Mechanical and Electrical Information Aggregation through Semantic Combining of Concepts

Abstract:

In order to compute intelligent answers to complex mechanical and electrical information processing questions, using information existing in the Web, computers have (1) to translate such mechanical and electrical information, typically from text service, into a data structure suitable for semantic development; (2) to aggregate enough mechanical and electrical information about a certain topic or area by integrating these data structures, taking into account new mechanical and electrical information, additional details, better accuracy, and contradiction found in the incoming data structures to be added.This article seeks to solve point (2) by using a method CC(Concept Combining), with its algorithm and implementation, to integrate two concepts (coming from Web service) without human participation, producing a third concept. Results of CC working on concepts extracted from Web service are shown.

• Periodical: Advanced Materials Research (Volume 429)
• Pages: 97-100
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.429.97
• Online since: January 2012
• Authors: Zhao Yang Peng
• Keywords: Concept Integration, Electrical, Information Description, Intelligence Agent, Mechanical, Web Service

References

[1] A. Kakas and M. Denecker. Abduction in logic programming. In A. Kakas and F. Sadri, editors, Computational Logic: Logic Programming and Beyond. Part I, number 2407 in LNAI, pages 402-436. Springer, (2002).

DOI: 10.1007/3-540-45628-7_16

[2] R. Moller, V. Haarslev, and B. Neumann. Concepts based information finding. In Proc. IT&KNOWS-98: International Conference on Information Technology and Knowledge Systems, 31. August- 4. September, Vienna, Budapest, pages 49(6), (1998).

[3] R. Moller and B. Neumann. Ontology-based reasoning techniques for multimedia transformation and finding. In Semantic Multimedia and Ontologies : Theory and Applications. 2007. To appear.

[4] B. Neumann and R. Moller. On Scene Transformation with Description Logics. In H. Christensen and H. -H. Nagel, editors, Cognitive Vision Systems: Sampling the Spectrum of Approaches, number 3948 in LNCS, pages 247-278. Springer, (2006).

[5] S. E. Peraldi, A. Kaya, S. Melzer, R. Moller, and M. Wessel. Multimedia Transformation as Abduction. In Proc. DL-2007: International Workshop on Description Logics, (2007).

[6] M. Shanahan. Perception as Abduction: Turning Sensor Data Into Meaningful Representation. Cognitive Science, 29(1): 103{134, (2005).

DOI: 10.1207/s15516709cog2901_5

[7] R. P. Thagard. The best explanation: Criteria for theory choice. The Journal of Philosophy, (1978).

[8] Edwards, J., Handzic, M., Carlsson, S. and Nissen, M. (2003), Information Management Research and Practice: Visions and Directions, Information Management Research & Practice, 1(1), 49–60.

DOI: 10.1057/palgrave.kmrp.8500005