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Detecting Similar Linked Datasets Using Topic Modelling Read chapter Read first chapter

2016 | OriginalPaper | Chapter

Knowledge Extraction for Information Retrieval

Authors : Francesco Corcoglioniti , Mauro Dragoni, Marco Rospocher, Alessio Palmero Aprosio

Published in: The Semantic Web. Latest Advances and New Domains

Publisher: Springer International Publishing

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Abstract

Document retrieval is the task of returning relevant textual resources for a given user query. In this paper, we investigate whether the semantic analysis of the query and the documents, obtained exploiting state-of-the-art Natural Language Processing techniques (e.g., Entity Linking, Frame Detection) and Semantic Web resources (e.g., YAGO, DBpedia), can improve the performances of the traditional term-based similarity approach. Our experiments, conducted on a recently released document collection, show that Mean Average Precision (MAP) increases of 3.5 % points when combining textual and semantic analysis, thus suggesting that semantic content can effectively improve the performances of Information Retrieval systems.

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previous chapter AskNow: A Framework for Natural Language Query Formalization in SPARQL
next chapter Efficient Graph-Based Document Similarity
Footnotes
6
We report in this section the main NLP/KE tasks for the extraction of semantic terms. Some of them typically build on additional NLP analyses, such as Tokenization, Part-of-Speech tagging, Dependency Parsing and Constituency Parsing.
 
7
Given the lack of normalization in sim(d, q), our scheme can be roughly classified as ltn.ntn using the SMART notation; see http://​bit.​ly/​weighting_​schemes [22].
 
9
Subset of FrameBase ontology used in PIKES. Mapping-based properties with xsd:date, xsd:dateTime, xsd:gYear, and xsd:gYearMonth objects, YAGO types and type hierarchy from DBpedia 2015-04. All data available on KE4IR website.
 
16
To give an idea, the impact of each semantic layer on the whole processing time for the document collection of Sect. 5 is: uri (3.5 %), type (16.3 %), time (2.9 %), frame (77.3 %). Note also that substantial improvements of KE4IR indexing throughput can be achieved with further engineering and optimization, out-of-scope here.
 
17
For comparison, on KE4IR website we make available for download an instance of SOLR (a popular search engine based on Lucene) indexing the same document collection used in our evaluation, and we report on its performances on the test queries.
 
Literature
1.
Gangemi, A., Draicchio, F., Presutti, V., Nuzzolese, A.G., Recupero, D.R.: A machine reader for the semantic web. In: Demos of ISWC, pp. 149–152 (2013)
2.
Rospocher, M., van Erp, M., Vossen, P., Fokkens, A., Aldabe, I., Rigau, G., Soroa, A., Ploeger, T., Bogaard, T.: Building event-centric knowledge graphs from news. J. Web Semant. (to appear)
3.
Corcoglioniti, F., Rospocher, M., Palmero Aprosio, A.: A 2-phase frame-based knowledge extraction framework. In: Proceedings of ACM Symposium on Applied Computing (SAC 2016) (2016, to appear)
4.
Lehmann, J., Isele, R., Jakob, M., Jentzsch, A., Kontokostas, D., Mendes, P.N., Hellmann, S., Morsey, M., van Kleef, P., Auer, S., Bizer, C.: DBpedia - a large-scale, multilingual knowledge base extracted from Wikipedia. Semantic Web 6(2), 167–195 (2015)
5.
Salton, G., Wong, A., Yang, C.S.: A vector space model for automatic indexing. Commun. ACM 18(11), 613–620 (1975)CrossRefMATH
6.
Waitelonis, J., Exeler, C., Sack, H.: Linked data enabled generalized vector space model to improve document retrieval. In: Proceedings of NLP & DBpedia 2015 Workshop in Conjunction with 14th International Semantic Web Conference (ISWC 2015). CEUR Workshop Proceedings (2015)
7.
Croft, W.B.: User-specified domain knowledge for document retrieval. In: Bernardi, L.R., Rabitti, F. (eds.) SIGIR, pp. 201–206. ACM (1986)
8.
Gonzalo, J., Verdejo, F., Chugur, I., Cigarrán, J.: Indexing with WordNet synsets can improve text retrieval. CoRR (1998)
9.
Fellbaum, C. (ed.): WordNet: An Electonic Lexical Database. MIT Press, Cambridge (1998)MATH
10.
Dridi, O.: Ontology-based information retrieval: overview and new proposition. In: RCIS, pp. 421–426 (2008)
11.
Tomassen, S.L.: Research on ontology-driven information retrieval. In: Meersman, R., Tari, Z., Herrero, P. (eds.) OTM 2006 Workshops. LNCS, vol. 4278, pp. 1460–1468. Springer, Heidelberg (2006)CrossRef
12.
Castells, P., Fernández, M., Vallet, D.: An adaptation of the vector-space model for ontology-based information retrieval. IEEE Trans. Knowl. Data Eng. 19(2), 261–272 (2007)CrossRef
13.
Vallet, D., Fernández, M., Castells, P.: An ontology-based information retrieval model. In: Gómez-Pérez, A., Euzenat, J. (eds.) ESWC 2005. LNCS, vol. 3532, pp. 455–470. Springer, Heidelberg (2005)CrossRef
14.
Jimeno-Yepes, A., Llavori, R.B., Rebholz-Schuhmann, D.: Ontology refinement for improved information retrieval. Inf. Process. Manage. 46(4), 426–435 (2010)CrossRef
15.
Fernández, M., Cantador, I., Lopez, V., Vallet, D., Castells, P., Motta, E.: Semantically enhanced information retrieval: an ontology-based approach. J. Web Sem. 9(4), 434–452 (2011)CrossRef
16.
Spink, A., Jansen, B., Blakely, C., Koshman, S.: A study of results overlap and uniqueness among major web search engines. Inf. Process. Manage. 42(5), 1379–1391 (2006)CrossRef
17.
Stojanovic, N.: An approach for defining relevance in the ontology-based information retrieval. In: Web Intelligence, pp. 359–365 (2005)
18.
Baziz, M., Boughanem, M., Pasi, G., Prade, H.: An information retrieval driven by ontology: from query to document expansion. In: RIAO (2007)
19.
Rouces, J., de Melo, G., Hose, K.: FrameBase: representing n-ary relations using semantic frames. In: Gandon, F., Sabou, M., Sack, H., d’Amato, C., Cudré-Mauroux, P., Zimmermann, A. (eds.) ESWC 2015. LNCS, vol. 9088, pp. 505–521. Springer, Heidelberg (2015)CrossRef
20.
Hoffart, J., Suchanek, F.M., Berberich, K., Weikum, G.: YAGO2: a spatially and temporally enhanced knowledge base from Wikipedia. Artif. Intell. 194, 28–61 (2013)MathSciNetCrossRefMATH
21.
da Costa Pereira, C., Dragoni, M., Pasi, G.: Multidimensional relevance: prioritized aggregation in a personalized information retrieval setting. Inf. Process. Manage. 48(2), 340–357 (2012)CrossRef
22.
Manning, C.D., Raghavan, P., Schütze, H., et al.: Introduction to Information Retrieval, vol. 1. Cambridge University Press, Cambridge (2008)CrossRefMATH
23.
Corcoglioniti, F., Rospocher, M., Mostarda, M., Amadori, M.: Processing billions of RDF triples on a single machine using streaming and sorting. In: ACM SAC, pp. 368–375 (2015)
24.
Voorhees, E., Harman, D.: Overview of the sixth text retrieval conference (trec-6). In: TREC, pp. 1–24 (1997)
25.
Järvelin, K., Kekäläinen, J.: Cumulated gain-based evaluation of IR techniques. ACM Trans. Inf. Syst. 20(4), 422–446 (2002)CrossRef
26.
Sanderson, M., Zobel, J.: Information retrieval system evaluation: effort, sensitivity, and reliability. In: SIGIR, pp. 162–169. ACM (2005)
27.
Noreen, E.W.: Computer-Intensive Methods for Testing Hypotheses: An Introduction. Wiley, New York (1989)
28.
Abdelali, A., Cowie, J., Soliman, H.: Improving query precision using semantic expansion. Inf. Process. Manage. 43(3), 705–716 (2007)CrossRef
Metadata
Title
Knowledge Extraction for Information Retrieval
Authors
Francesco Corcoglioniti
Mauro Dragoni
Marco Rospocher
Alessio Palmero Aprosio
Copyright Year
2016
Book
The Semantic Web. Latest Advances and New Domains

Print ISBN: 978-3-319-34128-6

Electronic ISBN: 978-3-319-34129-3

Copyright Year: 2016

DOI
https://doi.org/10.1007/978-3-319-34129-3_20