Semantic similarity of ontology instances using polarity mining

Abstract

Semantic similarity is vital to many areas, such as information retrieval. Various methods have been proposed with a focus on comparing unstructured text documents. Several of these have been enhanced with ontology; however, they have not been applied to ontology instances. With the growth in ontology instance data published online through, for example, Linked Open Data, there is an increasing need to apply semantic similarity to ontology instances. Drawing on ontologysupported polarity mining (OSPM), we propose an algorithm that enhances the computation of semantic similarity with polarity mining techniques. The algorithm is evaluated with online customer review data. The experimental results show that the proposed algorithm outperforms the baseline algorithm in multiple settings.

Mining opinion summarizations using convolutional neural networks in Chinese microblogging systems

“…Zhou and Chaovalit [21] aimed to enhance polarity mining with ontology by providing detailed topic-specific information. Narock et al [22] proposed an algorithm that enhances the computation of semantic similarity with polarity mining techniques. Zhu et al [16] introduced another kind of unsupervised method that establishes the duality between sentiment clustering and coclustering of a tripartite graph using a unified tri-clustering framework.…”