Abstract
In the paper, we proposed a general framework that could automatically extract key-phrases from a collection of web pages concerning a specific topic with the help of The Free Dictionary and then construct a personal knowledge base. Both the base and visual feature in a web page are used to calculate the weight of each candidate phrase. The system extracts top p% key-phrases for each web page based on these two features and then generates a term set using union operators. Next, the system builds the relationships between terms in the term set by referencing The Free Dictionary, and then generates a list of terms sorted by weights. With the top q terms specified by users, a semantic graph can be constructed to present the part of a personal knowledge base, which shows the relationships between terms from the same domain. Finally, the experimental results show that the key-phrases generated by the proposed extractor are with good quality and acceptable for humans.
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
D’Avanzo, E., Magnini, B.: A Keyphrase-based Approach to Summarization: the LAKE System at DUC-2005. In: Document Understanding Workshop (2005)
El-Beltagy, S.R., Rafea, A.: KP-Miner: a Keyphrase Extraction System for English and Arabic Documents. Information Systems 34(1), 132–144 (2009)
HaCohen-Kerner, Y.: Automatic Extraction of Keywords from Abstracts. In: Palade, V., Howlett, R.J., Jain, L. (eds.) KES 2003. LNCS, vol. 2773, pp. 843–849. Springer, Heidelberg (2003)
HaCohen-Kerner, Y., Gross, Z., Masa, A.: Automatic Extraction and Learning of Keyphrases from Scientific Articles. In: Gelbukh, A. (ed.) CICLing 2005. LNCS, vol. 3406, pp. 657–669. Springer, Heidelberg (2005)
Kumar, N., Srinathan, K.: Automatic Keyphrase Extraction from Scientific Documents Using N-gram Filtration Technique. In: 8th ACM Symposium on Document Engineering, pp. 199–208 (2008)
Turney, P.D.: Learning Algorithms for Keyphrase Extraction. Information Retrieval 2(4), 303–336 (2000)
Turney, P.D.: Coherent Keyphrase Extraction via Web Mining. In: 20th International Joint Conference on Artificial Intelligence, pp. 434–439 (2003)
Witten, I.H., Paynter, G.W., Frank, E., et al.: KEA: Practical Automatic Keyphrase Extraction. In: 4th ACM Conference on Digital Libraries, pp. 254–255 (1999)
Zhang, K., Xu, H., Tang, J., Li, J.: eyword Extraction Using Support Vector Machine. In: Yu, J.X., Kitsuregawa, M., Leong, H.-V. (eds.) WAIM 2006. LNCS, vol. 4016, pp. 85–96. Springer, Heidelberg (2006)
Schmid, H.: Probabilistic Part-of-speech Tagging Using Decision Trees. In: International Conference on New Methods in Language Processing, pp. 44–49 (1994)
The Free Dictionary, http://www.thefreedictionary.com/
Cao, L.: In-depth Behavior Understanding and Use: the Behavior Informatics Approach. Information Science 180(17), 3067–3085 (2010)
Zhang, Y., Milios, E., Zincir-Heywood, N.: Narrative Text Classification for Automatic Key Phrase Extraction in Web Document Corpora. In: 7th Annual ACM International Workshop on Web Information and Data Management, pp. 51–58 (2005)
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
Huang, YF., Ciou, CS. (2012). Constructing Personal Knowledge Base: Automatic Key-Phrase Extraction from Multiple-Domain Web Pages. In: Cao, L., Huang, J.Z., Bailey, J., Koh, Y.S., Luo, J. (eds) New Frontiers in Applied Data Mining. PAKDD 2011. Lecture Notes in Computer Science(), vol 7104. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28320-8_6
Download citation
DOI: https://doi.org/10.1007/978-3-642-28320-8_6
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-28319-2
Online ISBN: 978-3-642-28320-8
eBook Packages: Computer ScienceComputer Science (R0)