Decision of Semantic Similarity Using Description Logic and Vector Weight between Concepts

2008 ◽  
Author(s):  
Su-Kyoung Kim ◽  
Ho-Jin Choi
Keyword(s):  
Semantic Similarity ◽  
Description Logic ◽  
Vector Weight

scholarly journals Toward semantic similarity measure between concepts in an ontology

2019 ◽  
Vol 14 (3) ◽  
pp. 1356
Author(s):  
Suwan Tongphu
Keyword(s):  
Semantic Similarity ◽  
Similarity Measure ◽  
Description Logic ◽  
Classical Problem ◽  
Similarity Score ◽  
New Method ◽  
Snomed Ct ◽  
Major Drawback ◽  
Semantic Similarity Measure ◽  
Concept Definition

<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>

scholarly journals Defining degree of semantic similarity using description logic tools

2021 ◽  
pp. 024-033
Author(s):  
O.V. Zakharova
Keyword(s):  
Semantic Similarity ◽  
Description Logic ◽  
Special Functions ◽  
Estimation Methods ◽  
Knowledge Based ◽  
Specific Concept ◽  
Semantic Proximity ◽  
Quantitative Indicators ◽  
The Impact

Establishing the semantic similarity of information is an integral part of the process of solving any information retrieval tasks, including tasks related to big data processing, discovery of semantic web services, categorization and classification of information, etc. The special functions to determine quantitative indicators of degree of semantic similarity of the information allow ranking the found information on its semantic proximity to the purpose or search request/template. Forming such measures should take into account many aspects from the meanings of the matched concepts to the specifics of the business-task in which it is done. Usually, to construct such similarity functions, semantic approaches are combined with structural ones, which provide syntactic comparison of concepts descriptions. This allows to do descriptions of the concepts more detail, and the impact of syntactic matching can be significantly reduced by using more expressive descriptive logics to represent information and by moving the focus to semantic properties. Today, DL-ontologies are the most developed tools for representing semantics, and the mechanisms of reasoning of descriptive logics (DL) provide the possibility of logical inference. Most of the estimates presented in this paper are based on basic DLs that support only the intersection constructor, but the described approaches can be applied to any DL that provides basic reasoning services. This article contains the analysis of existing approaches, models and measures based on descriptive logics. Classification of the estimation methods both on the levels of defining similarity and the matching types is proposed. The main attention is paid to establishing the similarity between concepts (conceptual level models). The task of establishing the value of similarity between instances and between concept and instance consists of finding the most specific concept for the instance / instances and evaluating the similarity between the concepts. The term of existential similarity is introduced. In this paper the examples of applying certain types of measures to evaluate the degree of semantic similarity of notions and/or knowledge based on the geometry ontology is demonstrated.

scholarly journals Defining degree of semantic similarity using description logic tools

2021 ◽  
pp. 016-026
Author(s):  
O.V. Zakharova ◽  
Keyword(s):  
Semantic Similarity ◽  
Description Logic ◽  
Special Functions ◽  
Estimation Methods ◽  
Knowledge Based ◽  
Specific Concept ◽  
Semantic Proximity ◽  
Quantitative Indicators ◽  
The Impact

Establishing the semantic similarity of information is an integral part of the process of solving any information retrieval tasks, including tasks related to big data processing, discovery of semantic web services, categorization and classification of information, etc. The special functions to determine quantitative indicators of degree of se­mantic similarity of the information allow ranking the found information on its semantic proximity to the pur­po­se or search request/template. Forming such measures should take into account many aspects from the mea­nings of the matched concepts to the specifics of the business-task in which it is done. Usually, to construct such si­milarity functions, semantic ap­proaches are combined with structural ones, which provide syntactic comparison of concepts descriptions. This allows to do descriptions of the concepts more detail, and the impact of syntactic matching can be significantly reduced by using more expressive descriptive logics to represent information and by moving the focus to semantic properties. Today, DL-ontologies are the most developed tools for representing semantics, and the mechanisms of reasoning of descriptive logics (DL) provide the possibility of logical inference. Most of the estimates presented in this paper are based on basic DLs that support only the intersection constructor, but the described approaches can be applied to any DL that provides basic reasoning services. This article contains the analysis of existing approaches, models and measures based on descriptive logics. Classification of the estimation methods both on the levels of defining similarity and the matching types is proposed. The main attention is paid to establishing the similarity between concepts (conceptual level models). The task of establishing the value of similarity between instances and between concept and instance consists of finding the most specific concept for the instance / instances and evaluating the similarity between the concepts. The term of existential similarity is introduced. In this paper the examples of applying certain types of measures to evaluate the degree of semantic similarity of notions and/or knowledge based on the geometry ontology is demonstrated.

Document- and Keyword-based Author Co-citation Analysis

2018 ◽  
Vol 2 (2) ◽  
pp. 70-82 ◽  
Author(s):  
Binglu Wang ◽  
Yi Bu ◽  
Win-bin Huang
Keyword(s):  
Citation Analysis ◽  
Semantic Similarity ◽  
Method Validation ◽  
New Method ◽  
Global Network ◽  
Network Visualization ◽  
Knowledge Domain ◽  
Knowledge Domains ◽  
Domain Mapping ◽  
The Relationship

AbstractIn the field of scientometrics, the principal purpose for author co-citation analysis (ACA) is to map knowledge domains by quantifying the relationship between co-cited author pairs. However, traditional ACA has been criticized since its input is insufficiently informative by simply counting authors’ co-citation frequencies. To address this issue, this paper introduces a new method that reconstructs the raw co-citation matrices by regarding document unit counts and keywords of references, named as Document- and Keyword-Based Author Co-Citation Analysis (DKACA). Based on the traditional ACA, DKACA counted co-citation pairs by document units instead of authors from the global network perspective. Moreover, by incorporating the information of keywords from cited papers, DKACA captured their semantic similarity between co-cited papers. In the method validation part, we implemented network visualization and MDS measurement to evaluate the effectiveness of DKACA. Results suggest that the proposed DKACA method not only reveals more insights that are previously unknown but also improves the performance and accuracy of knowledge domain mapping, representing a new basis for further studies.

The Opposition of Surprisal and Semantic Similarity in the Prediction of Language Processing: Evidence from Eye-tracking Data

2020 ◽  
Author(s):  
Kun Sun
Keyword(s):  
Eye Tracking ◽  
Semantic Similarity ◽  
Cognitive Processing ◽  
Language Processing ◽  
Language Comprehension ◽  
Word Processing ◽  
Reading Time ◽  
Computational Models ◽  
Tracking Data ◽  
Dynamic Approach

Expectations or predictions about upcoming content play an important role during language comprehension and processing. One important aspect of recent studies of language comprehension and processing concerns the estimation of the upcoming words in a sentence or discourse. Many studies have used eye-tracking data to explore computational and cognitive models for contextual word predictions and word processing. Eye-tracking data has previously been widely explored with a view to investigating the factors that influence word prediction. However, these studies are problematic on several levels, including the stimuli, corpora, statistical tools they applied. Although various computational models have been proposed for simulating contextual word predictions, past studies usually preferred to use a single computational model. The disadvantage of this is that it often cannot give an adequate account of cognitive processing in language comprehension. To avoid these problems, this study draws upon a massive natural and coherent discourse as stimuli in collecting the data on reading time. This study trains two state-of-art computational models (surprisal and semantic (dis)similarity from word vectors by linear discriminative learning (LDL)), measuring knowledge of both the syntagmatic and paradigmatic structure of language. We develop a `dynamic approach' to compute semantic (dis)similarity. It is the first time that these two computational models have been merged. Models are evaluated using advanced statistical methods. Meanwhile, in order to test the efficiency of our approach, one recently developed cosine method of computing semantic (dis)similarity based on word vectors data adopted is used to compare with our `dynamic' approach. The two computational and fixed-effect statistical models can be used to cross-verify the findings, thus ensuring that the result is reliable. All results support that surprisal and semantic similarity are opposed in the prediction of the reading time of words although both can make good predictions. Additionally, our `dynamic' approach performs better than the popular cosine method. The findings of this study are therefore of significance with regard to acquiring a better understanding how humans process words in a real-world context and how they make predictions in language cognition and processing.

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