A fuzzy ontology framework in information retrieval using semantic query expansion

Jain, Saurabh; Seeja, K. R.; Jindal, Rajni

doi:10.1016/j.jjimei.2021.100009

Cited by 41 publications

(35 citation statements)

References 41 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In addition the WordNet API enhances the performance of SCBR. From the measurements our approach (SCBR) provide more accurate search results compare than other three models (26) and other (27) .…”

Section: Results Discussionmentioning

confidence: 96%

Refining Search Performance through Semantic based CBR Model and QoS Ranking Methodology

Palaniammal¹

2021

IJST

View full text Add to dashboard Cite

Objectives: To refine search performance using semantic web with an improved algorithm to retrieve the information efficiently. Methods: In order to establish the SCBR model and improve the performance of Web search, this paper adopts the Natural Language Processing (NLP) technology and the Quality of Service (QoS) ranking method, and endeavors to develop a relevant reliable and efficient search engine. Findings: Mean average precision tests revealed for quickness and precious of search results, and achieves the values from 82.98% to 99.53%. The experimental results show that the NLP technique improves the performance of SCBR model, and achieves higher average precision and recall values. Novelty: This research focuses to develop a related reliable and an efficient search engine to retrieve the accurate results for the user's complex query. It even bears the human error in typing, and suggests the expected word to search for. It also aims to retrieving the same result for synonym words which prevent the appearance of irrelevant search results.

show abstract

Section: Results Discussionmentioning

confidence: 96%

Refining Search Performance through Semantic based CBR Model and QoS Ranking Methodology

Palaniammal¹

2021

IJST

View full text Add to dashboard Cite

show abstract

“…Several authors have proposed extensions of object-oriented databases with fuzzy logic [7,8], for applications such as video application databases [9]; querying on fuzzy multivalued attributes [73]; reasoning on fuzzy object-oriented databases [74]; for deduction [75]; or to choose appropriate building elements in construction [76]. Fuzzy logic has also been proposed to assess the semantic relationships between types in ontologies [77,78] and the use of AND, or OR, operators on the specialization hierarchy [79]. The recursive process of comparing the structure of complex objects was shown by [38].…”

Section: Discussionmentioning

confidence: 99%

A Temporal Case-Based Reasoning Platform Relying on a Fuzzy Vector Spaces Object-Oriented Model and a Method to Design Knowledge Bases and Decision Support Systems in Multiple Domains

et al. 2022

View full text Add to dashboard Cite

Knowledge bases in complex domains must take into account many attributes describing numerous objects that are themselves components of complex objects. Temporal case-based reasoning (TCBR) requires comparing the structural evolution of component objects and their states (attribute values) at different levels of granularity. This paper provides some significant contributions to computer science. It extends a fuzzy vector space object-oriented model and method (FVSOOMM) to present a new platform and a method guideline capable of designing objects and attributes that represent timepoint knowledge objects. It shows how temporal case-based reasoning can use distances between temporal fuzzy vector functions to compare these knowledge objects’ evolution. It describes examples of interfaces that have been implemented on this new platform. These include an expert’s interface that describes a knowledge class diagram; a practitioner’s interface that instantiates domain objects and their attribute constraints; and an end-user interface to input attribute values of the real cases stored in a domain case database. This paper illustrates resultant knowledge bases in different domains, with examples of pulmonary embolism diagnosis in medicine and decision making in French municipal territorial recomposition. The paper concludes with the current limitations of the proposed model, its future perspectives and possible platform enhancements.

show abstract

“…Raza et al discussed that domain-specific ontologies are widely used in medicine, agriculture, and other scientific fields [ 4 ]. Multiple automated QE systems are proposed in IR [ 5 ]. Yunzhi et al constructed an Arabic ontology based on the Protégé and SPARQL language to extract candidate expansion terms [ 6 ].…”

Section: Literature Reviewmentioning

confidence: 99%

Managing and Retrieving Bilingual Documents Using Artificial Intelligence-Based Ontological Framework

Alothman

Sait

2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

In recent times, artificial intelligence (AI) methods have been applied in document and content management to make decisions and improve the organization’s functionalities. However, the lack of semantics and restricted metadata hinders the current document management technique from achieving a better outcome. E-Government activities demand a sophisticated approach to handle a large corpus of data and produce valuable insights. There is a lack of methods to manage and retrieve bilingual (Arabic and English) documents. Therefore, the study aims to develop an ontology-based AI framework for managing documents. A testbed is employed to simulate the existing and proposed framework for the performance evaluation. Initially, a data extraction methodology is utilized to extract Arabic and English content from 77 documents. Researchers developed a bilingual dictionary to teach the proposed information retrieval technique. A classifier based on the Naïve Bayes approach is designed to identify the documents’ relations. Finally, a ranking approach based on link analysis is used for ranking the documents according to the users’ queries. The benchmark evaluation metrics are applied to measure the performance of the proposed ontological framework. The findings suggest that the proposed framework offers supreme results and outperforms the existing framework.

show abstract

A fuzzy ontology framework in information retrieval using semantic query expansion

Cited by 41 publications

References 41 publications

Refining Search Performance through Semantic based CBR Model and QoS Ranking Methodology

Refining Search Performance through Semantic based CBR Model and QoS Ranking Methodology

A Temporal Case-Based Reasoning Platform Relying on a Fuzzy Vector Spaces Object-Oriented Model and a Method to Design Knowledge Bases and Decision Support Systems in Multiple Domains

Managing and Retrieving Bilingual Documents Using Artificial Intelligence-Based Ontological Framework

Contact Info

Product

Resources

About