Authors:
Haytham Mohtasseb
and
Amr Ahmed
Affiliation:
University of Lincoln, United Kingdom
Keyword(s):
Commonsense knowledgebase, Semantic network, Ontology development, Psycholinguistic, Text classification.
Related
Ontology
Subjects/Areas/Topics:
Applications and Case-studies
;
Artificial Intelligence
;
Knowledge Engineering and Ontology Development
;
Knowledge Representation
;
Knowledge-Based Systems
;
Networked Ontologies
;
Ontology Matching and Alignment
;
Ontology Sharing and Reuse
;
Symbolic Systems
Abstract:
Ontologies have been widely accepted as the most advanced knowledge representation model. This paper introduces PsychoNet, a new knowledgebase that forms the link between psycholinguistic taxonomy, existing in LIWC, and its semantic textual representation in the form of commonsense semantic ontology, represented by ConceptNet. The integration of LIWC and ConceptNet and the added functionalities facilitate employing ConceptNet in psycholinguistic studies. Furthermore, it simplifies utilization of the huge network of Concept-Net for a specific multimedia application based on key category(ies) from LIWC, such as visual or biological applications. PsychoNet adds a new layer of complementary psycholinguistic functions to the original semantic network. Moreover, learning, either clustering or classification, is more applicable in the developed ontology. The paper shows a sample application of text classification for mood prediction task. The result confirms the validity of the proposed net
work as PsychoNet outperforms LIWC in mood prediction.
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