A density compensation-based path computing model for measuring semantic similarity

by   Xinhua Zhu, et al.

The shortest path between two concepts in a taxonomic ontology is commonly used to represent the semantic distance between concepts in the edge-based semantic similarity measures. In the past, the edge counting is considered to be the default method for the path computation, which is simple, intuitive and has low computational complexity. However, a large lexical taxonomy of such as WordNet has the irregular densities of links between concepts due to its broad domain but. The edge counting-based path computation is powerless for this non-uniformity problem. In this paper, we advocate that the path computation is able to be separated from the edge-based similarity measures and form various general computing models. Therefore, in order to solve the problem of non-uniformity of concept density in a large taxonomic ontology, we propose a new path computing model based on the compensation of local area density of concepts, which is equal to the number of direct hyponyms of the subsumers of concepts in their shortest path. This path model considers the local area density of concepts as an extension of the edge-based path and converts the local area density divided by their depth into the compensation for edge-based path with an adjustable parameter, which idea has been proven to be consistent with the information theory. This model is a general path computing model and can be applied in various edge-based similarity algorithms. The experiment results show that the proposed path model improves the average correlation between edge-based measures with human judgments on Miller and Charles benchmark from less than 0.8 to more than 0.85, and has a big advantage in efficiency than information content (IC) computation in a dynamic ontology, thereby successfully solving the non-uniformity problem of taxonomic ontology.


A New Similarity Measure for Taxonomy Based on Edge Counting

This paper introduces a new similarity measure based on edge counting in...

A Novel Comprehensive Approach for Estimating Concept Semantic Similarity in WordNet

Computation of semantic similarity between concepts is an important foun...

A Parametric Similarity Method: Comparative Experiments based on Semantically Annotated Large Datasets

We present the parametric method SemSimp aimed at measuring semantic sim...

An enhanced method to compute the similarity between concepts of ontology

With the use of ontologies in several domains such as semantic web, info...

Evaluation of taxonomic and neural embedding methods for calculating semantic similarity

Modelling semantic similarity plays a fundamental role in lexical semant...

Clustering Concept Chains from Ordered Data without Path Descriptions

This paper describes a process for clustering concepts into chains from ...

Learning Graph Embeddings from WordNet-based Similarity Measures

We present a new approach for learning graph embeddings, that relies on ...

Please sign up or login with your details

Forgot password? Click here to reset