Ontology-based Knowledge Management Research Articles

The impact of ontology-based knowledge management on improving tax accounting procedures and reducing tax risks

This paper aims to investigate the impact of ontology-based knowledge management on improving tax accounting procedures and reducing tax risks, specifically focusing on the income tax system in a developing country. To achieve this objective, a mixed methodology approach was adopted, combining quantitative and qualitative data collection and analysis methods. The data were gathered through document analysis, interviews, and questionnaires administered to stakeholders in the Egyptian Income Tax Authority. The findings of this study hold significance for both academic researchers and accounting practitioners. The results demonstrate numerous advantages associated with the utilization of ontology in tax management. It has the potential to reduce costs in tax advisory services, minimize errors in tax calculations, and enhance tax revenue generation by promoting transparency and accountability. The empirical analysis further confirms a positive correlation between ontology-based knowledge management and improved accountability procedures, as well as a positive correlation with the reduction of tax risks. These statistically significant relationships (r = 0.85, p < 0.01 and r = 0.79, p < 0.01, respectively) provide empirical evidence of the actual effects of implementing ontology-based knowledge management in improving tax accounting procedures and mitigating tax risks.

BIM and ontology-based knowledge management for dam safety monitoring

Dam Safety Monitoring Systems (DSMSs) are crucial in evaluating the operational state of dams. However, heterogeneous monitoring data distributed from multiple sources during the dam operation lacks a unified data integration method and prohibits knowledge extraction and intelligent analysis, which currently poses a labor-intensive task. To address this issue, a solution relying on Building Information Modeling (BIM) and domain ontologies is proposed. Specifically, a DSMS domain ontology (OntoDSMS) is developed by comprehensively collecting domain knowledge and extracting context information from the dam information model. Furthermore, a rule-based reasoner and SPARQL queries are implemented. The proposed approach facilitates the effective integration of dam safety monitoring information while reducing retrieval time effectively compared with traditional databases. A case is illustrated to demonstrate the feasibility and practicality of the proposed approach.

An Ontology-based Knowledge Mining Model for Effective Exploitation of Agro Information

The quality of agriculture depends on the quality of the yield, which is usually obtained through the well-being of the crop. The quality of any crop depends on the minerals in the soil, the type of soil, the location, and the seasons. The crop yield depends on soil fertility, availability of water, climate, and disease prevention. Although this information is prevailing in plenty among the expert farmers, the means of abducting the information to the future generation has not been much promoted. Hence, the knowledge disseminated regarding agriculture becomes scarce, affecting the entire agricultural process. Given these facts, a single source, strong knowledge management system is proposed to be designed. The system aims to embrace the different kinds of knowledge associated with agriculture and attempt to obtain a single source of agro information that is very much usable and reusable to the users. To ensure the maximum level of reusability, the knowledge of the domain needs to be modeled and represented in a way that is scalable and flexible. One of the knowledge representation techniques that emphasizes on reusability and scalability is ontology. Thus, this paper attempts to design an ontology-based agro knowledge management system. A rule base is constructed to improve the expressiveness of the knowledge. An incremental mining approach is adopted to extract the knowledge from multiple ontology. To understand better to aid decision-making, a visualization task is carried out. A multi ontology-based knowledge mining model is attempted in this research to provide better insight regarding agro knowledge.

A-SHIP: Ontology-Based Adaptive Sustainable Healthcare Insurance Policy

Healthcare has a significant impact on human capital anywhere. Countries usually allocate financial resources to manage healthcare, which might impose a substantial financial burden on the scope of healthcare coverage. Thus, the healthcare sector must provide the best possible services at the lowest cost. This significant challenge can only be achieved through applying appropriate policies and technologies, including those used by healthcare insurance policy providers. This paper proposes an innovative, customer-centric, sustainable healthcare insurance policy model. The main objective of this model is to sustain wellness by applying technologies to avoid illness and provide wellbeing for patients by empowering self-care remotely. The proposed solution uses an adaptive ontology-based knowledge management system to satisfy customers and market needs. The proposed system creates a customized policy that consists of various packages to match customers’ healthcare needs based on their health status. The system was tested and validated using a real dataset.

Ontology-Based Knowledge Management Tools for Knowledge Sharing in Organization—A Review

Knowledge management (KM) comprises several processes, and one of the most important is the knowledge sharing activities. The ability of an organization to manage its organizational knowledge, specifically in the context of knowledge sharing, may enhance the organization’s overall performance. Various approaches and technologies have been introduced to assist the process in achieving that target. Ontology as one of the knowledge representation methods has been becoming popular to assist knowledge sharing in the organization. Previous reviews have mainly focused on general KM issues, with little emphasis on the use of ontology in knowledge sharing. Thus, this article reviews several ontology-based KM tools that can support knowledge-sharing activities to provide some insight into future research in this area. Thirteen ontology-based KM tools were reviewed using ten elements’ comparison criteria: the motivation, domain, source of knowledge, type of knowledge, knowledge extraction, knowledge input process, knowledge retrieval process, knowledge sharing technology, source of ontology component, and ontology methodology. The review found that several elements can be further studied to improve KM implementation in the organization, especially on the knowledge sharing dimension. This includes simplifying the knowledge extraction and retrieval process to explore various knowledge domains from implicit knowledge sources. The review’s outcome also includes proposed components and functions of an ideal ontology-based KM tool.

Development of an Ontology-based Approach for Knowledge Management in Software Testing

Software development organizations are seeking to add quality to their products. Testing processes are strategic elements to manage projects and product quality. However, advances in technology and the emergence of increasingly critical applications make testing a complex task and large volumes of information are generated. In fact, software testing is a knowledge intensive process. Because of this, these organizations have shown a growing interest in Knowledge Management (KM) programs, which in turn support the improvement of testing procedures. KM emerges as a means to manage testing knowledge, and, consequently, to improve software quality. However, there are only a few KM solutions supporting software testing. This paper reports experiences from the development of an approach, called Ontology-based Testing Knowledge Management (OntoT-KM), that aims to assist in launching KM initiatives in the software testing domain with the support of Knowledge Management Systems (KMSs). OntoT-KM provides a process guiding how to start applying KM in software testing. OntoT-KM is based on the findings of a systematic mapping on KM in software testing and the results of a survey with testing practitioners. Moreover, OntoT-KM considers the conceptualization established by a Reference Ontology on Software Testing (ROoST). As a proof of concept, OntoT-KM was applied to develop a KMS called Testing KM Portal (TKMP), which was evaluated in terms of usefulness, usability, and functional correctness. Results show that the developed KMS from OntoT-KM is a potential system for managing knowledge in software testing, so, the approach can guide KM initiatives in software testing.

A Knowledge-Based Deep Learning Architecture for Aspect-Based Sentiment Analysis.

The task of sentiment analysis tries to predict the affective state of a document by examining its content and metadata through the application of machine learning techniques. Recent advances in the field consider sentiment to be a multi-dimensional quantity that pertains to different interpretations (or aspects), rather than a single one. Based on earlier research, the current work examines the said task in the framework of a larger architecture that crawls documents from various online sources. Subsequently, the collected data are pre-processed, in order to extract useful features that assist the machine learning algorithms in the sentiment analysis task. More specifically, the words that comprise each text are mapped to a neural embedding space and are provided to a hybrid, bi-directional long short-term memory network, coupled with convolutional layers and an attention mechanism that outputs the final textual features. Additionally, a number of document metadata are extracted, including the number of a document's repetitions in the collected corpus (i.e. number of reposts/retweets), the frequency and type of emoji ideograms and the presence of keywords, either extracted automatically or assigned manually, in the form of hashtags. The novelty of the proposed approach lies in the semantic annotation of the retrieved keywords, since an ontology-based knowledge management system is queried, with the purpose of retrieving the classes the aforementioned keywords belong to. Finally, all features are provided to a fully connected, multi-layered, feed-forward artificial neural network that performs the analysis task. The overall architecture is compared, on a manually collected corpus of documents, with two other state-of-the-art approaches, achieving optimal results in identifying negative sentiment, which is of particular interest to certain parties (like for example, companies) that are interested in measuring their online reputation.

Ontology-based knowledge management with verbal interaction for command interpretation and execution by home service robots

This paper describes a system for service robots that combines ontological knowledge reasoning and human–robot interaction to interpret natural language commands and successfully perform household chores, such as finding and delivering objects. Knowledge and context reasoning is essential for providing more efficient service robots, given their diverse and continuously changing environments. Moreover, since they are in contact with humans, robots require such skills as interaction and language. Therefore, we developed a system with specific modules to manage robots’ knowledge and reasoning, command analysis, decision-making, and talking interaction. The system relies on inference methods and verbal interaction to understand commands and clarify uncertain information. We tested our system inside a simulated environment where the robot receives commands with missing or unclear information. The system’s performance was compared with the average performance of human subjects who completed the same commands in the simulation.

Ontology-based knowledge management in income tax of Nepal

Organisational knowledge management in the government agencies is crucial to create a common understanding about data, system and procedures for stakeholders. Main purpose of this work is to create a knowledge repository for income tax of Nepal using domain ontology. To create this ontology, we have used 'Methontology' a broadly used ontology development methodology. After the knowledge acquisition; competency questions were created and conceptualisation was specified. A class hierarchy, object properties and data propertied are identified and specified. Further, ontology was created by using the Protégé tool. Consistency of the developed ontology was evaluated by correctly answering all the competency questions using SPARQL. The ontology can be used by the Inland Revenue Department as a stepping stone towards knowledge management through ontology. The work can be a foundation for the future works in this domain in good governance, data sharing, interoperability and transparency for the government of Nepal.

Ontology-based knowledge management in income tax of Nepal

Ontology-based knowledge management in income tax of Nepal

An ontology-based knowledge management approach supporting simulation-aided design for car crash simulation in the development phase

In the automotive industry, the design process is both costly and time-consuming. This research focuses on improving the design process by mainly reducing time while producing more robust vehicles. Vehicle development is based on simulation; thus, the design process is referred to as simulation-aided design. Engineering design is highly collaborative and knowledge intensive. Therefore, knowledge management plays a crucial role in today's global economy and is essential for the competitiveness of companies. However, current research on engineering knowledge management focuses on either the codification or the personalisation approaches of knowledge management. Thus, this paper addresses an integrated and collaborative approach. This paper aims to develop an ontology-based knowledge management approach to support simulation-aided design, specifically car crash simulation. The knowledge management support system is designed to ensure the capture and retrieval of engineering knowledge and to enable collaboration between different stakeholders. An evaluation of the models and technologies used is also undertaken, based on use case scenarios.

Semantic Web Ontologies based Knowledge Management Framework for IT Service Management

Technology driven organisations are investing hugely in training and knowledge enrichment of their employees. This is due to the fact that knowledge is now considered as an asset by organisations. Additionally, with emerging technologies, organisations are also spending heavily in Information and Communication Technology (ICT) to enhance their internal operations and processes. Among the various internal processes, Knowledge Management is an area which has been there since many years but when it is about the application of latest technology and innovations for Knowledge Management practices, there are huge opportunities. This paper presents an analysis of various KM frameworks available for different domains and based on current state and limitation identified, it proposes a Semantic Web Ontology based Knowledge Management System for IT Service Industry

Ontology-Based Knowledge Management System for Digital Highway Construction Inspection

Inspection is critical to ensuring the quality of infrastructure construction. In recent years, state highway agencies (SHAs) have been facing the challenge of a shortage of experienced inspectors due to retirements, workforce downsizing, and resignations to take jobs in the private sector. There is a critical need to retain and manage the accumulated construction inspection knowledge (what, when, and how to inspect) and integrate this knowledge into the construction business process. This paper presents an ontological approach to managing inspection knowledge and the development of a risk-based digital inspection system integrated with the construction process. The developed system consists of five knowledge compartments: inspection activities prioritized based on risk; activity-centered pay items; inspection check items; detailed checks that include priority, frequency, object, attribute, and acceptance criteria; and training materials (optional). This risk-based system is designed to align with the construction process by connecting an activity to a pay item and that pay item to a check item, resulting in an automatically generated comprehensive inspection checklist for the activity. The optional training materials are designed to train inspectors in real time on an as-needed basis. Since risk is embedded in every knowledge compartment, this system not only automates and retains the inspection knowledge but also enables SHAs to allocate their limited resources to the most critical items. This system is currently being implemented by the Indiana Department of Transportation as a digital inspection protocol integrated in its e-construction process.

Hybrid ontology-learning materials engineering system for pharmaceutical products: Multi-label entity recognition and concept detection

The dawn of a new era in knowledge management due to information explosion is making old habits of modeling knowledge and decision-making inadequate. In the search for new modeling paradigms, we expect ontologies to play a big role. One of the critical challenges we face is the scarcity of semantically rich, properly populated, ontologies in most application domains in chemical and materials engineering. Developing such ontologies is a very challenging task requiring considerable investment in time, effort, and expert knowledge. One needs automation tools that can assist an ontology engineer to quickly develop and curate domain-specific ontologies. We consider our conceptual framework in this paper, a general approach for populating scientific ontologies, and its implementation as the prototype HOLMES, as an early attempt towards such an automated knowledge management environment. Our approach integrates a variety of machine learning and natural language processing methods to extract information from journal articles and store them semantically in an ontology. In this work, identification of key terms (such as chemicals, drugs, processes, anatomical entities, etc.) from abstracts, and the classification of these terms into 25 classes are presented. Two methods, a multi-class classifier (SVM) and a multi-label classifier (HOMER), were tested on an annotated data set for the pharmaceutical industry. The test was done using two different versions of the same data set, one using the BIO notation and the other not. The F1 scores for HOMER, were better in the BIO notation (63.6% vs 48.5%) while SVM performed better in the non-BIO version (54.1% vs 53.2%). However, the standard metrics did not consider the effect of the multiple answers that the multi-label classifier is allowed to obtain. As the results of our computational experiments show, while the performance of multi-label classifier is encouraging, much more remains to be done in order to develop a practically viable automated ontology-based knowledge management system.

The Construction of an Ontology-Based Knowledge Management Model for Departure Procedures

With increased global economic development, the air traffic volume (including passengers and parcels) increases every year. Thus, the business-related load on air traffic controllers becomes heavier, which influences their need for frequent learning and training opportunities. Therefore, the only solution is to enhance the performance of the training for the air traffic controllers (ATCs) and to build uniform training procedures and standardized training content, which constitute a shared framework with ontology knowledge. Taking departure operation control processes as an example, the research analyzes an aviation organization framework, with information communication procedures and relevant knowledge assets from the viewpoint of departure processes, to explore a knowledge management model of aviation control. This research studies how explicit knowledge is transformed by CommonKADS methodology using the CommonKADS sheet, and the Unified Modeling Language (UML) graph presentation and ontology model are discussed in this research. This analysis procedure can be applied to the systematic analysis and establishment of similar business-based information systems, allowing this research result to be further shared in different industries

Application of Ontology in Emergency Plan Management of Metro Operation

Emergency plans play a key role in the emergency management of metro operation. Well-prepared emergency response supported by plans can greatly mitigate the significant impact of metro incidents. However, most emergency plans remain as plain-text documents, which make it difficult to conduct efficient administrative work such as plan generation, documentation and maintenance. Operational use of plans such as rapid knowledge retrieval and acquisition cannot be performed as well, affecting emergency training during the preparation process as well as plan review at the scene of the accident. Additionally, the knowledge codified in emergency plans is mostly depicted by texts, which are not vivid and intuitive enough to clearly convey the instructions of response procedures and relative information. In this paper, an ontology-based knowledge management method is proposed, and a unified and formalized plan repository is built to facilitate the efficient administrative and operational use of emergency plans. BIM technology is applied to provide realistic visualization of the plan knowledge for better understanding. A prototype of emergency plan training system, which integrates BIM and the ontology-based plan repository, has been developed to demonstrate the feasibility and effectiveness of the method. A case study illustrates the knowledge management process and shows how staff training can benefit from the system.

Ontology based Knowledge Management for Administrative Processes of University

Knowledge management is a challenging task especially in administrative processes with a typical workflow such as higher educational institutions and Universities. We have proposed a system aSPOCMS (An Agent-based Semantic Web for Paperless Office Content Management System) that aims at providing paperless environment for the typical workflows of the universities, which requires ontology based knowledge management to manage the files and documents of various departments and sections of a university. In Semantic Web, Ontology describes the concepts, relationships among the concepts and properties within their domain. It provides automatic inferring and interoperability between applications which is an appropriate vision for knowledge management. In this paper we discussed, how Semantic Web technology can be utilized in higher educational institution for knowledge representation of various resources and handling the task of administrative processes. This requires exploitation of knowledge of various resources such as department, school, section, file and employee etc. of the University by aSPOCMS which is built as an agent-based system using the ontology for communication between agent, user and for knowledge representation and management. Index Terms—Ontology, Knowledge Management, aSPOCMS, Workflow.

Automating design with intelligent human–machine integration

This paper reviews the state-of-the-art methodologies for automating design with intelligent human–machine integration from the perspectives of ontology and epistemology. The human–machine integrated automating design paradigm is reviewed systematically based on a proposed prototype of human–machine integrated design, from the aspects of ontology-based knowledge management with local-to-global ontology transitions, and epistemology-based upward-spiral cognitive process of coupled design ideation. Particularly, imaginal thinking frame is proposed as the foundation of intelligent human–machine interaction that puts human and machine on an equal platform. Further, this paper presents implementations and applications of the automating design paradigm and concludes with the identification of future trend.

DESIGN, BUILD AND EVALUATION OF AN ONTOLOGY-BASED KMS FOR SUPPORTING CMMI-DEV UNDERSTANDING: BENEFITS AND LIMITATIONS

The design, build and evaluation of an ontology-based knowledge management system prototype tool — entitled CMMIoKMS — for supporting the consultation and understanding of CMMI for Development (CMMI-DEV) is reported. Ontology-based tools have been proposed for improving such complex cognitive tasks realized by Software Engineering practitioners involved in CMMI implementations. The design of the CMMI-oKMS prototype tool is based on four proprietary ontologies (of things, of systems, of business, and of CMMI models) and built with Protégé tool. The result from a pilot empirical test, is reported as being satisfactory — compared with the extant reported systems — on the metric of perceived value (with statistical significance) and over usefulness, compatibility, and test score constructs (with practical significance), while that no gain was found in ease of use. Hence, given the limited number of similar KMS tools reported in the current literature, this research contributes to our body of knowledge in Software Engineering by providing useful design, building and evaluation insights for improving these knowledge support systems.

Multi-criteria decision making in ontologies

Decision support is one of the main objectives of ontology-based knowledge management systems. However, there is no standard method that would define how to model decisions in ontologies. Despite many research efforts and established methods for decision modelling and support, they have not yet been systematically applied to the field of ontologies. This paper proposes an ontology based multi-criteria decision making method that enables one to define decision models using ontology as the base construct. It structures decision models in such a way that the problem solution can be obtained by reasoning upon the ontology. We propose a generic approach that can be applied to an arbitrary domain. The proposed method is based on qualitative multi-criteria decision making, which is applied to the field of ontologies. OWL is used as the ontology representation language. As a proof of concept we have developed an ontology-based decision support system for an electric power transmission company. The proposed method represents an important step forward in the field of ontologies. Its main advantages are: a higher level of decision support provided by the ontology, direct use of information captured in ontology for decision making, a higher level of business process automation, and reuse of decision model concepts in definitions of more complex ontology concepts.