Abstract
A comprehensive ontology can ease the discovery, maintenance and popularization of knowledge in many domains. As a means to enhance existing ontologies, attribute extraction has attracted tremendous research attentions. However, most existing attribute extraction techniques focus on exploring a single type of sources, such as structured (e.g., relational databases), semi-structured (e.g., Extensible Markup Language (XML)) or unstructured sources (e.g., Web texts, images), which leads to the poor coverage of knowledge bases (KBs). This paper presents a framework for ontology augmentation by extracting attributes from four types of sources, namely existing knowledge bases (KBs), query stream, Web texts, and Document Object Model (DOM) trees. In particular, we use query stream and two major KBs, DBpedia and Freebase, to seed the attribute extraction from Web texts and DOM trees. We specially focus on exploring the extraction technique from DOM trees, which is rarely studied in previous works. Algorithms and a series of filters are developed. Experiments show the capability of our approach in augmenting existing KB ontology.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Adelberg, B.: NoDoSE - A Tool for Semi-automatically Extracting Structured and Semistructured Data from Text Documents. ACM SIGMOD Record 27(2), 283–294 (1998)
Arasu, A., Garcia-Molina, H.: Extracting structured data from web pages. In: Proceedings of ACM SIGMOD Conference (SIGMOD 2003), New York, USA (2003)
Bing, L., Lam, W., Gu, Y.: Towards a unified solution: data record region detection and segmentation. In: Proceedings of the 20th ACM Intl. Conf. on Information and Knowledge Management (CIKM 2011), New York, NY, USA (2011)
Crescenzi, V., Mecca, G., Merialdo, P.: RoadRunner: automatic data extraction from data-intensive web sites. In: Proceedings of the 2002 ACM SIGMOD Conference (SIGMOD 2002), New York, NY, USA (2002)
Grishman, R.: Information extraction: capabilities and challenges. In: Notes for the 2012 International Winter School in Language and Speech Technologies. Rovira i Virgili University, Tarragona (2012)
Gupta, R., Halevy, A., Wang, X., Whang, S., Wu, F.: Biperpedia: An Ontology for Search Applications. The VLDB Endowment (PVLDB) 7(7), 505–516 (2014)
Haghighi, A., Klein, D.: Simple coreference resolution with rich syntactic and semantic features. In: Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing (EMNLP 2009), Singapore (2009)
Irmak, U., Suel, T.: Interactive wrapper generation with minimal user effort. In: Proceedings of the 15th International Conference on World Wide Web (WWW 2006), New York, NY, USA (2006)
Kopliku, A., Boughanem, M., Pinel-Sauvagnat, K.: Towards a framework for attribute retrieval. In: Proceedings of the 20th ACM International Conference on Information and Knowledge Management (CIKM 2011), New York, NY, USA (2011)
Kristjansson, T., Culotta, A., Viola, P., McCallum, A.: Interactive information extraction with constrained conditional random fields. In: Proceedings of the 19th National Conf. on Artifical Intelligence (AAAI 2004), San Jose, California (2004)
Lee, T., Wang, Z., Wang, H., won Hwang, S.: Attribute extraction and scoring: a probabilistic approach. In: Proceedings of 29th International Conference on Data Engineering (ICDE 2013), Brisbane, Australia (2013)
Liu, B., Grossman, R., Zhai, Y.: Mining data records in web pages. In: Proceedings of the 9th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2003), New York, NY, USA (2003)
Liu, L., Pu, C., Han, W.: XWRAP: an XML-enabled wrapper construction system for web information sources. In: Proceedings of the 16th International Conference on Data Engineering (ICDE 2000), San Diego, California, USA (2000)
Paşca, M., Alfonseca, E., Robledo-Arnuncio, E., Martin-Brualla, R., Hall, K.: The role of query sessions in extracting instance attributes from web search queries. In: Gurrin, C., He, Y., Kazai, G., Kruschwitz, U., Little, S., Roelleke, T., Rüger, S., van Rijsbergen, K. (eds.) ECIR 2010. LNCS, vol. 5993, pp. 62–74. Springer, Heidelberg (2010)
Pasca, M., Durme, B.V.: What you seek is what you get: extraction of class attributes from query logs. In: Proceedings of the 20th International Joint Conference on Artificial Intelligence (IJCAI 2007), Hyderabad, India (2007)
Turmo, J., Ageno, A., Català , N.: Adaptive Information Extraction. ACM Computing Surveys (CSUR) 38(2), 4-es (2006)
Zhu, J., Nie, Z., Wen, J.R., Zhang, B., Ma, W.Y.: Simultaneous record detection and attribute labeling in web data extraction. In: Proceedings of the 12th ACM SIGKDD Conference (KDD 2006), New York, NY, USA (2006)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Fang, X.S., Wang, X., Sheng, Q.Z. (2015). Ontology Augmentation via Attribute Extraction from Multiple Types of Sources. In: Sharaf, M., Cheema, M., Qi, J. (eds) Databases Theory and Applications. ADC 2015. Lecture Notes in Computer Science(), vol 9093. Springer, Cham. https://doi.org/10.1007/978-3-319-19548-3_2
Download citation
DOI: https://doi.org/10.1007/978-3-319-19548-3_2
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-19547-6
Online ISBN: 978-3-319-19548-3
eBook Packages: Computer ScienceComputer Science (R0)