Extracting and evaluating ontologies of human activities from linked open data and social media

被引：0

作者：

Kataoka Y. ^{[1
]}

Nakatsuji M. ^{[2
]}

Toda H. ^{[1
]}

Koike Y.

Matsuo Y. ^{[3
]}

机构：

[1] NTT Service Evolution Laboratories, Nippon Telegraph and Telephone Corporation

[2] Smart Navigation Business Division, NTT Resonant Inc.

[3] Graduate School of Engineering, The University of Tokyo

来源：

Transactions of the Japanese Society for Artificial Intelligence | 2016年 / 31卷 / 01期

关键词：

Human activity; Knowledge acquisition; Linked Open Data; Machine learning;

D O I：

10.1527/tjsai.LOD-H-NGC

中图分类号：

学科分类号：

摘要：

Many of the problems we face today in artificial intelligence, e.g., real-world activity navigation, can only be solved if we have adequate knowledge bases of human behavior. Although some of the knowledge is available as Linked Open Data (LOD), the amount of data that is available is not enough to realize truly useful navigation applications. The purpose of this paper is to propose and evaluate a method that can automatically extract an ontology of human activities. This ontology is composed of a graph based on ConceptNet5 whose nodes represent activities and whose edges represent semantics between activities. The challenges to developing a truly useful activity navigation system are the following: (i) improving the currently low coverage rate of extracted activities, and (ii) allowing and predicting multiple semantics between activities. The proposed method has two steps: (1) extract mutually related activities from social media, and (2) predict the semantic label between activities via supervised learning by leveraging the semantics on ConceptNet5 as labeled data. Comparisons with ConceptNet5 show that the proposed method can discover more activities than ConceptNet5 and predict semantic relations between activities more accurately, about 10% more accurately, than the baseline method. © 2016, Japanese Society for Artificial Intelligence. All rights reserved.

引用

页码：1 / 12

页数：11

共 24 条

[1]

Acosta M., Zaveri A., Simperl E., Kontokostas D., Auer S., Lehmann J., Crowdsourcing Linked Data Quality Assessment, Proc. of the 12Th International Semantic Web Conference-Part II, ISWC ’13, pp. 260-276, (2013)

[2]

Bishop C.M., Pattern Recognition and Machine Learning, (2006)

[3]

Bizer C., Heath T., Berners-Lee T., Linked data-the story so far, Int’l Journal on Semantic Web and Information Systems, 5, 3, pp. 1-22, (2009)

[4]

Etzioni O., Fader A., Christensen J., Soderland S., Mausam M., Open Information Extraction: The Second Generation, Proc. of the 22Th International Joint Conference on Artificial Intelligence-Volume 1, IJCAI’11, pp. 3-10, (2011)

[5]

Ferrucci D.A., Introduction to ”This is Watson, IBM J. Res. Dev., pp. 235-249, (2012)

[6]

Fukazawa Y., Ota J., Automatic modeling of user’s real world activities from the web for semantic IR, Proc of the 3Rd International Semantic Search Workshop in WWW2010, (2010)

[7]

Gupta R., Kochenderfer M.J., Common sense data acquisition for indoor mobile robots, AAAI, pp. 605-610, (2004)

[8]

Havasi C., Speer R., Alonso J., ConceptNet 3: A flexible, multilingual semantic network for common sense knowledge, Recent Advances in Natural Language Processing, pp. 27-29, (2007)

[9]

Jung Y., Ryu J., Kim K.-M., Myaeng S.-H., Automatic Construction of a Large-scale Situation Ontology by Mining How-to Instructions from the Web, Journal of Web Semantics, pp. 110-124, (2010)

[10]

Kudo T., Matsumoto Y., Fast methods for kernel-based text analysis, Proc. of the 41St Annual Meeting on Association for Computational Linguistics-, 1, pp. 24-31, (2003)

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