Ontology-based Knowledge Management System Research Articles

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.

Development methodologies for ontology‐based knowledge management systems: A review

AbstractKnowledge management systems (KMS) are computer‐based systems highly valued in business organizations because they support knowledge management (KM) processes. Most KMS have been developed using non‐intelligent computer technology—that is, DMS, CMS, DBMS, and CIS—, and thus, they cannot provide advanced capabilities. Consequently, enhanced KMS using intelligent technologies of ontologies with inference engines—called ontology‐based knowledge management systems (OKMS)—have been proposed in the last three decades. Nowadays, however, the implementation of OKMS in real‐world settings is still scarce. Lack of comprehensive and systematic development methodologies including Project Management and Technical Systems Engineering processes—as the Systems and Software Systems Engineering standards propose—have been suggested as a factor that inhibits OKMS implementations. In this study, we review the OKMS literature (1990–2021 period)—from six seminal studies located using a research search engine—to assess OKMS development methodologies that can be considered comprehensive and systematic. Five methodologies were identified and assessed using an evaluation subset from the ISO/IEC/IEEE 15288:2015 Systems and Software Engineering standard. Two of them—CommonKADS and NeON—were found with a high comprehensive and systematic level and both are suggested for organizations interested in OKMS implementations, but none of them qualified as agile, which is a current development approach for systems and software systems. Hence, further empirical research toward the realization of comprehensive and systematic OKMSs development methodologies, including agile versions, is suggested for fostering the implementation of OKMS in real‐world settings.

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.

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.

A Conceptual Architecture of Ontology Based KM System for Failure Mode and Effects Analysis

Failure Mode and Effects Analysis (FMEA) is a systematic method for procedure analyses and risk assessment. It is a structured way to identify potential failure modes of a product or process, probability of their occurrence, and their overall effects. The basic purpose of this analysis is to mitigate the risk and the impact associated to a failure by planning and prioritizing actions to make a product or a process robust to failure. Effective manufacturing and improved quality products are the fruits of successful implementation of FMEA. During this activity valuable knowledge is generated which turns into product or process quality and efficiency. If this knowledge can be shared and reused then it would be helpful in early identification of failure points and their troubleshooting, and will also help the quality management to get decision support in time. But integration and reuse of this knowledge is difficult because there are number of challenges e.g., unavailability of unified criteria of FMEA knowledge, lack of semantic organization, natural language text based description of knowledge, most of the times FMEA is started from scratch instead of using existing knowledge that makes it incomplete for larger systems, and above all its success depends on the knowledge which is stored in the brains of perfectionists in the form of experience which may or may not be available anytime anywhere. In this article we are proposing an Information and Communication Technology (ICT) based solution to preserve, reuse, and share the valuable knowledge produced during FMEA. In proposed system existing knowledge available in repositories and experts head will be gathered and stored in a knowledge base using an ontology, and at the time of need this knowledge base will be inferred to make decisions in order to mitigate the probable risks. Ontology based approaches are best suited for the knowledge management systems, in which human experts are required to model and analyze their expertise in order to feed them in a conceptual knowledge base for its preservation and reuse. Keywords: Knowledge Management (KM), ontology, Failure Mode and Effects Anal- ysis (FMEA).

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.

Semantic annotation of cognitive map for knowledge sharing between heterogeneous businesses

A cognitive map has been regarded as one of the most efficient ways to solve problems like lack and uncertainty of knowledge in many different domains. In this paper, we have designed an ontology-based knowledge management systems (KMS) which is capable of annotating cognitive maps. Then, an OntoCM (ontological cognitive map) framework is proposed to collaboratively share the annotated cognitive maps between businesses by using an OntoCM Repository. Thereby, in order to manipulate the annotated cognitive maps, we have defined OntoCM operations such as expansion, contradiction, augmentation, and screener. By employing several concept hierarchies as ontologies and simulating synthesis patterns on OntoCM, we have extracted potential relationships between the existing concepts, and expected that the relationships between the cognitive maps can help the businesses to improve understandability.

Study the impact of Requirements management Characteristics in global software development projects: An Ontology based approach

Requirements Management is one of the challenging and key tasks in the development of software products in distributed software development environment. One of the key reasons found in our literature survey the failure of software projects due to poor project management and requirement management activity. This main aim of this paper 1. Formulate a framework for the successful and efficient requirements management framework for Global Software Development Projects. (GSD) 2. Design a Mixed organization structure of both traditional approaches and agile approaches, of global software development projects. 3. Apply Ontology based Knowledge Management Systems for both the approaches to achieve requirements issues such as missing, inconsistency of requirements, communication and knowledge management issues and improve the project management activities in a global software development environment. 4. Propose requirements management metrics to measure and manage software process during the development of information systems. The major contribution of this paper is to analyze the requirements management issues and challenges associated with global software development projects. Two hypotheses have been formulated and tested this problem through statistical techniques like correlation and regression analysis and validate the same.

Ontology-Based Interorganizational Knowledge Management in Sustainable Construction Project

Due to the great negative impact, the construction industry needs to undergo a paradigm shift from traditional construction to sustainable construction. To reach the goal of sustainable development, the construction industry needs to intensify its efforts to move to a knowledge intensive mode. Based on the analysis of e-Cognos and the concept of ontology, this paper proposes that e-Cognos ontology can be applied in the development of sustainable construction process ontology, which is a key part of knowledge management system (KMS). Following this, the application process of ontology-based KMS is analyzed using IDEF0 modeling method. Finally, this paper analyzes interorganizational collaboration model in sustainable project.

User-driven generation of semantic web content in Enterprise 2.0: a case study

The use of semantic web technologies in enterprise environments has been subject to lots of research and development since the end of the 1990s, with topics ranging from semantic middleware infrastructures to ontology-based knowledge management systems. In this paper, we focus on the particular issue of user-driven generation of semantic web content in Enterprise 2.0 environments, and demonstrate how we extended several already-deployed user-driven applications (such as blogs, wikis and tagging systems) to produce and use semantic annotations in such context. Especially, we discuss the technical aspects involved in the process as well as the incentives that have been provided to involve users in producing such data. In addition, we present how users reacted and accepted the tools and what are the lessons that we learnt during that project.

On KM, KMS and ontology-based KMS in the domain of SwE standards and models of processes: A conceptual survey

Standards and models of processes - such as ISO/lEC 9000 standard for deploying quality management systems - have been developed for international organizations to promote the utilization of best managerial and engineering practices. However, given their conceptual density, a large number of concepts, composite concepts, and interrelationships, understanding them cannot be considered a trivial cognitive task for new readers and decision makers. Consequently, some IT-supported systems have been used to improve such an understanding. These tools provide basic searching services via keywords or catalog indices on monolithic text-based, hypertext technology, or multimedia documents. As a result, their system performance, correctness, user satisfaction, and lately understanding metrics, are still unsatisfactory. In this paper, we support the hypothesis that IT systems, where knowledge structures are explicitly codified, such as ontology-based knowledge management systems (o-KMS), can provide a plausible better solution. To support this hypothesis, we firstly report a conceptual survey on KMS and KM foundations (IT for KMS, KM Processes) to describe the capabilities of KMS and supporting needs for KM processes. Secondly, we describe the profiles of standards and models of process, and review related research on KMS. Finally, we present the problem-solution links, and discuss how o-KMS might provide a better support. We conclude that research results to date require further advance.

An ontology-based knowledge management system to support technology intelligence

High technology enterprises must now continually reduce costs and develop better products and services in order to sustain a competitive advantage in an endlessly changing business environment. Escalating research and development (R&D) costs force high technology enterprises to select more carefully which technologies to invest in. Advanced technology planning tools enable enterprises to make better strategic decisions. Knowledge of the technological changes relevant to an enterprise represents a critical issue in enhancing the performance of that enterprise and sustaining its competitive edge. The creation of ontology is usually the first step towards a sustainable knowledge management system. In this article, we describe the framework of an ontology-based knowledge management system including design and application at a real case. The proposed system was developed in the Protege environment, a generic system that is applicable to a variety of domain applications.

A framework for distributed knowledge management: Design and implementation

This paper describes a framework for implementing distributed ontology-based knowledge management systems (DOKMS). The framework, in particular, focuses on knowledge management within organizations. It investigates the functional requirements to enable Individual Knowledge Workers (IKWs) and distributed communities (e.g., project teams) to create, manage and share knowledge with the support of ontologies. On the one hand, the framework enables distributed and collaborative work by relying on a P2P virtual office model. On the other hand, it provides a multi-layer ontology framework to enable semantics-driven knowledge processing. The ontology framework allows organizational knowledge to be modeled at different levels. An Upper Ontology is exploited to establish a common organizational knowledge background. A set of Workspace Ontologies can be designed to manage, share and search knowledge within communities by the establishment of a contextual (i.e., related to the aim of a group) understanding. Finally, Personal Ontologies support IKWs in personal knowledge management activities. We present an implementation of the designed framework in the K-link+ system and show the suitability of this approach through a use case. The evaluation of K-link+ in a real network is also discussed.

A process-oriented ontology-based knowledge management system for facilitating operational procedures in public administration

Public organizations produce daily a great volume of administrative documents, in order to fulfill their mission. This requires the use of a certain, unique for each procedure, legal framework. This article adopts a process oriented approach, through a web-based knowledge management system that provides this legal framework in an up-to-date and accurate manner. The system also supports the interpretation of the legal framework, supplying civil servants, citizens and businesses with precedents and opinions. The system employs an ontology in OWL for representing the public administration structure and any kind of document that flows among administrative units, during the execution of the procedures, which are mapped into OWL-S service models.

An ontology-based knowledge management system for flow and water quality modeling

Currently, the numerical simulation of flow and/or water quality becomes more and more sophisticated. There arises a demand on the integration of recent knowledge management (KM), artificial intelligence technology with the conventional hydraulic algorithmic models in order to assist novice application users in selection and manipulation of various mathematical tools. In this paper, an ontology-based KM system (KMS) is presented, which employs a three-stage life cycle for the ontology design and a Java/XML-based scheme for automatically generating knowledge search components. The prototype KMS on flow and water quality is addressed to simulate human expertise during the problem solving by incorporating artificial intelligence and coupling various descriptive knowledge, procedural knowledge and reasoning knowledge involved in the coastal hydraulic and transport processes. The ontology is divided into information ontology and domain ontology in order to realize the objective of semantic match for knowledge search. The architecture, the development and the implementation of the prototype system are described in details. Both forward chaining and backward chaining are used collectively during the inference process. In order to demonstrate the application of the prototype KMS, a case study is presented.

지식 문서에서 도메인 온톨로지를 이용한 개념 추출 기법

We propose a novel technique to categorize XML documents and extract a concept efficiently using domain ontology. First, we create domain ontology that use text mining technique and statistical technique. We propose a DScore technique to classify XML documents by using the structural characteristic of XML document. We also present TScore technique to extract a concept by comparing the association term set of domain ontology and the terms in the XML document. To verify the efficiency of the proposed technique, we perform experiment for 295 papers in the computer science area. The results of experiment show that the proposed technique using the structural information in the XML documents is more efficient than the existing technique. Especially, the TScore technique effectively extract the concept of documents although frequency of term is few. Hence, the proposed concept-based retrieval techniques can be expected to contribute to the development of an efficient ontology-based knowledge management system.