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25 pages, 3225 KiB

Open AccessArticle

Ontology-Based Methodology for Knowledge Acquisition from Groupware

by Chukwudi Festus Uwasomba, Yunli Lee, Zaharin Yusoff and Teck Min Chin

Appl. Sci. 2022, 12(3), 1448; https://0-doi-org.brum.beds.ac.uk/10.3390/app12031448 - 29 Jan 2022

Cited by 2 | Viewed by 2478

Groupware exist, and they contain expertise knowledge (explicit and tacit) that is primarily for solving problems, and it is collected on-the-job through virtual teams; such knowledge should be harvested. A system to acquire on-the-job knowledge of experts from groupware in view of the [...] Read more.

Groupware exist, and they contain expertise knowledge (explicit and tacit) that is primarily for solving problems, and it is collected on-the-job through virtual teams; such knowledge should be harvested. A system to acquire on-the-job knowledge of experts from groupware in view of the enrichment of intelligent agents has become one of the important technologies that is very much in demand in the field of knowledge technology, especially in this era of textual data explosion including due to the ever-increasing remote work culture. Before acquiring new knowledge from sentences in groupware into an existing ontology, it is vital to process the groupware discussions to recognise concepts (especially new ones), as well as to find the appropriate mappings between the said concepts and the destination ontology. There are several mapping procedures in the literature, but these have been formulated on the basis of mapping two or more independent ontologies using concept-similarities and it requires a significant amount of computation. With the goal of lowering computational complexities, identification difficulties, and complications of insertion (hooking) of a concept into an existing ontology, this paper proposes: (1) an ontology-based framework with changeable modules to harvest knowledge from groupware discussions; and (2) a facts enrichment approach (FEA) for the identification of new concepts and the insertion/hooking of new concepts from sentences into an existing ontology. This takes into consideration the notions of equality, similarity, and equivalence of concepts. This unique approach can be implemented on any platform of choice using current or newly constructed modules that can be constantly revised with enhanced sophistication or extensions. In general, textual data is taken and analysed in view of the creation of an ontology that can be utilised to power intelligent agents. The complete architecture of the framework is provided and the evaluation of the results reveal that the proposed methodology performs significantly better compared to the universally recommended thresholds as well as the existing works. Our technique shows a notable high improvement on the F1 score that measures precision and recall. In terms of future work, the study recommends the development of algorithms to fully automate the framework as well as for harvesting tacit knowledge from groupware. Full article

(This article belongs to the Special Issue Ontology-Based Information Systems Establishment and Recent Development)

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30 pages, 15062 KiB

Open AccessArticle

Automatic Ontology-Based Model Evolution for Learning Changes in Dynamic Environments

by Roua Jabla, Maha Khemaja, Félix Buendia and Sami Faiz

Appl. Sci. 2021, 11(22), 10770; https://0-doi-org.brum.beds.ac.uk/10.3390/app112210770 - 15 Nov 2021

Cited by 5 | Viewed by 2053

Abstract

Knowledge engineering relies on ontologies, since they provide formal descriptions of real-world knowledge. However, ontology development is still a nontrivial task. From the view of knowledge engineering, ontology learning is helpful in generating ontologies semi-automatically or automatically from scratch. It not only improves [...] Read more.

Knowledge engineering relies on ontologies, since they provide formal descriptions of real-world knowledge. However, ontology development is still a nontrivial task. From the view of knowledge engineering, ontology learning is helpful in generating ontologies semi-automatically or automatically from scratch. It not only improves the efficiency of the ontology development process but also has been recognized as an interesting approach for extending preexisting ontologies with new knowledge discovered from heterogenous forms of input data. Driven by the great potential of ontology learning, we present an automatic ontology-based model evolution approach to account for highly dynamic environments at runtime. This approach can extend initial models expressed as ontologies to cope with rapid changes encountered in surrounding dynamic environments at runtime. The main contribution of our presented approach is that it analyzes heterogeneous semi-structured input data for learning an ontology, and it makes use of the learned ontology to extend an initial ontology-based model. Within this approach, we aim to automatically evolve an initial ontology-based model through the ontology learning approach. Therefore, this approach is illustrated using a proof-of-concept implementation that demonstrates the ontology-based model evolution at runtime. Finally, a threefold evaluation process of this approach is carried out to assess the quality of the evolved ontology-based models. First, we consider a feature-based evaluation for evaluating the structure and schema of the evolved models. Second, we adopt a criteria-based evaluation to assess the content of the evolved models. Finally, we perform an expert-based evaluation to assess an initial and evolved models’ coverage from an expert’s point of view. The experimental results reveal that the quality of the evolved models is relevant in considering the changes observed in the surrounding dynamic environments at runtime. Full article

(This article belongs to the Special Issue Ontology-Based Information Systems Establishment and Recent Development)

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20 pages, 3308 KiB

Open AccessArticle

An Ontology-Based Expert System for Rice Disease Identification and Control Recommendation

by Watanee Jearanaiwongkul, Chutiporn Anutariya, Teeradaj Racharak and Frederic Andres

Appl. Sci. 2021, 11(21), 10450; https://0-doi-org.brum.beds.ac.uk/10.3390/app112110450 - 07 Nov 2021

Cited by 5 | Viewed by 2401

Abstract

A great deal of information related to rice cultivation has been published on the web. Conventionally, this information is studied by end-users to identify pests, and to prevent production losses from rice diseases. Despite its benefits, such information has not yet been encoded [...] Read more.

A great deal of information related to rice cultivation has been published on the web. Conventionally, this information is studied by end-users to identify pests, and to prevent production losses from rice diseases. Despite its benefits, such information has not yet been encoded in a machine-processable form. This research closes the gap by modeling the knowledge-bases using ontologies and semantic technologies. Our modeled ontologies are externalized from existing reliable sources only, and offer axioms that describe abnormal appearances in rice diseases (and insects) and the corresponding controls. In addition, we developed an expert system called RiceMan, based on our ontologies, to support technical and non-technical users for diagnosing problems from observed abnormalities. We also introduce a composition procedure that aggregates users’ observation data with others for realizing spreadable diseases. This procedure, together with ontology reasoning, lies at the heart of our methodology. Finally, we evaluate our methodology practically with four groups of stakeholders in Thailand: senior agronomists, junior agronomists, agricultural students, and ontology specialists. Both ontologies and RiceMan are evaluated to verify their correctness, usefulness, and usability in various aspects. Our experimental results show that ontology reasoning is a promising approach for this domain problem. Full article

(This article belongs to the Special Issue Ontology-Based Information Systems Establishment and Recent Development)

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20 pages, 4522 KiB

Open AccessArticle

Semantic IFC Data Model for Automatic Safety Risk Identification in Deep Excavation Projects

by Yongcheng Zhang, Xuejiao Xing and Maxwell Fordjour Antwi-Afari

Appl. Sci. 2021, 11(21), 9958; https://0-doi-org.brum.beds.ac.uk/10.3390/app11219958 - 25 Oct 2021

Cited by 4 | Viewed by 2342

Abstract

Safety risk identification throughout deep excavation construction is an information-intensive task, involving construction information scattered in project planning documentation and dynamic information obtained from different field sensors. However, inefficient information integration and exchange have been an important obstacle to the development of automatic [...] Read more.

Safety risk identification throughout deep excavation construction is an information-intensive task, involving construction information scattered in project planning documentation and dynamic information obtained from different field sensors. However, inefficient information integration and exchange have been an important obstacle to the development of automatic safety risk identification in actual applications. This research aims to achieve the requirements for information integration and exchange by developing a semantic industry foundation classes (IFC) data model based on a central database of Building Information Modeling (BIM) in dynamic deep excavation process. Construction information required for risk identification in dynamic deep excavation is analyzed. The relationships among construction information are identified based on the semantic IFC data model, involved relationships (i.e., logical relationships and constraints among risk events, risk factors, construction parameters, and construction phases), and BIM elements. Furthermore, an automatic safety risk identification approach is presented based on the semantic data model, and it is tested through a construction risk identification prototype established under the BIM environment. Results illustrate the effectiveness of the BIM-based central database in accelerating automatic safety risk identification by linking BIM elements and required construction information corresponding to the dynamic construction process. Full article

(This article belongs to the Special Issue Ontology-Based Information Systems Establishment and Recent Development)

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20 pages, 5600 KiB

Open AccessArticle

Ontology-Based Framework for Cooperative Learning of 3D Object Recognition

by Parkpoom Chaisiriprasert, Karn Yongsiriwit, Matthew N. Dailey and Chutiporn Anutariya

Appl. Sci. 2021, 11(17), 8080; https://0-doi-org.brum.beds.ac.uk/10.3390/app11178080 - 31 Aug 2021

Cited by 2 | Viewed by 2055

Abstract

Advanced service robots are not, as of yet, widely adopted, partly due to the effectiveness of robots’ object recognition capabilities, the issue of object heterogeneity, a lack of knowledge sharing, and the difficulty of knowledge management. To encourage more widespread adoption of service [...] Read more.

Advanced service robots are not, as of yet, widely adopted, partly due to the effectiveness of robots’ object recognition capabilities, the issue of object heterogeneity, a lack of knowledge sharing, and the difficulty of knowledge management. To encourage more widespread adoption of service robots, we propose an ontology-based framework for cooperative robot learning that takes steps toward solving these problems. We present a use case of the framework in which multiple service robots offload compute-intensive machine vision tasks to cloud infrastructure. The framework enables heterogeneous 3D object recognition with the use of ontologies. The main contribution of our proposal is that we use the Unified Robot Description Format (URDF) to represent robots, and we propose the use of a new Robotic Object Description (ROD) ontology to represent the world of objects known by the collective. We use the WordNet database to provide a common understanding of objects across various robotic applications. With this framework, we aim to give a widely distributed group of robots the ability to cooperatively learn to recognize a variety of 3D objects. Different robots and different robotic applications could share knowledge and benefit from the experience of others via our framework. The framework was validated and then evaluated using a proof-of-concept, including a Web application integrated with the ROD ontology and the WordNet API for semantic analysis. The evaluation demonstrates the feasibility of using an ontology-based framework and using the Ontology Web Language (OWL) to provide improved knowledge management while enabling cooperative learning between multiple robots. Full article

(This article belongs to the Special Issue Ontology-Based Information Systems Establishment and Recent Development)

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25 pages, 1149 KiB

Open AccessArticle

Research of Personalized Recommendation Technology Based on Knowledge Graphs

by Xu Yang, Ziyi Huan, Yisong Zhai and Ting Lin

Appl. Sci. 2021, 11(15), 7104; https://0-doi-org.brum.beds.ac.uk/10.3390/app11157104 - 31 Jul 2021

Cited by 12 | Viewed by 3015

Abstract

Nowadays, personalized recommendation based on knowledge graphs has become a hot spot for researchers due to its good recommendation effect. In this paper, we researched personalized recommendation based on knowledge graphs. First of all, we study the knowledge graphs’ construction method and complete [...] Read more.

Nowadays, personalized recommendation based on knowledge graphs has become a hot spot for researchers due to its good recommendation effect. In this paper, we researched personalized recommendation based on knowledge graphs. First of all, we study the knowledge graphs’ construction method and complete the construction of the movie knowledge graphs. Furthermore, we use Neo4j graph database to store the movie data and vividly display it. Then, the classical translation model TransE algorithm in knowledge graph representation learning technology is studied in this paper, and we improved the algorithm through a cross-training method by using the information of the neighboring feature structures of the entities in the knowledge graph. Furthermore, the negative sampling process of TransE algorithm is improved. The experimental results show that the improved TransE model can more accurately vectorize entities and relations. Finally, this paper constructs a recommendation model by combining knowledge graphs with ranking learning and neural network. We propose the Bayesian personalized recommendation model based on knowledge graphs (KG-BPR) and the neural network recommendation model based on knowledge graphs (KG-NN). The semantic information of entities and relations in knowledge graphs is embedded into vector space by using improved TransE method, and we compare the results. The item entity vectors containing external knowledge information are integrated into the BPR model and neural network, respectively, which make up for the lack of knowledge information of the item itself. Finally, the experimental analysis is carried out on MovieLens-1M data set. The experimental results show that the two recommendation models proposed in this paper can effectively improve the accuracy, recall, F1 value and MAP value of recommendation. Full article

(This article belongs to the Special Issue Ontology-Based Information Systems Establishment and Recent Development)

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Review

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13 pages, 1358 KiB

Open AccessReview

Ontology-Based Regression Testing: A Systematic Literature Review

by Muhammad Hasnain, Imran Ghani, Muhammad Fermi Pasha and Seung-Ryul Jeong

Appl. Sci. 2021, 11(20), 9709; https://0-doi-org.brum.beds.ac.uk/10.3390/app11209709 - 18 Oct 2021

Cited by 5 | Viewed by 1982

Abstract

Web systems evolve by adding new functionalities or modifying them to meet users’ requirements. Web systems require retesting to ensure that existing functionalities are according to users’ expectations. Retesting a web system is challenging due to high cost and time consumption. Existing ‘systematic [...] Read more.

Web systems evolve by adding new functionalities or modifying them to meet users’ requirements. Web systems require retesting to ensure that existing functionalities are according to users’ expectations. Retesting a web system is challenging due to high cost and time consumption. Existing ‘systematic literature review’ (SLR) studies do not comprehensively present the ontology-based regression testing approaches. Therefore, this study focuses on ontology-based regression testing approaches because ontologies have been a growing research solution in regression testing. Following this, a systematic search of studies was performed using the “Preferred Reporting Items for Systematic Reviews and Meta-Analyses” (PRISMA) guidelines. A total of 24 peer-reviewed studies covering ontologies (semantic and inference rules) and regression testing, published between 2007 and 2019, were selected. The results showed that mainly ontology-based regression testing approaches were published in 2011–2012 and 2019 because ontology got momentum in research in other fields of study during these years. Furthermore, seven challenges to ontology-driven regression testing approaches are reported in the selected studies. Cost and validation are the main challenges examined in the research studies. The scalability of regression testing approaches has been identified as a common problem for ontology-based and other benchmark regression testing approaches. This SLR presents that the safety of critical systems is a possible future research direction to prevent human life risks. Full article

(This article belongs to the Special Issue Ontology-Based Information Systems Establishment and Recent Development)

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20 pages, 3412 KiB

Open AccessReview

A Systematic Mapping with Bibliometric Analysis on Information Systems Using Ontology and Fuzzy Logic

by Diana Kalibatiene and Jolanta Miliauskaitė

Appl. Sci. 2021, 11(7), 3003; https://0-doi-org.brum.beds.ac.uk/10.3390/app11073003 - 27 Mar 2021

Cited by 12 | Viewed by 3621

Abstract

The ontology-based information systems (IS) development is beneficial for analyzing, conceptual modeling, designing, and re-engineering complex IS to be semantically enriched and suitable for sophisticated reasoning on the IS content. On the other hand, fuzzy theory employment to handle uncertainty and fuzziness in [...] Read more.

The ontology-based information systems (IS) development is beneficial for analyzing, conceptual modeling, designing, and re-engineering complex IS to be semantically enriched and suitable for sophisticated reasoning on the IS content. On the other hand, fuzzy theory employment to handle uncertainty and fuzziness in IS becomes a hot topic in different practical domains, such as engineering, IS, computer sciences, etc. As such, ontology- and fuzzy-based IS are being developed. Consequently, there is a need to provide a comprehensive systematic mapping study (SMS) to build a structure on the ontology- and fuzzy-based IS field of interest and to grasp the main ideas. This paper presents findings of SMS, based on the papers extracted from Web of Science and Scopus and employing a bibliometric analysis tool to automate keyword mapping. We conclude this paper by summarizing the previous work and identifying possible research trends, which future investigations can extend. The main finding indicates that ontology and fuzzy logic contribute to ISs by expanding traditional IS to be intelligent IS, which is applicable for solving complex, fuzzy, and semantically rich (ontological) information collection, saving, processing, sharing, and reasoning in different application domains according to users’ needs in various countries. Full article

(This article belongs to the Special Issue Ontology-Based Information Systems Establishment and Recent Development)

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