Information and Relation Extraction for Semantic Annotation of eBook Texts

Abstract

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This paper presents our algorithmic approach for information and relation extraction from unstructured texts (such as from eBook sections or webpages), performing other useful analytics on the text, and automatically generating a semantically meaningful structure (RDF schema). Our algorithmic formulation parses the unstructured text from eBooks and identifies key concepts described in the eBook along with relationship between the concepts. The extracted information is then used for four purposes: (a) for generating some computed metadata about the text source (such as readability of an eBook), (b) generate a concept profile for each distinct part of text, (c) identifying and plotting relationship between key concepts described in the text, and (d) to generate RDF representation for the text source. We have done our experiments on eBook texts from Computer Science domain; however, the approach can be applied to work on different forms of text in other domains as well. The results are not only useful for concept based tagging and navigation of unstructured text documents (such as eBook) but can also be used to design a comprehensive and sophisticated learning recommendation system.