An Interval Type-2 Fuzzy Ontological Similarity Measure

Abstract

Human language is naturally fuzzy by nature, with words meaning different things to different people, depending on the context. Fuzzy words, are words with a subjective meaning, typically used in everyday human natural language dialogue; they are often ambiguous and vague in meaning depending on an individual’s perception. Fuzzy Sentence Similarity Measures (FSSM) are algorithms that can compare two or more short texts which contain fuzzy words and return a numeric measure of similarity of meaning between them. This paper proposes a new FSSM called FUSE (FUzzy Similarity mEasure). FUSE is an ontology-based similarity measure that uses Interval Type-2 Fuzzy Sets to model relationships between categories of human perception-based words. The FUSE algorithm has been developed over four versions and been compared to several state-of-the-art, traditional semantic similarity measures (SSM’s) which do not consider the presence of fuzzy words. The FUSE algorithm along with the other traditional SSM’s mentioned have been evaluated on several published, gold standard and newly created datasets. Results have shown the FUSE algorithm is able to improve on the limitations of traditional SSM’s by achieving a higher correlation with the average human rating (AHR) compared to traditional SSM’s that do not consider the presence of fuzzy words. The key contributions of this work can be summarised as follows: The development of a new methodology to model fuzzy words using Interval Type-2 fuzzy sets. This has led to the creation of a fuzzy dictionary for nine fuzzy categories, a useful resource which can be used by other researchers in the field of natural language processing and Computing with Words (CWW) with other fuzzy applications such as semantic clustering.

Keywords

• Computing with words
• natural language processing
• FUSE
• semantic similarity
• fuzzy sets
• machine learning