Toward semantic similarity measure between concepts in an ontology

<p>A similarity measure is one classical problem in Description Logic which aims at identifying the similarity between concepts in an ontology. Finding a hierarchy distance among concepts in an ontology is one popular technique. However, one major drawback of such a technique is that it usually ignores a concept definition analysis. This work introduces a new method for similarity measure. The proposed system semantically analyzes structures of two concept descriptions and then computes the similarity score based on the number of shared features. The efficiency of the proposed algorithm is measured by means of the satisfaction of desirable properties and intensive experiments on the Snomed ct ontology.</p>

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A New Semantic Similarity Measure Based On Ontology for Movie Rate Prediction

International Journal of Recent Technology and Engineering - 2 ◽

10.35940/ijrte.c4442.098319 ◽

2019 ◽

Vol 8 (3) ◽

pp. 6756-6762

Keyword(s):

Semantic Similarity ◽

Similarity Measure ◽

Experimental Evaluation ◽

Pearson Correlation ◽

Similarity Measures ◽

Similarity Score ◽

Cosine Similarity ◽

Semantic Similarity Measure ◽

Rate Prediction ◽

Target User

A recommendation algorithm comprises of two important steps: 1) Predicting rates, and 2) Recommendation. Rate prediction is a cumulative function of the similarity score between two movies and rate history of those movies by other users. There are various methods for rate prediction such as weighted sum method, regression, deviation based etc. All these methods rely on finding similar items to the items previously viewed/rated by target user, with assumption that user tends to have similar rating for similar items. Computing the similarities can be done using various similarity measures such as Euclidian Distance, Cosine Similarity, Adjusted Cosine Similarity, Pearson Correlation, Jaccard Similarity etc. All of these well-known approaches calculate similarity score between two movies using simple rating based data. Hence, such similarity measures could not accurately model rating behavior of user. In this paper, we will show that the accuracy in rate prediction can be enhanced by incorporating ontological domain knowledge in similarity computation. This paper introduces a new ontological semantic similarity measure between two movies. For experimental evaluation, the performance of proposed approach is compared with two existing approaches: 1) Adjusted Cosine Similarity (ACS), and 2) Weighted Slope One (WSO) algorithm, in terms of two performance measures: 1) Execution time and 2) Mean Absolute Error (MAE). The open-source Movielens (ml-1m) dataset is used for experimental evaluation. As our results show, the ontological semantic similarity measure enhances the performance of rate prediction as compared to the existing-well known approaches.

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HESML: a real-time semantic measures library for the biomedical domain with a reproducible survey

BMC Bioinformatics ◽

10.1186/s12859-021-04539-0 ◽

2022 ◽

Vol 23 (1) ◽

Author(s):

Juan J. Lastra-Díaz ◽

Alicia Lara-Clares ◽

Ana Garcia-Serrano

Keyword(s):

Real Time ◽

Semantic Similarity ◽

Similarity Measure ◽

Shortest Path ◽

State Of The Art ◽

Biomedical Domain ◽

Snomed Ct ◽

Shortest Path Algorithm ◽

Semantic Similarity Measure ◽

Current State

Abstract Background Ontology-based semantic similarity measures based on SNOMED-CT, MeSH, and Gene Ontology are being extensively used in many applications in biomedical text mining and genomics respectively, which has encouraged the development of semantic measures libraries based on the aforementioned ontologies. However, current state-of-the-art semantic measures libraries have some performance and scalability drawbacks derived from their ontology representations based on relational databases, or naive in-memory graph representations. Likewise, a recent reproducible survey on word similarity shows that one hybrid IC-based measure which integrates a shortest-path computation sets the state of the art in the family of ontology-based semantic measures. However, the lack of an efficient shortest-path algorithm for their real-time computation prevents both their practical use in any application and the use of any other path-based semantic similarity measure. Results To bridge the two aforementioned gaps, this work introduces for the first time an updated version of the HESML Java software library especially designed for the biomedical domain, which implements the most efficient and scalable ontology representation reported in the literature, together with a new method for the approximation of the Dijkstra’s algorithm for taxonomies, called Ancestors-based Shortest-Path Length (AncSPL), which allows the real-time computation of any path-based semantic similarity measure. Conclusions We introduce a set of reproducible benchmarks showing that HESML outperforms by several orders of magnitude the current state-of-the-art libraries in the three aforementioned biomedical ontologies, as well as the real-time performance and approximation quality of the new AncSPL shortest-path algorithm. Likewise, we show that AncSPL linearly scales regarding the dimension of the common ancestor subgraph regardless of the ontology size. Path-based measures based on the new AncSPL algorithm are up to six orders of magnitude faster than their exact implementation in large ontologies like SNOMED-CT and GO. Finally, we provide a detailed reproducibility protocol and dataset as supplementary material to allow the exact replication of all our experiments and results.

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Toward New Method for Adaptive Learning

Learning Strategies and Constructionism in Modern Education Settings - Advances in Educational Technologies and Instructional Design ◽

10.4018/978-1-5225-5430-1.ch011 ◽

2018 ◽

pp. 177-196

Author(s):

Souhaib Aammou ◽

Youssef Jdidou ◽

Kaoutar El Bakkari

Keyword(s):

Learning Style ◽

Semantic Similarity ◽

Similarity Measure ◽

Adaptive Learning ◽

New Method ◽

Semantic Similarity Measure ◽

Educational Activities ◽

Content Model ◽

Best Fit ◽

Decision Engine

This chapter deals with the design, creation, and implementation of the content model of an adaptive system. The authors propose the use of a meta-ontology composed of three conceptual models. They also propose SCORM as a technical means for structuring ontology and resources. They also present the problem of adaptation as the search for the most relevant pedagogical sequence among the available ones. To evaluate this relevance, the authors propose to use an original semantic similarity measure. This allows one to measure a distance between each of the available sequences and the metadata vector returned by the decision engine. Thus, the authors recommend the educational activities that best fit the learner's learning style, as one would have thought.

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An Ontology-Based Semantic Similarity Measure Considering Multi-Inheritance in Biomedicine

Mathematical Problems in Engineering ◽

10.1155/2015/305369 ◽

2015 ◽

Vol 2015 ◽

pp. 1-9

Author(s):

Fengqin Yang ◽

Yuanyuan Xing ◽

Hongguang Sun ◽

Tieli Sun ◽

Siya Chen

Keyword(s):

Semantic Similarity ◽

Similarity Measure ◽

Word Sense Disambiguation ◽

Word Sense ◽

Snomed Ct ◽

Semantic Similarity Measure ◽

Multiple Inheritance ◽

Knowledge Resources ◽

Text Understanding ◽

Document Categorization

Computation of semantic similarity between words for text understanding is a vital issue in many applications such as word sense disambiguation, document categorization, and information retrieval. In recent years, different paradigms have been proposed to compute semantic similarity based on different ontologies and knowledge resources. In this paper, we propose a new similarity measure combining both superconcepts of the evaluated concepts and their common specificity feature. The common specificity feature considers the depth of the Least Common Subsumer (LCS) of two concepts and the depth of the ontology to obtain more semantic evidence. The multiple inheritance phenomenon in a large and complex taxonomy is taken into account by all superconcepts of the evaluated concepts. We evaluate and compare the correlation obtained by our measure with human scores against other existing measures exploiting SNOMED CT as the input ontology. The experimental evaluations show the applicability of the measure on different datasets and confirm the efficiency and simplicity of our proposed measure.

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