Abstract
Most knowledge sources on the Data Web were extracted from structured or semi-structured data. Thus, they encompass solely a small fraction of the information available on the document-oriented Web. In this paper, we present BOA, a bootstrapping strategy for extracting RDF from text. The idea behind BOA is to extract natural-language patterns that represent predicates found on the Data Web from unstructured data by using background knowledge from the Data Web. These patterns are then used to extract instance knowledge from natural-language text. This knowledge is finally fed back into the Data Web, therewith closing the loop. The approach followed by BOA is quasi independent of the language in which the corpus is written. We demonstrate our approach by applying it to four different corpora and two different languages. We evaluate BOA on these data sets using DBpedia as background knowledge. Our results show that we can extract several thousand new facts in one iteration with very high accuracy.
Access provided by Autonomous University of Puebla. Download to read the full chapter text
Chapter PDF
Similar content being viewed by others
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
References
Auer, S., Lehmann, J., Ngonga Ngomo, A.-C.: Introduction to Linked Data and Its Lifecycle on the Web. In: Polleres, A., d’Amato, C., Arenas, M., Handschuh, S., Kroner, P., Ossowski, S., Patel-Schneider, P. (eds.) Reasoning Web 2011. LNCS, vol. 6848, pp. 1–75. Springer, Heidelberg (2011)
Carlson, A., Betteridge, J., Kisiel, B., Settles, B., Hruschka Jr., E.R., Mitchell, T.M.: Toward an architecture for never-ending language learning. In: AAAI (2010)
Fader, A., Soderland, S., Etzioni, O.: Identifying relations for open information extraction. In: EMNLP, pp. 1535–1545. ACL (2011)
Finkel, J.R., Manning, C.D.: Hierarchical joint learning: improving joint parsing and named entity recognition with non-jointly labeled data. In: ACL 2010, pp. 720–728 (2010)
Gaag, A., Kohn, A., Lindemann, U.: Function-based solution retrieval and semantic search in mechanical engineering. In: IDEC 2009, pp. 147–158 (2009)
Gerber, D., Ngonga Ngomo, A.-C.: Bootstrapping the linked data web. In: 1st Workshop on Web Scale Knowledge Extraction ISWC (2011)
Jiang, J.J., Conrath, D.W.: Semantic Similarity Based on Corpus Statistics and Lexical Taxonomy. In: ROCLING X, p. 9008 (September 1997)
Kim, S.N., Medelyan, O., Kan, M.-Y., Baldwin, T.: Semeval-2010 task 5: Automatic keyphrase extraction from scientific articles. In: SemEval 2010 (2010)
Mendes, P.N., Jakob, M., García-Silva, A., Bizer, C.: DBpedia Spotlight: Shedding Light on the Web of Documents. In: I-SEMANTICS, pp. 1–8. ACM (2011)
Mintz, M., Bills, S., Snow, R., Jurafsky, D.: Distant supervision for relation extraction without labeled data. In: ACL, pp. 1003–1011 (2009)
Nakashole, N., Theobald, M., Weikum, G.: Scalable knowledge harvesting with high precision and high recall. In: WSDM, Hong Kong, pp. 227–236 (2011)
Ngonga Ngomo, A.-C., Heino, N., Lyko, K., Speck, R., Kaltenböck, M.: SCMS – Semantifying Content Management Systems. In: Aroyo, L., Welty, C., Alani, H., Taylor, J., Bernstein, A., Kagal, L., Noy, N., Blomqvist, E. (eds.) ISWC 2011, Part II. LNCS, vol. 7032, pp. 189–204. Springer, Heidelberg (2011)
Seco, N., Veale, T., Hayes, J.: An intrinsic information content metric for semantic similarity in WordNet. In: Proc. of ECAI, vol. 4, pp. 1089–1090 (2004)
Unger, C., Bühmann, L., Lehmann, J., Ngonga Ngomo, A.-C., Gerber, D., Cimiano, P.: Sparql template-based question answering. In: Proceedings of WWW (2012)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Gerber, D., Ngomo, AC.N. (2012). Extracting Multilingual Natural-Language Patterns for RDF Predicates. In: ten Teije, A., et al. Knowledge Engineering and Knowledge Management. EKAW 2012. Lecture Notes in Computer Science(), vol 7603. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33876-2_10
Download citation
DOI: https://doi.org/10.1007/978-3-642-33876-2_10
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-33875-5
Online ISBN: 978-3-642-33876-2
eBook Packages: Computer ScienceComputer Science (R0)