article

Building spatial temporal relation graph of concepts pair using web repository

Authors:

Kim-Kwang Raymond Choo,

Chuanping HuAuthors Info & Claims

Information Systems Frontiers, Volume 19, Issue 5

Pages 1029 - 1038

https://doi.org/10.1007/s10796-016-9676-4

Published: 01 October 2017 Publication History

Abstract

Mining semantic relations between concepts underlies many fundamental tasks including natural language processing, web mining, information retrieval, and web search. In order to describe the semantic relation between concepts, in this paper, the problem of automatically generating spatial temporal relation graph (STRG) of semantic relation between concepts is studied. The spatial temporal relation graph of semantic relation between concepts includes relation words, relation sentences, relation factor, relation graph, faceted feature, temporal feature, and spatial feature. The proposed method can automatically generate the spatial temporal relation graph (STRG) of semantic relation between concepts, which is different from the manually generated annotation repository such as WordNet and Wikipedia. Moreover, the proposed method does not need any prior knowledge such as ontology or the hierarchical knowledge base such as WordNet. Empirical experiments on real dataset show that the proposed algorithm is effective and accurate.

References

[1]

Agichtein E. and Gravano L. (2000) Snowball: extracting relations from large plain-text collections. In International Conference on Digital Libraries.

[2]

Arnold, P., & Rahm, E. (2015). Automatic Extraction of Semantic Relations from Wikipedia. International Journal on Artificial Intelligence Tools, 24(2), 1540010.

[3]

Ball, F., Bernasconi, F., & Busch, N. A. (2015). Semantic relations between visual objects can be unconsciously processed but not reported under change blindness. Journal of Cognitive Neuroscience, 27, 2253-2268.

Digital Library

[4]

Banko M, Cafarella M., Soderland S., Broadhead M., and Etzioni O. (2009) Open information extraction from the web. In Proceedings of International Joint Conference on Artificial Intelligence, 2670- 2676.

[5]

Bollegala D., Matsuo Y., and Mitsuru I. (2010) Relational Duality: Unsupervised Extraction of Semantic Relations between Entities on the Web. In Proceedings of the 19 h International Conference on World Wide Web, 151-160.

[6]

Brin S. (1998) Extracting patterns and relations from the world wide web. In International Workshop on the Web and Databases.

[7]

Conforti, D., & De Luca, L. (1999). Computer implementation of a medical diagnosis problem by pattern classification. Future Generation Computer Systems, 15(2), 287-292.

Digital Library

[8]

Etzioni, O., Cafarella, M., Downey, D., Popescu, A.-M., Shaked, T., Soderland, S., Weld, D. S., & Yates, A. (2005). Unsupervised named-entity extraction from the web: an experimental study. Artificial Intelligence, 165(1), 91-134.

[9]

Gani, A. (2016). Et al. a survey on indexing techniques for big data: taxonomy and performance evaluation. Knowledge and Information Systems, 46(2), 241-284.

Digital Library

[10]

Giuliano C., Lavelli A., and Romano L. Exploiting shallow linguistic information for relation extraction from biomedical literature. In EACL, 2006.

[11]

Han, J., & Chang, K. (2002). Data mining for web intelligence. Computer, 35(11), 64-70.

Digital Library

[12]

Harabagiu A., Bejan C. A., and Morarescu P. (2005) Shallow semantics for relation extraction. In Proceedings of the 19th International Joint Conference on Artificial Intelligence, 1061-1066.

[13]

Ji, Y., Ying, H., Tran, J., Dews, P., Mansour, A., & Massanari, R. (2013). A Method for Mining Infrequent Causal Associations and Its Application in Finding Adverse Drug Reaction Signal Pairs. IEEE Transactions on Knowledge and Data Engineering, 25, 721-733.

Digital Library

[14]

Liu, Y., Zhang, Q., & Lionel, M. N. (2010). Opportunity-Based Topology Control in Wireless Sensor Networks. IEEE Transactions on Parallel and Distributed Systems, (21(3), 405-416.

[15]

Liu, Y., Zhu, Y., Lionel, M. N., & Xue, G. (2011). A Reliability-Oriented Transmission Service in Wireless Sensor Networks. IEEE Transactions on Parallel and Distributed Systems, 22(12), 2100-2107.

Digital Library

[16]

Luo G., Tang C., and Tian Y. (2007) Answering Relationship Queries on the Web. In Proceedings of the 16th International Conference on World Wide Web, 561-570.

[17]

Luo, X., Xu, Z., Yu, J., & Chen, X. (2011). Building association link network for semantic link on web resources. IEEE Transactions on Automation Science and Engineering, 8(3), 482-494.

[18]

Ma, Y., Wang, L., et al. (2013). Distributed data structure templates for data-intensive remote sensing application. Concurrency and computation: practice and experience, 25(12), 1784-1797.

[19]

Moschopoulos, T., Iosif, E., Demetropoulou, L., Potamianos, A., & Narayanan, S. (2013). Towards the automatic extraction of policy networks using web links and documents. IEEE Transactions on Knowledge and Data Engineering, 25, 2404-2417.

[20]

Shinyama Y. & Sekine S. (2006) Preemptive information extraction using unrestricted relation discovery. In Proceedings of the main conference on Human Language Technology Conference of the North American Chapter of the Association of Computational Linguistic, 304-311.

[21]

Solvberg, I., Nordbo, I., & Aamodt, A. (1992). Knowledge-based information retrieval. Future Generation Computer Systems, 7(4), 379-390.

Digital Library

[22]

Sparrow, B., Liu, J., & Wegner, D. (2011). Google effects on memory: cognitive consequences of having information at our fingertips. Science, 333, 776-778.

[23]

TREC. (2005) Proceedings (relationship task in the QA track). http://trec.nist.gov/pubs/trec14/t14_proceedings.html.

[24]

Wang, L., & Khan, S. (2013). Review of performance metrics for green data centers: a taxonomy study. The Journal of Supercomputing, 63(3), 639-656.

Digital Library

[25]

Wang, L., Chen, D., et al. (2013a). Towards enabling cyber infrastructure as a service in clouds. Computer & Electrical Engineering, 39(1), 3-14.

Digital Library

[26]

Wang, L., Tao, J., et al. (2013b). G-Hadoop: MapReduce across distributed data centers for data-intensive computing. Future Generation Computer Systems, 29(3), 739-750.

Digital Library

[27]

Xu, Z., Luo, X., Yu, J., & Xu, W. (2011). Measuring semantic similarity between words by removing noise and redundancy in web snippets. Concurrency and computation-practice & experience, 23(18), 2496-2510.

[28]

Xu, Z., Luo, X., Wei, X., & Mei, L. (2013). Temporal Faceted Learning of Concepts using Web Search Engines. The 12th International Conference on Web-based Learning, 8167, 254-263.

[29]

Yen, N., Shih, T., Zhao, L., & Jin, Q. (2010). Ranking metrics and search guidance for learning object repository. IEEE Transactions on Learning Technologies, 3(3), 250-264.

Digital Library

[30]

Yen, N., Shih, T., & Jin, Q. (2013). LONET: an interactive search network for intelligent lecture path generation. ACM Transactions on Intelligent Systems and Technology, 4(2), 30.

Digital Library

[31]

Yuan, D., Yang, Y., Liu, X., Li, W., Cui, L., Xu, M., & Chen, J. (2013). A highly practical approach towards achieving minimum datasets storage cost in the cloud. IEEE Transactions on Parallel and Distributed Systems, 24(6), 1234-1244.

Digital Library

[32]

Zelenko, D., AoneE, C., & Richardella, A. (2003). Kernel methods for relation extraction. Journal of Machine Learning Research, 3, 1083-1106.

Digital Library

[33]

Zhou G., Zhang M., Ji D. H., and Zhu Q. (2007) Tree kernel-based relation extraction with context-sensitive structured parse tree information. In Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, 728-736.

[34]

Zhu J., Nie Z., Liu X., Zhang B., and Wen J. (2009) StatSnowball: a Statistical Approach to Extracting Entity Relationships. In Proceedings of the 18th International Conference on World Wide Web, 101-110.

[35]

Zhuge, H. (2009). Communities and emerging semantics in semantic link network: discovery and learning. IEEE Transactions on Knowledge and Data Engineering, 21(6), 785-799.

Digital Library

[36]

Zhuge, H. (2011). Semantic linking through spaces for cyber-physical-socio intelligence: a methodology. Artificial Intelligence, 175, 988-1019.

Digital Library

Cited By

Al-Turjman F(2022)Cognitive routing protocol for disaster-inspired Internet of ThingsFuture Generation Computer Systems10.1016/j.future.2017.03.01492:C(1103-1115)Online publication date: 15-Apr-2022
https://dl.acm.org/doi/10.1016/j.future.2017.03.014
Costa AHeuer de Carvalho V(2021)Public Security Sentiment Analysis on Social WebInternational Journal of Decision Support System Technology10.4018/IJDSST.202101010113:1(1-20)Online publication date: 1-Jan-2021
https://dl.acm.org/doi/10.4018/IJDSST.2021010101
Li MTan JWang YZhang LSalapura V(2017)SparkBenchCluster Computing10.1007/s10586-016-0723-120:3(2575-2589)Online publication date: 1-Sep-2017
https://dl.acm.org/doi/10.1007/s10586-016-0723-1

Building spatial temporal relation graph of concepts pair using web repository
1. Information systems

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cover image Information Systems Frontiers

Information Systems Frontiers Volume 19, Issue 5

October 2017

256 pages

ISSN:1387-3326

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Copyright © Copyright © 2017 Springer Science+Business Media, LLC.

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Kluwer Academic Publishers

United States

Publication History

Published: 01 October 2017

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Cited By

Al-Turjman F(2022)Cognitive routing protocol for disaster-inspired Internet of ThingsFuture Generation Computer Systems10.1016/j.future.2017.03.01492:C(1103-1115)Online publication date: 15-Apr-2022
https://dl.acm.org/doi/10.1016/j.future.2017.03.014
Costa AHeuer de Carvalho V(2021)Public Security Sentiment Analysis on Social WebInternational Journal of Decision Support System Technology10.4018/IJDSST.202101010113:1(1-20)Online publication date: 1-Jan-2021
https://dl.acm.org/doi/10.4018/IJDSST.2021010101
Li MTan JWang YZhang LSalapura V(2017)SparkBenchCluster Computing10.1007/s10586-016-0723-120:3(2575-2589)Online publication date: 1-Sep-2017
https://dl.acm.org/doi/10.1007/s10586-016-0723-1

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