Search results

The purpose of this paper is to improve the conceptual-based search by incorporating structural ontological information such as concepts and relations. Generally, Semantic-based information retrieval aims to identify relevant information based on the meanings of the query terms or on the context of the terms and the performance of semantic information retrieval is carried out through standard measures-precision and recall. Higher precision leads to the (meaningful) relevant documents obtained and lower recall leads to the less coverage of the concepts.

In this paper, the authors enhance the existing ontology-based indexing proposed by Kohler et al., by incorporating sibling information to the index. The index designed by Kohler et al., contains only super and sub-concepts from the ontology. In addition, in our approach, we focus on two tasks; query expansion and ranking of the expanded queries, to improve the efficiency of the ontology-based search. The aforementioned tasks make use of ontological concepts, and relations existing between those concepts so as to obtain semantically more relevant search results for a given query.

The proposed ontology-based indexing technique is investigated by analysing the coverage of concepts that are being populated in the index. Here, we introduce a new measure called index enhancement measure, to estimate the coverage of ontological concepts being indexed. We have evaluated the ontology-based search for the tourism domain with the tourism documents and tourism-specific ontology. The comparison of search results based on the use of ontology “with and without query expansion” is examined to estimate the efficiency of the proposed query expansion task. The ranking is compared with the ORank system to evaluate the performance of our ontology-based search. From these analyses, the ontology-based search results shows better recall when compared to the other concept-based search systems. The mean average precision of the ontology-based search is found to be 0.79 and the recall is found to be 0.65, the ORank system has the mean average precision of 0.62 and the recall is found to be 0.51, while the concept-based search has the mean average precision of 0.56 and the recall is found to be 0.42.

When the concept is not present in the domain-specific ontology, the concept cannot be indexed. When the given query term is not available in the ontology then the term-based results are retrieved.

In addition to super and sub-concepts, we incorporate the concepts present in same level (siblings) to the ontological index. The structural information from the ontology is determined for the query expansion. The ranking of the documents depends on the type of the query (single concept query, multiple concept queries and concept with relation queries) and the ontological relations that exists in the query and the documents. With this ontological structural information, the search results showed us better coverage of concepts with respect to the query.

The purpose of this paper is to propose the Collaborative Search System that attempts to achieve collaboration by implicitly identifying and reflecting search behaviour of collaborators in an academic network that is automatically and dynamically formed. By using the constructed Collaborative Hit Matrix (CHM), results are obtained that are based on the search behaviour and earned preferences of specialist communities of researchers, which are relevant to the user's need and reduce the time spent on bad links.

By using the Digital Bibliography Library Project (DBLP), the research communities are formed implicitly and dynamically based on the users’ research presence in the search environment and in the publication scenario, which is also used to assign users’ roles and establish links between the users. The CHM, to store the hit count and hit list of page results for queries, is also constructed and updated after every search session to enhance the collaborative search among the researchers.

The implicit researchers community formation, the assignment and dynamic updating of roles of the researchers based on research, search presence and search behaviour on the web as well as the usage of these roles during Collaborative Web Search have highly improved the relevancy of results. The CHM that holds the collaborative responses provided by the researchers on the search query results to support searching distinguishes this system from others. Thus the proposed system considerably improves the relevancy and reduces the time spent on bad links, thus improving recall and precision.

The research findings illustrate the better performance of the system, by connecting researchers working in the same field and allowing them to help each other in a web search environment.

The primary aim of this study is to detail the use of soft computing techniques in business and management research. Its objectives are as follows: to conduct a comprehensive scientometric analysis of publications in the field of soft computing, to explore the evolution of keywords, to identify key research themes and latent topics and to map the intellectual structure of soft computing in the business literature.

This research offers a comprehensive overview of the field by synthesising 43 years (1980–2022) of soft computing research from the Scopus database. It employs descriptive analysis, topic modelling (TM) and scientometric analysis.

This study's co-citation analysis identifies three primary categories of research in the field: the components, the techniques and the benefits of soft computing. Additionally, this study identifies 16 key study themes in the soft computing literature using TM, including decision-making under uncertainty, multi-criteria decision-making (MCDM), the application of deep learning in object detection and fault diagnosis, circular economy and sustainable development and a few others.

This analysis offers a valuable understanding of soft computing for researchers and industry experts and highlights potential areas for future research.

This study uses scientific mapping and performance indicators to analyse a large corpus of 4,512 articles in the field of soft computing. It makes significant contributions to the intellectual and conceptual framework of soft computing research by providing a comprehensive overview of the literature on soft computing literature covering a period of four decades and identifying significant trends and topics to direct future research.

This study aims to the OQuaRE quality model to the developed methodology.

Ontologies are formal, well-defined and flexible representations of knowledge related to a specific domain. They provide the base to develop efficient and interoperable solutions. Hence, a proliferation of ontologies in many domains is unleashed. Then, it is necessary to define how to compare such ontologies to decide which one is the most suitable for the specific needs of users/developers. As the emerging development of ontologies, several studies have proposed criteria to evaluate them.

In a previous study, the authors propose a methodological process to qualitatively and quantitatively compare ontologies at Lexical, Structural and Domain Knowledge levels, considering correctness and quality perspectives. As the evaluation methods of the proposal are based on a golden-standard, it can be customized to compare ontologies in any domain.

To show the suitability of the proposal, the authors apply the methodological approach to conduct comparative studies of ontologies in two different domains, one in the robotic area, in particular for the simultaneous localization and mapping (SLAM) problem; and the other one, in the cultural heritage domain. With these cases of study, the authors demonstrate that with this methodological comparative process, we are able to identify the strengths and weaknesses of ontologies, as well as the gaps still needed to fill in the target domains.

Using these metrics and the quality model from OQuaRE, the authors are incorporating a standard of software engineering at the quality validation into the Semantic Web.

The purpose of this paper is not only to design a more effective recommendation system for libraries, but also to eliminate many of the weaknesses found in the existing library recommender systems.

A novel library recommender system was designed for English collections by integrating personal ontology model and collaborative filtering model with domain specification.

The trend of the traditional library is evolving toward that of digital library. The personal ontology recommender (PORE) system offers a friendly user interface and provides several personalized services.

This system is only implemented and tested in the Library of National Chung Hsing University in Taiwan.

The paper demonstrates a good methodology to offer an active, effective, and personalized recommendation system for library patrons.

Genetics, a discipline of biology, is one of the most recent and rapidly advancing disciplines in science. This study aims to present a bibliometric analysis of the genetics research output of Iranian authors, map the intellectual structure of these studies and investigate the development path of this literature and the interrelationships among the main topics.

This study searched the Web of Science database for documentation of Iranian-published genetics research published up to 2020. Further, this study used HistCite software to profile and analyze the most cited articles and references and to draw their historiographies.

A database search revealed 21,329 documents that created the study population. The highest cited publications based on the Global Citation Score (GCS) and Local Citation Score (LCS) achieved scores of 602 and 47, respectively. The publication growth rate study demonstrated consistent expansion over time. The scientific maps based on LCS and GCS had five and four clusters, respectively. Furthermore, journal articles emerged as the predominant type of publication.

The significance of this study is in its contribution to understanding the genetics research position in Iran, informing policymakers and researchers, helping scientific collaboration and its impact on public attitudes and quality of life. The results of the present study, with benefits for various groups of communities, such as policymakers, academic groups and public society, can bridge the gap between theoretical research and practical implications.

The results of this study, by helping future advancement in health care, medical genetics and disease prevention, may have a direct and indirect positive influence on the quality of life. Furthermore, it may lead to more informed discussions on health care and biotechnology as well as influencing public attitudes and perceptions.

Ultimately, this study concludes that despite the proliferation of publications in terms of quantity and complexity, especially in areas such as disease diagnosis, prevention and treatment, there remains a need for more attention to other facets of genetics such as biology and biotechnology. Iranian publications are most related to population genetics, human genetics, molecular genetics, medical genetics, genomics, developmental genetics and evolutionary genetics out of 10 branches of genetics. This study reveals patterns in scientific outputs and authorship collaborations and plays an alternative and innovative role in revealing Iranian research trends in genetics.

The purpose of this study is to review the effect of usability factors on e-learning user relationships, namely, student–student interaction (SSI), student–instructor interaction (SII) and student–content interaction (SCI), in the existing e-learning literature. Further, this study intended to identify whether usability contributes to the satisfaction of e-learners.

This study has undertaken a systematic review using the PRISMA methodology to filter the literature in the domain of e-learning with respect to usability concerns using six databases. An analytical framework has been formulated to evaluate the literature against different dimensions of interactions and usability.

Results reveal that while SSI has grabbed 71.4 per cent research attention with respect to usability factors of e-learning systems, SCI has been given the least focus, i.e. 26.6 per cent. According to the results, e-learning systems’ usability issues influence the user relationships and affect the user satisfaction, which will lead to lack of user continuity.

The findings of this review will provide insights to instructional designers to construct more satisfied learning content for the users. The analysis framework of this study will encourage researchers to drive future research in e-learning along with the concern of usability.

This research emphasizes on the importance of SCI to focus future e-learning research on a different angle, in addition to SSI and SII. The analysis framework of this study will provide different dimensions, specifically for the empirical research in the domain of e-learning.

Social networks have changed the communication patterns significantly. Information available from different social networking sites can be well utilized for the analysis of users opinion. Hence, the organizations would benefit through the development of a platform, which can analyze public sentiments in the social media about their products and services to provide a value addition in their business process. Over the last few years, deep learning is very popular in the areas of image classification, speech recognition, etc. However, research on the use of deep learning method in sentiment analysis is limited. It has been observed that in some cases the existing machine learning methods for sentiment analysis fail to extract some implicit aspects and might not be very useful. Therefore, we propose a deep learning approach for aspect extraction from text and analysis of users sentiment corresponding to the aspect. A seven layer deep convolutional neural network (CNN) is used to tag each aspect in the opinionated sentences. We have combined deep learning approach with a set of rule-based approach to improve the performance of aspect extraction method as well as sentiment scoring method. We have also tried to improve the existing rule-based approach of aspect extraction by aspect categorization with a predefined set of aspect categories using clustering method and compared our proposed method with some of the state-of-the-art methods. It has been observed that the overall accuracy of our proposed method is 0.87 while that of the other state-of-the-art methods like modified rule-based method and CNN are 0.75 and 0.80 respectively. The overall accuracy of our proposed method shows an increment of 7–12% from that of the state-of-the-art methods.

This article introduces a model of knowledge creation in consciousness, the creation of explicit knowledge in six forms and its register and organisation in documents.

Assuming the premise of three realms of reference to knowledge and two forms of reference to entities, this article, through a phenomenological perspective, deduces a model of the creation of knowledge in consciousness and the creation of explicit knowledge in six forms and its register in documents.

Two basic types of knowledge are introduced: situated knowledge and theoretical/normative knowledge. Considering three realms of reference of knowledge – the space–time realm, subjectivity realm and linguistic realm – six general types of knowledge are deduced. Finally, three layers of knowledge organisation are presented: classification and mapping documents, theoretical/normative documents and documents of situations.

This article can contribute to the development of more efficient forms of creation of explicit knowledge, its register in documents and the development of more efficient knowledge organisation and management systems.

Relying on established perspectives of the realms where subjectivity is immersed, this article discusses how knowledge is created in consciousness and registered in documents. It also presents a novel perspective of types of knowledge through the combination of dimensions, realms of reference and forms of reference to entities.

The purpose of this paper is to enhance the line congestion and to minimize power loss. Transmission line congestion is considered the most acute trouble during the operation of the power system. Therefore, congestion management acts as an effective tool in using the available power without breaking the system hindrances or limitations.

Over the past few years, determining the optimal location and size of the devices have pinched a great deal of consideration. Numerous approaches have been established to mitigate the congestion rate, and this paper aims to enhance the line congestion and minimize power loss by determining the compensation rate and optimal location of a thyristor-switched capacitor (TCSC) using adaptive moth swarm optimization (AMSO) algorithm.

An AMSO algorithm uses the performances of moth flame and the chaotic local search-based shrinking scheme of the bacterial foraging optimization algorithm. The proposed AMSO approach is executed and discussed for the IEEE-30 bus system for determining the optimal location of single TCSC and dual TCSC.

In addition to this, the proposed algorithm is compared with various other existing approaches, and the results thus obtained provide better performances than other techniques.