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Article
Publication date: 7 August 2017

Xiaolan Cui, Shuqin Cai and Yuchu Qin

The purpose of this paper is to propose a similarity-based approach to accurately retrieve reference solutions for the intelligent handling of online complaints.

Abstract

Purpose

The purpose of this paper is to propose a similarity-based approach to accurately retrieve reference solutions for the intelligent handling of online complaints.

Design/methodology/approach

This approach uses a case-based reasoning framework and firstly formalizes existing online complaints and their solutions, new online complaints, and complaint products, problems and content as source cases, target cases and distinctive features of each case, respectively. Then the process of using existing word-level, sense-level and text-level measures to assess the similarities between complaint products, problems and contents is explained. Based on these similarities, a measure with high accuracy in assessing the overall similarity between cases is designed. The effectiveness of the approach is evaluated by numerical and empirical experiments.

Findings

The evaluation results show that a measure simultaneously considering the features of similarity at word, sense and text levels can obtain higher accuracy than those measures that consider only one level feature of similarity; and that the designed measure is more accurate than all of its linear combinations.

Practical implications

The approach offers a feasible way to reduce manual intervention in online complaint handling. Complaint products, problems and content should be synthetically considered when handling an online complaint. The designed procedure of the measure with high accuracy can be applied in other applications that consider multiple similarity features or linguistic levels.

Originality/value

A method for linearly combining the similarities at all linguistic levels to accurately assess the overall similarities between online complaint cases is presented. This method is experimentally verified to be helpful to improve the accuracy of online complaint case retrieval. This is the first study that considers the accuracy of the similarity measures for online complaint case retrieval.

Details

Kybernetes, vol. 46 no. 7
Type: Research Article
ISSN: 0368-492X

Keywords

Article
Publication date: 11 July 2019

Manjula Wijewickrema, Vivien Petras and Naomal Dias

The purpose of this paper is to develop a journal recommender system, which compares the content similarities between a manuscript and the existing journal articles in two subject…

Abstract

Purpose

The purpose of this paper is to develop a journal recommender system, which compares the content similarities between a manuscript and the existing journal articles in two subject corpora (covering the social sciences and medicine). The study examines the appropriateness of three text similarity measures and the impact of numerous aspects of corpus documents on system performance.

Design/methodology/approach

Implemented three similarity measures one at a time on a journal recommender system with two separate journal corpora. Two distinct samples of test abstracts were classified and evaluated based on the normalized discounted cumulative gain.

Findings

The BM25 similarity measure outperforms both the cosine and unigram language similarity measures overall. The unigram language measure shows the lowest performance. The performance results are significantly different between each pair of similarity measures, while the BM25 and cosine similarity measures are moderately correlated. The cosine similarity achieves better performance for subjects with higher density of technical vocabulary and shorter corpus documents. Moreover, increasing the number of corpus journals in the domain of social sciences achieved better performance for cosine similarity and BM25.

Originality/value

This is the first work related to comparing the suitability of a number of string-based similarity measures with distinct corpora for journal recommender systems.

Details

The Electronic Library , vol. 37 no. 3
Type: Research Article
ISSN: 0264-0473

Keywords

Article
Publication date: 1 February 2016

Maciej Tabaszewski and Czeslaw Cempel

The observed diagnostic symptoms are often characterized by local fluctuations of their values. Hence, instead of direct observation of symptoms it is worth observing their grey…

Abstract

Purpose

The observed diagnostic symptoms are often characterized by local fluctuations of their values. Hence, instead of direct observation of symptoms it is worth observing their grey models and research similarity between life curves, which can enable to guess the nature of wear. The purpose of this paper is to find useful measures of similarity of diagnostics symptoms modeled by GM(1,1).

Design/methodology/approach

Measures of similarity may be used to determine the character of wear of the diagnosed object by way of comparison with known examples, which have previously been obtained and identified. A methodology for creation of such comparisons based on pre-smoothing by means of a GM(1,1) model with rolling window has been proposed. The process of smoothing enables to eliminate local fluctuations of a symptom. Their existence makes it difficult to compare symptoms. Application of a rolling window enables in turn to map the symptom properly, which may be difficult in the case of relatively short period of accelerated wear and changes of symptom values. To compare the life curves it is also necessary to normalize the life curves, so that they are represented by the same number of measurements (compression or extension of the measure of operation).

Findings

The paper concerns the similarity measures for symptom life curves obtained during vibration monitoring of fan mills working at a heat and power station. Similarity measures of symptoms were proposed and applied to the acquired data from the machines.

Practical implications

The method of symptom modeling and life curve comparing can be used to discover type of wear of the machine and eventually estimation of the remaining useful life.

Originality/value

The proposed method is very important for development of condition monitoring.

Details

Grey Systems: Theory and Application, vol. 6 no. 1
Type: Research Article
ISSN: 2043-9377

Keywords

Open Access
Article
Publication date: 26 November 2020

Bernadette Bouchon-Meunier and Giulianella Coletti

The paper is dedicated to the analysis of fuzzy similarity measures in uncertainty analysis in general, and in economic decision-making in particular. The purpose of this paper is…

1281

Abstract

Purpose

The paper is dedicated to the analysis of fuzzy similarity measures in uncertainty analysis in general, and in economic decision-making in particular. The purpose of this paper is to explain how a similarity measure can be chosen to quantify a qualitative description of similarities provided by experts of a given domain, in the case where the objects to compare are described through imprecise or linguistic attribute values represented by fuzzy sets. The case of qualitative dissimilarities is also addressed and the particular case of their representation by distances is presented.

Design/methodology/approach

The approach is based on measurement theory, following Tversky’s well-known paradigm.

Findings

A list of axioms which may or may not be satisfied by a qualitative comparative similarity between fuzzy objects is proposed, as extensions of axioms satisfied by similarities between crisp objects. They enable to express necessary and sufficient conditions for a numerical similarity measure to represent a comparative similarity between fuzzy objects. The representation of comparative dissimilarities is also addressed by means of specific functions depending on the distance between attribute values.

Originality/value

Examples of functions satisfying certain axioms to represent comparative similarities are given. They are based on the choice of operators to compute intersection, union and difference of fuzzy sets. A simple application of this methodology to economy is given, to show how a measure of similarity can be chosen to represent intuitive similarities expressed by an economist by means of a quantitative measure easily calculable. More detailed and formal results are given in Coletti and Bouchon-Meunier (2020) for similarities and Coletti et al. (2020) for dissimilarities.

Details

Asian Journal of Economics and Banking, vol. 4 no. 3
Type: Research Article
ISSN: 2615-9821

Keywords

Article
Publication date: 6 May 2014

Jin Zhang and Marcia Lei Zeng

– The purpose of this paper is to introduce a new similarity method to gauge the differences between two subject hierarchical structures.

Abstract

Purpose

The purpose of this paper is to introduce a new similarity method to gauge the differences between two subject hierarchical structures.

Design/methodology/approach

In the proposed similarity measure, nodes on two hierarchical structures are projected onto a two-dimensional space, respectively, and both structural similarity and subject similarity of nodes are considered in the similarity between the two hierarchical structures. The extent to which the structural similarity impacts on the similarity can be controlled by adjusting a parameter. An experiment was conducted to evaluate soundness of the measure. Eight experts whose research interests were information retrieval and information organization participated in the study. Results from the new measure were compared with results from the experts.

Findings

The evaluation shows strong correlations between the results from the new method and the results from the experts. It suggests that the similarity method achieved satisfactory results.

Practical implications

Hierarchical structures that are found in subject directories, taxonomies, classification systems, and other classificatory structures play an extremely important role in information organization and information representation. Measuring the similarity between two subject hierarchical structures allows an accurate overarching understanding of the degree to which the two hierarchical structures are similar.

Originality/value

Both structural similarity and subject similarity of nodes were considered in the proposed similarity method, and the extent to which the structural similarity impacts on the similarity can be adjusted. In addition, a new evaluation method for a hierarchical structure similarity was presented.

Details

Journal of Documentation, vol. 70 no. 3
Type: Research Article
ISSN: 0022-0418

Keywords

Article
Publication date: 12 May 2021

Maryam Yaghtin, Hajar Sotudeh, Alireza Nikseresht and Mahdieh Mirzabeigi

Co-citation frequency, defined as the number of documents co-citing two articles, is considered as a quantitative, and thus, an efficient proxy of subject relatedness or prestige…

Abstract

Purpose

Co-citation frequency, defined as the number of documents co-citing two articles, is considered as a quantitative, and thus, an efficient proxy of subject relatedness or prestige of the co-cited articles. Despite its quantitative nature, it is found effective in retrieving and evaluating documents, signifying its linkage with the related documents' contents. To better understand the dynamism of the citation network, the present study aims to investigate various content features giving rise to the measure.

Design/methodology/approach

The present study examined the interaction of different co-citation features in explaining the co-citation frequency. The features include the co-cited works' similarities in their full-texts, Medical Subject Headings (MeSH) terms, co-citation proximity, opinions and co-citances. A test collection is built using the CITREC dataset. The data were analyzed using natural language processing (NLP) and opinion mining techniques. A linear model was developed to regress the objective and subjective content-based co-citation measures against the natural log of the co-citation frequency.

Findings

The dimensions of co-citation similarity, either subjective or objective, play significant roles in predicting co-citation frequency. The model can predict about half of the co-citation variance. The interaction of co-opinionatedness and non-co-opinionatedness is the strongest factor in the model.

Originality/value

It is the first study in revealing that both the objective and subjective similarities could significantly predict the co-citation frequency. The findings re-confirm the citation analysis assumption claiming the connection between the cognitive layers of cited documents and citation measures in general and the co-citation frequency in particular.

Peer review

The peer review history for this article is available at https://publons.com/publon/10.1108/OIR-04-2020-0126.

Article
Publication date: 16 August 2019

Lunyan Wang, Qing Xia, Huimin Li and Yongchao Cao

The fuzziness and complexity of evaluation information are common phenomenon in practical decision-making problem, interval neutrosophic sets (INSs) is a power tool to deal with…

Abstract

Purpose

The fuzziness and complexity of evaluation information are common phenomenon in practical decision-making problem, interval neutrosophic sets (INSs) is a power tool to deal with ambiguous information. Similarity measure plays an important role in judging the degree between ideal and each alternative in decision-making process, the purpose of this paper is to establish a multi-criteria decision-making method based on similarity measure under INSs.

Design/methodology/approach

Based on an extension of existing cosine similarity, this paper first introduces an improved cosine similarity measure between interval neutosophic numbers, which considers the degrees of the truth membership, the indeterminacy membership and the falsity membership of the evaluation values. And then a multi-criteria decision-making method is established based on the improved cosine similarity measure, in which the ordered weighted averaging (OWA) is adopted to aggregate the neutrosophic information related to each alternative. Finally, an example on supplier selection is given to illustrate the feasibility and practicality of the presented decision-making method.

Findings

In the whole process of research and practice, it was realized that the application field of the proposed similarity measure theory still should be expanded, and the development of interval number theory is one of further research direction.

Originality/value

The main contributions of this paper are as follows: this study presents an improved cosine similarity measure under INSs, in which the weights of the three independent components of an interval number are taken into account; OWA are adopted to aggregate the neutrosophic information related to each alternative; and a multi-criteria decision-making method using the proposed similarity is developed under INSs.

Details

International Journal of Intelligent Computing and Cybernetics, vol. 12 no. 3
Type: Research Article
ISSN: 1756-378X

Keywords

Article
Publication date: 10 November 2020

Muzammil Khan, Sarwar Shah Khan, Arshad Ahmad and Arif Ur Rahman

The World Wide Web has become an essential platform for a news publication, and it has become one of the primary sources of information dissemination in the past few years…

Abstract

Purpose

The World Wide Web has become an essential platform for a news publication, and it has become one of the primary sources of information dissemination in the past few years. Electronic media, i.e., television channels, magazines and newspapers, have started publishing news online. This online information is prompt to be disappeared because of short life-span and imperative to be archived for the long-term and future generations. This paper presents a content-based similarity measure based on the headings of the news articles for linking digital news stories published in various newspapers during the preservation process that helps to ensure future accessibility.

Design/methodology/approach

To evaluate the accuracy and assess the effectiveness and worth of the proposed measure for linking news articles in Digital News Story Archive (DNSA), we adopted both, system-centric and user-centric (human judgment) evaluation over different datasets of news articles.

Findings

The proposed similarity measure is evaluated using different sizes of datasets, and the results are compared by both user-centric technique, i.e., expert judgment and system-centric techniques, i.e., cosine similarity measure, extended Jaccard measure and common ratio measure for stories (CRMS). The comparison helps to get a broader impact and can be helpful for generalization of the measure for different categories of news articles. Multiple experiments have conducted the findings of which showed that the measure presented viable results for national and international news, while best results for linking sports news articles during preservation based on headings.

Originality/value

The DNSA preserves a huge number of news articles from multiple news sources and to link with a vast collection, which encourages to introduce an efficient linking mechanism with few terms to manipulate. The CRMS is modified to deal with the headings of news articles as a part of the digital news stories preservation framework and comprehensively analysed.

Details

Library Hi Tech, vol. 40 no. 5
Type: Research Article
ISSN: 0737-8831

Keywords

Article
Publication date: 11 July 2019

M. Priya and Aswani Kumar Ch.

The purpose of this paper is to merge the ontologies that remove the redundancy and improve the storage efficiency. The count of ontologies developed in the past few eras is…

Abstract

Purpose

The purpose of this paper is to merge the ontologies that remove the redundancy and improve the storage efficiency. The count of ontologies developed in the past few eras is noticeably very high. With the availability of these ontologies, the needed information can be smoothly attained, but the presence of comparably varied ontologies nurtures the dispute of rework and merging of data. The assessment of the existing ontologies exposes the existence of the superfluous information; hence, ontology merging is the only solution. The existing ontology merging methods focus only on highly relevant classes and instances, whereas somewhat relevant classes and instances have been simply dropped. Those somewhat relevant classes and instances may also be useful or relevant to the given domain. In this paper, we propose a new method called hybrid semantic similarity measure (HSSM)-based ontology merging using formal concept analysis (FCA) and semantic similarity measure.

Design/methodology/approach

The HSSM categorizes the relevancy into three classes, namely highly relevant, moderate relevant and least relevant classes and instances. To achieve high efficiency in merging, HSSM performs both FCA part and the semantic similarity part.

Findings

The experimental results proved that the HSSM produced better results compared with existing algorithms in terms of similarity distance and time. An inconsistency check can also be done for the dissimilar classes and instances within an ontology. The output ontology will have set of highly relevant and moderate classes and instances as well as few least relevant classes and instances that will eventually lead to exhaustive ontology for the particular domain.

Practical implications

In this paper, a HSSM method is proposed and used to merge the academic social network ontologies; this is observed to be an extremely powerful methodology compared with other former studies. This HSSM approach can be applied for various domain ontologies and it may deliver a novel vision to the researchers.

Originality/value

The HSSM is not applied for merging the ontologies in any former studies up to the knowledge of authors.

Details

Library Hi Tech, vol. 38 no. 2
Type: Research Article
ISSN: 0737-8831

Keywords

Article
Publication date: 21 September 2012

Jorge Martinez‐Gil and José F. Aldana‐Montes

Semantic similarity measures are very important in many computer‐related fields. Previous works on applications such as data integration, query expansion, tag refactoring or text…

Abstract

Purpose

Semantic similarity measures are very important in many computer‐related fields. Previous works on applications such as data integration, query expansion, tag refactoring or text clustering have used some semantic similarity measures in the past. Despite the usefulness of semantic similarity measures in these applications, the problem of measuring the similarity between two text expressions remains a key challenge. This paper aims to address this issue.

Design/methodology/approach

In this article, the authors propose an optimization environment to improve existing techniques that use the notion of co‐occurrence and the information available on the web to measure similarity between terms.

Findings

The experimental results using the Miller and Charles and Gracia and Mena benchmark datasets show that the proposed approach is able to outperform classic probabilistic web‐based algorithms by a wide margin.

Originality/value

This paper presents two main contributions. The authors propose a novel technique that beats classic probabilistic techniques for measuring semantic similarity between terms. This new technique consists of using not only a search engine for computing web page counts, but a smart combination of several popular web search engines. The approach is evaluated on the Miller and Charles and Gracia and Mena benchmark datasets and compared with existing probabilistic web extraction techniques.

Details

Online Information Review, vol. 36 no. 5
Type: Research Article
ISSN: 1468-4527

Keywords

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