Authors:
Antonio M. Rinaldi
1
and
Cristiano Russo
2
Affiliations:
1
Dipartimento di Ingegneria Elettrica e delle Tecnologie dell’Informazione, IKNOS-LAB-Intelligent and Knowledge Systems-LUPT, University of Naples Federico II and Italy
;
2
LISSI Laboratory, University of Paris-Est Creteil (UPEC) and France
Keyword(s):
Document Representation, Semantic and Linguistic Analysis, WordNet, Lexical Chains, NoSQL, Neo4J.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Concept Mining
;
Knowledge Discovery and Information Retrieval
;
Knowledge-Based Systems
;
Mining Text and Semi-Structured Data
;
Symbolic Systems
;
Visual Data Mining and Data Visualization
Abstract:
As an increasing number of text-based documents, whose complexity increases in turn, are available over the Internet, it becomes obvious that handling such documents as they are, i.e. in their original natural-language based format, represents a daunting task to face up for computers. Thus, some methods and techniques have been used and refined, throughout the last decades, in order to transform the digital documents from the full text version to another suitable representation, making them easier to handle and thus helping users in getting the right information with a reduced algorithmic complexity. One of the most spread solution in document representation and retrieval has consisted in transforming the full text version into a vector, which describes the contents of the document in terms of occurrences patterns of words. Although the wide adoption of this technique, some remarkable drawbacks have been soon pointed out from the researchers’ community, mainly focused on the lack of
semantics for the associated terms. In this work, we use WordNet as a generalist linguistic database in order to enrich, at a semantic level, the document representation by exploiting a label and properties based graph model, implemented in Neo4J. This work demonstrates how such representation allows users to quickly recognize the document topics and lays the foundations for cross-document relatedness measures that go beyond the mere word-centric approach.
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