Publicación: EXTRACTING CONTEXT DATA FROM USER REVIEWS FOR RECOMMENDATION: A LINKED DATA APPROACH
Fecha
2017
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IN THIS PAPER WE DESCRIBE A NOVEL APPROACH TO EXTRACT CONTEXTUAL INFORMATION FROM USER REVIEWS, WHICH CAN BE EXPLOITED BY CONTEXT-AWARE RECOMMENDER SYSTEMS. THE APPROACH MAKES USE OF A GENERIC, LARGE-SCALE CONTEXT TAXONOMY THAT IS COMPOSED OF SEMANTIC ENTITIES FROM DBPEDIA, THE CORE ONTOLOGY AND KNOWLEDGE BASE OF THE LINKED DATA INITIATIVE. THE TAXONOMY IS BUILT IN A SEMI-AUTOMATIC FASHION THROUGH A SOFTWARE TOOL WHICH, ON THE ONE HAND, AUTOMATICALLY EXPLORES DBPEDIA BY ONLINE QUERYING FOR RELATED ENTITIES AND, ON THE OTHER HAND, ALLOWS FOR MANUAL ADJUSTMENTS OF THE TAXONOMY. THE PROPOSED APPROACH PERFORMS A APPING BETWEEN WORDS IN THE REVIEWS AND ELEMENTS OF THE TAXONOMY. IN THIS CASE, OUR TOOL ALSO ALLOWS FOR THE MANUAL VALIDATION AND CORRECTION OF EXTRACTED CONTEXT ANNOTATIONS. WE
DESCRIBE THE TAXONOMY CREATION PROCESS AND THE DEVELOPED TOOL, AND FURTHER PRESENT SOME PRELIMINARY RESULTS REGARDING THE EFFECTIVENESS OF OUR APPROACH.
