A semantic similarity adjusted document co-citation analysis: a case of tourism supply chain

Sanguri, Kamal; Bhuyan, Atanu; Patra, Sabyasachi

doi:10.1007/s11192-020-03608-0

Cited by 16 publications

(17 citation statements)

References 112 publications

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“…Sanguri et al [50] proposed a latent semantic analysis method to address the problems of dictionary-based semantic approaches. They extracted the latent information from the text and calculated the similarity of the document along with the co-citation relationship.…”

Section: Semantic-based Approachesmentioning

confidence: 99%

A Fast and Scalable Algorithm for Prior Art Search

Lee

Park

Lee³

2022

IEEE Access

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Companies and research institutes are acquiring monopolized technologies through patents to survive in a rapidly changing market. The applicant is guaranteed by law the exclusive right to the technology for a certain period. A prior art search is an essential task for this purpose. Therefore, the need for a successful prior art search is increasing. Previous studies have disadvantages because relevant fields are limited, requiring an in-depth understanding of specific technologies. This paper proposes a method to overcome these limitations using patent text and citation information. The proposed method searches for prior art using a citation network based on the shortest path derived from a breadth-first search. We examined based on experiments whether the proposed method could be applied to the industry. Based on the results, the proposed algorithm can efficiently search for prior art. Furthermore, it was possible to construct a counterstrategy based on the characteristics of the applicant. The code will be made available at github.com/lee-ju/k_step_PIC.

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Section: Semantic-based Approachesmentioning

confidence: 99%

A Fast and Scalable Algorithm for Prior Art Search

Lee

Park

Lee³

2022

IEEE Access

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“…More recent studies have brought about improvements in the results of co-citation analysis using new ideas and techniques, namely, semantic similarity at different proximity levels (Sanguri et al ., 2020; Colavizza et al , 2018; Hsiao and Chen, 2017), probability of co-citation (Rodriguez-Prieto et al , 2019) and reconstructing raw co-citation matrices based on document unit counts and keywords of references (Wang et al ., 2018).…”

Section: Literature Reviewmentioning

confidence: 99%

Modeling the co-citation dependence on semantic layers of co-cited documents

Yaghtin

Sotudeh

Nikseresht

et al. 2021

OIR

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PurposeCo-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/approachThe 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.FindingsThe 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/valueIt 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 reviewThe peer review history for this article is available at https://publons.com/publon/10.1108/OIR-04-2020-0126.

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“…Based on the above core formula, the similarity between two words or different texts is calculated according to the similarity of information between different texts. e corresponding calculation formula is shown in formula (2), in which the corresponding overall similarity is used as the reference basis of the semantic similarity algorithm and the corresponding Ha (W i , W j ) represents the set of shared information between different texts. From the corresponding calculation formula, when the corresponding text information is consistent, the corresponding similarity formula is 1.…”

Section: Deep Analysis Algorithm Of Language Structurementioning

confidence: 99%

“…At the same time, text as an important carrier of modern information data, it is very important and meaningful to deal with and analyze it efficiently and reasonably. In order to further improve the data processing rate and processing accuracy, text similarity processing technology emerges at the historic moment, and its corresponding semantic similarity processing algorithm is more necessary and meaningful [1,2]. e traditional semantic similarity algorithm is mainly used in intelligent data retrieval, automatic problem system of related data information, and corresponding text data retrieval system.…”

Section: Introductionmentioning

confidence: 99%

Improvement and Analysis of Semantic Similarity Algorithm Based on Linguistic Concept Structure

2021

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With the rapid development of information age, various social groups and corresponding institutions are producing a large amount of information data every day. For such huge data storage and identification, in order to manage such data more efficiently and reasonably, traditional semantic similarity algorithm emerges. However, the accuracy of the traditional semantic similarity algorithm is relatively low, and the convergence of corresponding algorithm is poor. Based on this problem, this paper starts with the conceptual structure of language, analyzes the depth of language structure and the distance between nodes, and analyzes the two levels as the starting point. For the information of a specific data resource description frame type, the weight of interconnected edges is used for impact analysis so as to realize the semantic similarity impact analysis of all information data. Based on the above improvements, this paper also systematically establishes the data information modeling process based on language conceptual structure and establishes the corresponding model. In the experimental part, the improved algorithm is simulated and analyzed. The simulation results show that compared with the traditional algorithm, the algorithm has obvious accuracy improvement.

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A semantic similarity adjusted document co-citation analysis: a case of tourism supply chain

Cited by 16 publications

References 112 publications

A Fast and Scalable Algorithm for Prior Art Search

A Fast and Scalable Algorithm for Prior Art Search

Modeling the co-citation dependence on semantic layers of co-cited documents

Improvement and Analysis of Semantic Similarity Algorithm Based on Linguistic Concept Structure

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