Benjamin Großmann
ABSTRACT
Traditional keyword-based IR approaches take into account the document context only in a limited manner. In our paper we present a novel document ranking approach based on the semantic relationships between named entities. In the first step we annotate all documents with named entities from a knowledge base (for example people, places and organisations). In the next step these annotations in combination with the relationships from the knowledge base are used to rank documents in order to perform a semantic search. Documents that contain the specific named entity that was searched for as well as other strongly related entities, receive a higher ranking. The inclusion of the document context in the ranking approach achieves a higher precision in the Top-K results.
- B. Aleman-Meza, I. Arpinar, M. Nural, and A. Sheth. Ranking documents semantically using ontological relationships. In Semantic Computing (ICSC), 2010 IEEE Fourth International Conference on, pages 299--304. IEEE, 2010. Google Scholar
Digital Library
- V. Bush and A. W. M. Think. The atlantic monthly. As we may think, 176(1):101--108, 1945.Google Scholar
- P. Castells, M. Fernández, and D. Vallet. An adaptation of the vector-space model for ontology-based information retrieval. IEEE Transactions on Knowledge and Data Engineering, 19:261--272, 2007. Google ScholarDigital Library
- J. Cohen. Weighted kappa: Nominal scale agreement provision for scaled disagreement or partial credit. Psychological bulletin, 70(4):213, 1968.Google ScholarCross Ref
- S. Kemmerer, B. Großmann, C. Müller, P. Adolphs, and H. Ehrig. The neofonie nerd system at the erd challenge 2014. In Proceedings of the first international workshop on Entity recognition & disambiguation, pages 83--88. ACM, 2014. Google ScholarDigital Library
- A. Kiryakov, B. Popov, I. Terziev, D. Manov, and D. Ognyanoff. Semantic annotation, indexing, and retrieval. In Web Semantics: Science, Services and Agents on the World Wide Web, volume 2, pages 49--79. December 2004. Google ScholarDigital Library
- F. Kuhlmann, J. Hannemann, M. Traub, C. Böhme, S. Zillner, A. Cavallaro, S. Seifert, B. Decker, R. Traphöner, S. Kayser, et al. The theseus use cases. In Towards the Internet of Services: The THESEUS Research Program, pages 259--287. Springer, 2014.Google Scholar
- C. Mangold. A survey and classification of semantic search approaches. IJMSO, 2(1):23--34, 2007. Google ScholarDigital Library
- C. D. Manning, P. Raghavan, and H. Schütze. Introduction to information retrieval, volume 1. Cambridge university press Cambridge, 2008. Google ScholarDigital Library
- C. Ramakrishnan, W. H. Milnor, M. Perry, and A. P. Sheth. Discovering informative connection subgraphs in multi-relational graphs. ACM SIGKDD Explorations Newsletter, 7(2):56--63, 2005. Google ScholarDigital Library
- J. J. Randolph. Free-marginal multirater kappa (multirater k {free}): An alternative to fleiss' fixed-marginal multirater kappa. Online Submission, 2005.Google Scholar
- C. Rocha, D. Schwabe, and M. P. Aragao. A hybrid approach for searching in the semantic web. In Proceedings of the 13th international conference on World Wide Web, pages 374--383. ACM, 2004. Google ScholarDigital Library
- J. Singh and D. A. Sharan. A comparative study between keyword and semantic based search engines. In International Conference on Cloud, Big Data and Trust, pages 13--15, 2013.Google Scholar
- A. Singhal. Modern information retrieval: A brief overview. IEEE Data Eng. Bull., 24(4):35--43, 2001.Google Scholar
- T. Štajner and D. Mladenić. Entity resolution in texts using statistical learning and ontologies. In The Semantic Web, pages 91--104. Springer, 2009. Google ScholarDigital Library
Index Terms
- Improving Semantic Search through Entity-Based Document Ranking
Recommendations
Context-sensitive document ranking
Ranking is a main research issue in IR-styled keyword search over a set of documents. In this paper, we study a new keyword search problem, called context-sensitive document ranking, which is to rank documents with an additional context that provides ...
An Empirical Evaluation on Semantic Search Performance of Keyword-Based and Semantic Search Engines: Google, Yahoo, Msn and Hakia
ICIMP '09: Proceedings of the 2009 Fourth International Conference on Internet Monitoring and ProtectionThis paper investigates the semantic search performance of search engines. Initially, three keyword-based search engines (Google, Yahoo and Msn) and a semantic search engine (Hakia) were selected. Then, ten queries, from various topics, and four phrases,...
Effective semantic search using thematic similarity
Most existing semantic search systems expand search keywords using domain ontology to deal with semantic heterogeneity. They focus on matching the semantic similarity of individual keywords in a multiple-keywords query; however, they ignore the semantic ...
Comments