Ontology based Domain Dictionary

Kolle, Padmaja; Bhagat, Snehal; Zade, Shruti; Dand, Bhavik; Lifna, C. S.

doi:10.1109/icscet.2018.8537346

Cited by 4 publications

(2 citation statements)

References 9 publications

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“…In the future, such a dictionary is used to create user interfaces, instructions, in the development support process [2]. Automatic construction of domain-specific ontologies is a difficult task [3,4], since it requires extracting domain-specific terms from the body of documents and assigning them the corresponding labels of domain concepts. On the other hand, the DSA availability allows to solve many problems associated with document processing [5].…”

Section: Introductionmentioning

confidence: 99%

Development of methods for pre-clustering and virtual merging of short documents for building domain dictionaries

Kungurtsev¹,

Zinovatna²,

Potochniak³

et al. 2020

EEJET

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The aim of research is to improve the quality of domain dictionaries by expanding the corpus of the documents under study by using short documents. A document model is proposed that allows to define a short document and the need to combine it with other documents to highlight verbose terms. An algorithm for highlighting the substantive part of the document has been developed, since in a short document the heading and closing parts usually contain terms that are not related to the studied domain. A method for preliminary clustering of short documents to highlight verbose terms has been developed. The method is based on highlighting and counting occurrences of nouns (one-word terms) for all analyzed documents. The concept of document proximity is introduced, which is determined by the combination of two criteria: the relative number of matching terms and the relative frequency of occurrence of matching terms. The principle of grouping documents at the customer's site often does not correspond to the principles of grouping necessary for building a dictionary of the domain. In a short document, it is usually impossible to isolate a verbose term because the repetition of terms is very low. A method has been developed for virtual combining of short documents based on the principle of achieving the necessary repeatability of one-word terms. The merged document has the highest possible frequency of terms for the cluster it belongs to. At the same time, the original text of documents is preserved and the ability to associate the selected verbose term with those documents in which it is included. The experiment made it possible to find the best ratio for the elements of the document proximity coefficient and confirm the effectiveness of the proposed preliminary clustering method

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Section: Introductionmentioning

confidence: 99%

Development of methods for pre-clustering and virtual merging of short documents for building domain dictionaries

Kungurtsev¹,

Zinovatna²,

Potochniak³

et al. 2020

EEJET

View full text Add to dashboard Cite

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“…An ontologybased approach used to represent the vocabularies and relationships between semantic entities. Ontology describes the elements that exist in any field or area to represent semantic relation [10]- [14]. The contributions in this paper are divided into four main categories and can be summarized as follows:…”

Section: Introductionmentioning

confidence: 99%

Enhanced Search Engine Using Proposed Framework and Ranking Algorithm Based on Semantic Relations

et al. 2019

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Today, most users need search engines to facilitate search and information retrieval processes. Unfortunately, traditional search engines have a significant challenge that they should retrieve high-precision results for a specific unclear query at a minimum response time. Also, a traditional search engine cannot expand a small, ambiguous query based on the meaning of each keyword and their semantic relationship. Therefore, this paper proposes a comprehensive search engine framework that combines the benefits of both a keyword-based and a semantic ontology-based search engine. The main contributions of this work are developing an algorithm for ranking results based on fuzzy membership value and a mathematical model of exploring a semantic relationship between different keywords. In the conducting experiments, eight different test cases were implemented to evaluate the proposed system. Executed test cases have achieved a precision rate of 97% with appropriate response time compared to the relevant systems.

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