Fuzzy ontologies in semantic similarity measures (original) (raw)

An Interval Type-2 Fuzzy Ontological Similarity Measure

IEEE Access, 2022

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.

Ontological and Fuzzy Set Similarity between Perception-Based Words

2019 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 2019

Fuzzy short text semantic similarity measures allow the inclusion of human perception based words to be within the similarity measurement which results in better correlation on the meaning of the short text with human understanding. Existing measures such as FUSE and FAST rely on the creation of fuzzy ontological structures from the modelling of perception words using type-1 or type-2 fuzzy sets. Due to the complex methodology of creating these ontologies, fuzzy word representation cannot be guaranteed due to language evolution. This paper presents a comparative study of simpler fuzzy set similarity measures. The results surprisingly indicate that a very simple fuzzy set similarity measure created from the center of gravity (COG) distance between type-2 fuzzy sets has a very high correlation with the FUSE semantic similarity measure.

On the creation of a fuzzy dataset for the evaluation of fuzzy semantic similarity measures

2014 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 2014

Short text semantic similarity (STSS) measures are algorithms designed to compare short texts and return a level of similarity between them. However, until recently such measures have ignored perception or fuzzy based words (i.e. very hot, cold less cold) in calculations of both word and sentence similarity. Evaluation of such measures is usually achieved through the use of benchmark data sets comprising of a set of rigorously collected sentence pairs which have been evaluated by human participants. A weakness of these datasets is that the sentences pairs include limited, if any, fuzzy based words that makes them impractical for evaluating fuzzy sentence similarity measures. In this paper, a method is presented for the creation of a new benchmark dataset known as SFWD (Single Fuzzy Word Dataset). After creation the data set is then used in the evaluation of FAST, an ontology based fuzzy algorithm for semantic similarity testing that uses concepts of fuzzy and computing with words to allow for the accurate representation of fuzzy based words. The SFWD is then used to undertake a comparative analysis of other established STSS measures.

Fuzzy Influence in Fuzzy Semantic Similarity Measures

2021 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

The field of Computing with Words has been pivotal in the development of fuzzy semantic similarity measures. Fuzzy semantic similarity measures allow the modelling of words in a given context with a tolerance for the imprecise nature of human perceptions. In this work, we look at how this imprecision can be addressed with the use of fuzzy semantic similarity measures in the field of natural language processing. A fuzzy influence factor is introduced into an existing measure known as FUSE. FUSE computes the similarity between two short texts based on weighted syntactic and semantic components in order to address the issue of comparing fuzzy words that exist in different word categories. A series of empirical experiments investigates the effect of introducing a fuzzy influence factor into FUSE across a number of short text datasets. Comparisons with other similarity measures demonstrates that the fuzzy influence factor has a positive effect in improving the correlation of machine similarity judgments with similarity judgments of humans.

FAST: A fuzzy semantic sentence similarity measure

2013 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 2013

A problem in the field of semantic sentence similarity is the inability of sentence similarity measures to accurately represent perception based (fuzzy) words that are commonly used in natural language. This paper presents a new sentence similarity measure that attempts to solve this problem. The new measure, Fuzzy Algorithm for Similarity Testing (FAST) is an ontology based similarity measure that uses concepts of fuzzy and computing with words to allow for the accurate representation of fuzzy based words. Through human experimentation fuzzy sets were created for six categories of words based on their levels of association with particular concepts. These fuzzy sets were then defuzzified and the results used to create new ontological relations between the words. Using these relationships allows for the creation of a new ontology based semantic text similarity algorithm that is able to show the effect of fuzzy words on computing sentence similarity as well as the effect that fuzzy words have on non-fuzzy words within a sentence. Experiments on FAST were conducted using a new fuzzy dataset, the creation of which is described in this paper. The results of the evaluation showed that there was an improved level of correlation between FAST and human test results over two existing sentence similarity measures.

Using Fuzzy Set Similarity in Sentence Similarity Measures

2020 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

Sentence similarity measures the similarity between two blocks of text. A semantic similarity measure between individual pairs of words, each taken from the two blocks of text, has been used in STASIS. Word similarity is measured based on the distance between the words in the WordNet ontology. If the vague words, referred to as fuzzy words, are not found in WordNet, their semantic similarity cannot be used in the sentence similarity measure. FAST and FUSE transform these vague words into fuzzy set representations, type-1 and type-2 respectively, to create ontological structures where the same semantic similarity measure used in WordNet can then be used. This paper investigates eliminating the process of building an ontology with the fuzzy words and instead directly using fuzzy set similarity measures between the fuzzy words in the task of sentence similarity measurement. Their performance is evaluated based on their correlation with human judgments of sentence similarity. In addition, statistical tests showed there is not any significant difference in the sentence similarity values produced using fuzzy set similarity measures between fuzzy sets representing fuzzy words and using FAST semantic similarity within ontologies representing fuzzy words.

A Combined Fuzzy Semantic Similarity Measure in OWL Ontologies

2008

An algorithm is presented in this paper to calculate a semantic similarity measure inside an OWL ontology. The formulation is based on a combined measure taking into account the two most important aspects involved in the similarity computation. These are the structural properties of a concept, and the information content inside the ontology. We define a fuzzy system to blend these information sources with a training process over some ontologies. Finding a similarity measure between concepts of an ontology is a fundamental topic to accomplish information exchange on the Web. Through this measure it is possible to perform sophisticated queries over the web where the user is able to request concepts with a predefined similarity (or even dissimilarity) degree.

A distance function to assess the similarity of words using ontologies

2010

Abstract When comparing categorical values, traditional approaches use metrics based only on the matching of the values, obtaining a Boolean result. In this paper, it is proposed to use a measure able to compute the degree of semantic similarity between a pair of terms using an ontology as background knowledge.

A Survey and Comparison of WordNet Based Semantic Similarity Measures

2013

Semantic Similarity relates to computing the similarity between concepts within ontology. We explore the different categories of approaches to compute semantic similarity and the most popular measures are evaluated using WordNet as the source ontology. We compare the measures using the benchmark dataset of Miller & Charles with WordNet to rank the measures category wise and overall.