ranking approaches for semantic search on web. Information Processing and Management, 50(146):416–425, 2014.
S. A. Kareem and P. M. Barnaghi. A context-aware ranking method for the complex relationships on the semantic web. In International Conference on Advanced Language Processing and Web Information Technology, pages 129–134. IEEE, 2007.
B. A. Kitchenham. Systematic review in software engineering: Where we are and where we should be going. In Proceedings of the 2Nd International Workshop on Evidential Assessment of Software Technologies, pages 1–2. ACM, 2012.
K. J. Kochut and M. Janik. Sparqler: Extended sparql for semantic association discovery. In E. Franconi, M. Kifer, and W. May, editors, Proceeding of the 4th European Semantic Web Conference, pages 145–159, 2007.
F. Lamberti, A. Sanna, and C. Demartini. A relation-based page rank algorithm for semantic web search engines. IEEE Transactions on Knowledge and Data Engineering, 21(1):123–136, 2009.
F. Lamberti, A. Sanna, and C. Demartini. A relation-based page rank algorithm for semantic web search engines. IEEE Transactions on Knowledge and Data Engineering, 21(1):123–136, 2009.
M. Lee and W. Kim. Semantic association search and rank method based on spreading activation for the semantic web. In 2009 IEEE International Conference on Industrial Engineering and Engineering Management, pages 1523–1527, 2009.
X. Ning, H. Jin, and H. Wu. Rss: A framework enabling ranked search on the semantic web. Information Processing & Management, 44(2):893 – 909, 2008.
A. J. Roa-Valverde and M.-A. Sicilia. A survey of approaches for ranking on the web of data. Information Retrieval, 17(4):295–325, 2014.
A. K. Thushar. An RDF approach for discovering the relevant semantic associations in a social network. In 16th International Conference on Advanced Computing and Communications, pages 214–220, 2008.
V.Derhami, J.Paksima, and H. Khajeh. Web pages ranking algorithm based on reinforcement learning and user feedback. Journal of AI and Data Mining, 3(2):157–168, 2015.
M.-e. Vidal, L. Rashid, L. Ibabez, J. Rivera, H. Rodrogiez, and E. Ruckhaus. A ranking-based approach to discover semantic association between linked data. In The 2nd international workshop on inductive reasoning and machine learning for the semantic web, volume 611, pages 18–29, 2010.
V. Viswanathan and K. Ilango. Ranking semantic relationships between two entities using personalization in context specification. Information Sciences, 207:35–49, 2012.
V. Viswanathan and I. Krishnamurthi. Ranking semantic associations between two entities – extended model. Intelligent Information and Database Systems: 4th Asian Conference, ACIIDS 2012, Kaohsiung, Taiwan, pages 152–162, 2012.
W. Wei, P. Barnaghi, and A. Bargiela. Rational research model for ranking semantic entities. Information Sciences, 181(13):2823 – 2840, 2011.
G. Wu, J. Li, L. Feng, and K. Wang. Identifying potentially important concepts and relations in an ontology. In Proceedings of the 7th International Semantic Web Conference, pages 33–49, 2008.
S. Yumusak, E. Dogdu, and H. Kodaz. A short survey of linked data ranking. In Proceedings of the 2014 ACM Southeast Regional Conference, pages 48:1–48:4. ACM, 2014.
Publicado
17/05/2017
Como Citar
SANTOS, Paulo; DELGADO, Karina; LAURETTO, Marcelo; RIBEIRO, Marcio.
Revisão Sistemática da Literatura sobre ranking de Relacionamentos na Web Semântica. In: SIMPÓSIO BRASILEIRO DE SISTEMAS DE INFORMAÇÃO (SBSI), 13. , 2017, Lavras.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2017
.
p. 32-39.
DOI: https://doi.org/10.5753/sbsi.2017.6023.
