Effective tag recommendation system based on topic ontology using Wikipedia and WordNet
|Effective tag recommendation system based on topic ontology using Wikipedia and WordNet|
|Author(s)||Subramaniyaswamy V., Chenthur Pandian S.|
|Published in||International Journal of Intelligent Systems|
|Keyword(s)||Unknown (Extra: Activation algorithm, Data sets, Experimental evaluation, External knowledge, Folksonomies, High quality, Lexical patterns, Ontology construction, Open directory projects, Precision and recall, Real world data, Semantic concept, Semantic relations, Semantic relationships, Semantic similarity measures, Software prototypes, Synsets, Tag recommendations, Web ontology language, Wikipedia, Wordnet, Clustering algorithms, Conformal mapping, Experiments, Semantic Web, Semantics, Software prototyping, Virtual reality, Websites, Ontology)|
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Effective tag recommendation system based on topic ontology using Wikipedia and WordNet is a 2012 journal article written in English by Subramaniyaswamy V., Chenthur Pandian S. and published in International Journal of Intelligent Systems.
In this paper, we proposed a novel approach based on topic ontology for tag recommendation. The proposed approach intelligently generates tag suggestions to blogs. In this approach, we construct topic ontology through enriching the set of categories in existing small ontology called as Open Directory Project. To construct topic ontology, a set of topics and their associated semantic relationships is identified automatically from the corpus-based external knowledge resources such as Wikipedia and WordNet. The construction relies on two folds such as concept acquisition and semantic relation extraction. In the first fold, a topic-mapping algorithm is developed to acquire the concepts from the semantic of Wikipedia. A semantic similarity-clustering algorithm is used to compute the semantic similarity measure to group the set of similar concepts. The second is the semantic relation extraction algorithm, which derives associated semantic relations between the set of extracted topics from the lexical patterns between synsets in WordNet. A suitable software prototype is created to implement the topic ontology construction process. A Jena API framework is used to organize the set of extracted semantic concepts and their corresponding relationship in the form of knowledgeable representation of Web ontology language. Thus, Protégé tool provides the platform to visualize the automatically constructed topic ontology successfully. Using the constructed topic ontology, we can generate and suggest the most suitable tags for the new resource to users. The applicability of topic ontology with a spreading activation algorithm supports efficient recommendation in practice that can recommend the most popular tags for a specific resource. The spreading activation algorithm can assign the interest scores to the existing extracted blog content and tags. The weight of the tags is computed based on the activation score determined from the similarity between the topics in constructed topic ontology and content of the existing blogs. High-quality tags that has the highest activation score is recommended to the users. Finally, we conducted experimental evaluation of our tag recommendation approach using a large set of real-world data sets. Our experimental results explore and compare the capabilities of our proposed topic ontology with the spreading activation tag recommendation approach with respect to the existing AutoTag mechanism. And also discuss about the improvement in precision and recall of recommended tags on the data sets of Delicious and BibSonomy. The experiment shows that tag recommendation using topic ontology results in the folksonomy enrichment. Thus, we report the results of an experiment mean to improve the performance of the tag recommendation approach and its quality.
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