Abstract
In a text, two concepts can hold either direct or higher order relationship where function of some concepts is considered as another concept. Essentially, we require a mechanism to capture complex associations between concepts. Keeping this in view, we propose a knowledge representation scheme which is flexible enough to capture any order of associations between concepts in factual as well as non-factual sentences. We utilize a five-tuple representation scheme to capture associations between concepts and based on our evaluation strategy we found that by this we are able to represent 90.7 % of the concept associations correctly. This is superior to existing pattern based methods. A use case in the domain of content retrieval has also been evaluated which has shown to retrieve more accurate content using our knowledge representation scheme thereby proving the effectiveness of our approach.
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Acknowledgments
This research was supported by Samsung R & D Institute India - Bangalore. We thank our colleagues Mr. Tripun Goel, Mr. Krishnamraju Murali Venkata Mutyala, Mr. Chandragouda Patil, Mr. Ramachandran Narasimhamurthy, Mr. Srinidhi Nirgunda Seshadri, Dr. Shankar M. Venkatesan for providing insight and expertise that greatly assisted the research. We thank them for comments to improve the manuscript.
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Chatterji, S., Varshney, N., Chanda, P.K., Mittal, V., Jagwani, B.B. (2016). Extracting and Representing Higher Order Predicate Relations between Concepts. In: Métais, E., Meziane, F., Saraee, M., Sugumaran, V., Vadera, S. (eds) Natural Language Processing and Information Systems. NLDB 2016. Lecture Notes in Computer Science(), vol 9612. Springer, Cham. https://doi.org/10.1007/978-3-319-41754-7_15
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