Abstract
Deriving knowledge from information stored in unstructured documents is a major challenge. More specifically, binary relationships representing facts between entities can be extracted to populate semantic triple stores or large knowledge bases. The main constraint of all knowledge extraction approaches is to find a trade-off between quality and scalability. Thus, we propose in this paper SPIDER, a novel integrated system for extracting binary relationships at large scale. Through series of experiments, we show the benefit of our approach, which in general, outperforms existing systems both in terms of quality (precision and the number of discovered facts) and scalability.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Agichtein, E., Gravano, L.: Snowball: Extracting relations from large plain-text collections. In: Proc. of DL, pp. 85–94. ACM (2000)
Banko, M., Cafarella, M.J., Soderland, S., Broadhead, M., Etzioni, O.: Open information extraction from the web. In: Proc. of IJCAI, pp. 2670–2676. Morgan Kaufmann (2007)
Brin, S.: Extracting patterns and relations from the world wide web. In: Atzeni, P., Mendelzon, A.O., Mecca, G. (eds.) WebDB 1998. LNCS, vol. 1590, pp. 172–183. Springer, Heidelberg (1999)
Carlson, A., Betteridge, J., Kisiel, B., Settles, B., Hruschka Jr., E., Mitchell, T.M.: Toward an architecture for never-ending language learning. In: Proc. of AAAI. AAAI Press (2010)
Etzioni, O., Banko, M., Soderland, S., Weld, D.S.: Open information extraction from the web. Communication of ACM 51, 68–74 (2008)
Fader, A., Soderland, S., Etzioni, O.: Identifying relations for open information extraction. In: Proc. of EMNLP, pp. 1535–1545. ACL (2011)
Levenshtein, V.: Binary Codes Capable of Correcting Deletions, Insertions and Reversals. Journal of Soviet Physics Doklady 10, 707 (1966)
Lynam, T.R., Cormack, G.V., Cheriton, D.R.: On-line spam filter fusion. In: Proc. of SIGIR, pp. 123–130. ACM (2006)
Mausam, Schmitz, M., Soderland, S., Bart, R., Etzioni, O.: Open language learning for information extraction. In: Proc. of EMNLP, pp. 523–534. ACL (2012)
Nakashole, N., Theobald, M., Weikum, G.: Scalable knowledge harvesting with high precision and high recall. In: Proc. of WSDM, pp. 227–236. ACM (2011)
Pantel, P., Pennacchiotti, M.: Espresso: leveraging generic patterns for automatically harvesting semantic relations. In: Proc. of ACL, pp. 113–120. ACL (2006)
Parameswaran, A., Garcia-Molina, H., Rajaraman, A.: Towards the web of concepts: extracting concepts from large datasets. VLDB Endowment 3, 566–577 (2010)
Resnik, P.: Semantic similarity in a taxonomy: An information-based measure and its application to problems of ambiguity in natural language. Journal of Artificial Intelligence Research 11, 95–130 (1999)
Takhirov, N., Duchateau, F., Aalberg, T.: An evidence-based verification approach to extract entities and relations for knowledge base population. In: Cudré-Mauroux, P., Heflin, J., Sirin, E., Tudorache, T., Euzenat, J., Hauswirth, M., Parreira, J.X., Hendler, J., Schreiber, G., Bernstein, A., Blomqvist, E. (eds.) ISWC 2012, Part I. LNCS, vol. 7649, pp. 575–590. Springer, Heidelberg (2012)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Takhirov, N., Duchateau, F., Aalberg, T., Sølvberg, I. (2013). An Integrated Approach for Large-Scale Relation Extraction from the Web. In: Ishikawa, Y., Li, J., Wang, W., Zhang, R., Zhang, W. (eds) Web Technologies and Applications. APWeb 2013. Lecture Notes in Computer Science, vol 7808. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37401-2_18
Download citation
DOI: https://doi.org/10.1007/978-3-642-37401-2_18
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-37400-5
Online ISBN: 978-3-642-37401-2
eBook Packages: Computer ScienceComputer Science (R0)