Information Content Based Semantic Similarity Approaches for Multiple Biomedical Ontologies

Saruladha, K.; Aghila, G.; Bhuvaneswary, A.

doi:10.1007/978-3-642-22714-1_34

Cited by 7 publications

(10 citation statements)

References 8 publications

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“…According to [2] by using more than one ontology, it provides additional knowledge that improves the similarity study and solves all cases where the terms are not represented in an ontology. Therefore, our method is an essential tool that can be used by ontology developers to evaluate several ontologies in the decision process.…”

Section: Discussionmentioning

confidence: 99%

“…His domain is agreed between several agents, whose agreement in turn facilitates re-liable, meaningful communications. All these communications in turn lead to other benefits such as interoperability, reuse and sharing [1] [2]. Therefore, communication or sharing of information and knowledge between different concepts has become a fundamental trend in computational linguistics [3].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

An Information Content and Set of Common Superconcepts-Based Algorithm to Estimate Similarity between Concepts of Ontologies

Gbame,

Morie,

Brou

2023

OJAppS

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Ontologies have been used for several years in life sciences to formally represent concepts and reason about knowledge bases in domains such as the semantic web, information retrieval and artificial intelligence. The exploration of these domains for the correspondence of semantic content requires calculation of the measure of semantic similarity between concepts. Semantic similarity is a measure on a set of documents, based on the similarity of their meanings, which refers to the similarity between two concepts belonging to one or more ontologies. The similarity between concepts is also a quantitative measure of information, calculated based on the properties of concepts and their relationships. This study proposes a method for finding similarity between concepts in two different ontologies based on feature, information content and structure. More specifically, this means proposing a hybrid method using two existing measures to find the similarity between two concepts from different ontologies based on information content and the set of common superconcepts, which represents the set of common parent concepts. We simulated our method on datasets. The results show that our measure provides similarity values that are better than those reported in the literature.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An Information Content and Set of Common Superconcepts-Based Algorithm to Estimate Similarity between Concepts of Ontologies

Gbame,

Morie,

Brou

2023

OJAppS

View full text Add to dashboard Cite

show abstract

“…Moreover, Saruladha et al (2011) [77] described a computational method for evaluating the semantic similarity between distinct and independent ontology concepts without constructing a common conceptualization. The investigation examined the possibility of adjusting procedures based on the information content of a single existing ontology to the proposed methods, comparing the semantic similarity of concepts from various ontologies.…”

Section: Semantic Similaritymentioning

confidence: 99%

A Conceptual Graph-Based Method to Compute Information Content

Quintero,

Torres-Ruiz,

Saldaña-Pérez

et al. 2023

Mathematics

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This research uses the computing of conceptual distance to measure information content in Wikipedia categories. The proposed metric, generality, relates information content to conceptual distance by determining the ratio of the information that a concept provides to others compared to the information that it receives. The DIS-C algorithm calculates generality values for each concept, considering each relationship’s conceptual distance and distance weight. The findings of this study are compared to current methods in the field and found to be comparable to results obtained using the WordNet corpus. This method offers a new approach to measuring information content applied to any relationship or topology in conceptualization.

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“…(e.g., MeSH, Snomed-CT) [11], and the International Classification Disease (ICD) family [6]. These ontologies are specifically created for the bio-medical domain that is different from WordNet.…”

Section: International Journal On Informatics Visualization Vol 1 (20mentioning

confidence: 99%

X-Similarity Comparison by using Wordnet

Kasim¹,

Omar²,

Akbar³

et al. 2017

JOIV : Int. J. Inform. Visualization

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Abstract-Semantic web is an addition of the previous one that represents information more significantly for humans and computers. It enables the description of contents and services in machine readable form. It also enables annotating, discovering, publishing, advertising and composing services to be programmed. Semantic web was developed based on Ontology which is measured as the backbone of the semantic web. Machine-readable is transformed to ma-chine-understandable in the current web. Moreover, Ontology provides a common vocabulary, a grammar for pub-lishing data and can provide a semantic description of data which can be used to conserve the Ontology and keep them ready for implication. There are many that used in feature based in semantic similarity. This research presents a single ontology of X-Similarity feature based method.

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Information Content Based Semantic Similarity Approaches for Multiple Biomedical Ontologies

Cited by 7 publications

References 8 publications

An Information Content and Set of Common Superconcepts-Based Algorithm to Estimate Similarity between Concepts of Ontologies

An Information Content and Set of Common Superconcepts-Based Algorithm to Estimate Similarity between Concepts of Ontologies

A Conceptual Graph-Based Method to Compute Information Content

X-Similarity Comparison by using Wordnet

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