Abstract
In this paper, we present an algorithmic formulation to automatically extract learning concepts and their relationships from eBook texts and to generate an RDF data that can be used for a number of purposes. Our algorithmic approach first extracts various parts of an eBook (such as chapters and sections) and then through a sentence-level parsing scheme identifies learning concepts described in the eBook text. We have programmed for the identification and extraction of relationships between different learning concepts occurring in a section. We have also been able to extract some general data about the eBooks such as author, price, and reviews (through eBook content mining and web crawling). The learning concepts, their relationships and other useful information extracted from the eBooks; is then programmatically transformed into a machine readable RDF data. The automated process of concept and relation extraction and their subsequent storage into RDF data, makes our effort important and useful for tasks like Information Extraction, Concept-based Search and Machine Reading.
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Piryani, R., Uddin, A., Devaraj, M., Singh, V.K. (2013). An Algorithmic Formulation for Extracting Learning Concepts and Their Relatedness in eBook Texts. In: Prasath, R., Kathirvalavakumar, T. (eds) Mining Intelligence and Knowledge Exploration. Lecture Notes in Computer Science(), vol 8284. Springer, Cham. https://doi.org/10.1007/978-3-319-03844-5_53
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DOI: https://doi.org/10.1007/978-3-319-03844-5_53
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-03843-8
Online ISBN: 978-3-319-03844-5
eBook Packages: Computer ScienceComputer Science (R0)