A translation approach to portable ontology specifications
Knowledge Acquisition - Special issue: Current issues in knowledge modeling
Contextual correlates of synonymy
Communications of the ACM
Verbs semantics and lexical selection
ACL '94 Proceedings of the 32nd annual meeting on Association for Computational Linguistics
Sentence Similarity Based on Semantic Nets and Corpus Statistics
IEEE Transactions on Knowledge and Data Engineering
A novel method for measuring semantic similarity for XML schema matching
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Gather customer concerns from online product reviews - A text summarization approach
Expert Systems with Applications: An International Journal
A semantic-based approach to content abstraction and annotation for content management
Expert Systems with Applications: An International Journal
An ontology, intelligent agent-based framework for the provision of semantic web services
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Semantic information integration and question answering based on pervasive agent ontology
Expert Systems with Applications: An International Journal
Semantic business process integration based on ontology alignment
Expert Systems with Applications: An International Journal
An weighted ontology-based semantic similarity algorithm for web service
Expert Systems with Applications: An International Journal
Measuring the understanding between two agents through concept similarity
Expert Systems with Applications: An International Journal
Hi-index | 12.05 |
A novel sentence similarity measure for semantic based expert systems is presented. The well-known problem in the fields of semantic processing, such as QA systems, is to evaluate the semantic similarity between irregular sentences. This paper takes advantage of corpus-based ontology to overcome this problem. A transformed vector space model is introduced in this article. The proposed two-phase algorithm evaluates the semantic similarity for two or more sentences via a semantic vector space. The first phase built part-of-speech (POS) based subspaces by the raw data, and the latter carried out a cosine evaluation and adopted the WordNet ontology to construct the semantic vectors. Unlike other related researches that focused only on short sentences, our algorithm is applicable to short (4-5 words), medium (8-12 words), and even long sentences (over 12 words). The experiment demonstrates that the proposed algorithm has outstanding performance in handling long sentences with complex syntax. The significance of this research lies in the semantic similarity extraction of sentences, with arbitrary structures.