Semantic retrieval of engineering knowledge is crucial in various engineering activities, such as process development and product model planning. To make a solution for this issue, previously, a few word-based semantic enabling existing approaches such as Lexical Retrieval Model with Semantic Residual Embedding's (LR-SRE), Document Retrieval Model through Semantic Linking (DR-MTS), Semantic Term Matching in Axiomatic Approaches to Information Retrieval (STM-IR) and Hybrid Ontology for Semantic Information Retrieval Model using Keyword Matching Indexing System (HOS-IR) were utilized. But, they all have shown less query-based accuracy results than the required value. In our proposed Information Retrieval (IR) design, the semantic knowledge-based retrieval scheme has been implemented. For query, entered by a user and processed for finding the dominated word. Word is then compared for its similarity equations, and similarity values are then computed to give output. Highly similar values are obtained as the class value. From class, the respective clusters are selected. Then, documents in that cluster are retrieved and ranked according to the relevance of the user. To support the accuracy level performance of this IR system, a word2vec model has been employed with the benefits of Horse Herd Optimization (HHO), which helps to extract vectors as features for classification. These results are stored as a .csv file for further retrieval. By implementing the proposed IR-word2vec model, the results showed that it outperforms other existing techniques by improved similarity index and accuracy for query results in an execution time of 1.7 s.

Anil Sharma

Suresh Kumar

Data & Knowledge Engineering

数据与知识工程 (DKE) 促进了这两个相关领域之间的思想交流和互动。 DKE 覆盖了全球范围内的研究人员、设计师、经理和用户。该杂志的主要目的是识别、调查和分析设计和有效使用这些系统的基本原则。

Data and Knowledge Engineering (DKE) facilitates the exchange of ideas and interaction between the two related fields of data and knowledge. The journal encompasses researchers, designers, managers, and users from around the globe. Its primary aim is to identify, investigate, and analyze the fundamental principles for designing and effectively utilizing these systems.

《数据与知识工程》(Data and Knowledge Engineering) 促进了数据与知识这两个相关领域之间的思想交流与互动。该期刊覆盖了全球范围内的研究人员、设计师、管理者和用户。其主要目的是识别、调查和分析设计及有效使用这些系统的基本原则。

Category	Quartile
计算机科学	3
计算机科学, 计算机人工智能	4
计算机科学, 计算机信息系统	4

Ontology-based semantic retrieval of documents using Word2vec model

ivySCI AI Smartly Parses PDF, Answers Researchers' Questions, and Helps You Understand Papers in Seconds

Journal Info

Category	Quartile
COMPUTER SCIENCE, INFORMATION SYSTEMS	2