Ontology Management System Research Articles

GOMS: Large‐scale ontology management system using graph databases

Large-scale ontology management is one of the main issues when using ontology data practically. Although many approaches have been proposed in relational database management systems (RDBMSs) or object-oriented DBMSs (OODBMSs) to develop large-scale ontology management systems, they have several limitations because ontology data structures are intrinsically different from traditional data structures in RDBMSs or OODBMSs. In addition, users have difficulty using ontology data because many terminologies (ontology nodes) in large-scale ontology data match with a given string keyword. Therefore, in this study, we propose a (graph database-based ontology management system (GOMS) to efficiently manage large-scale ontology data. GOMS uses a graph DBMS and provides new query templates to help users find key concepts or instances. Furthermore, to run queries with multiple joins and path conditions efficiently, we propose GOMS encoding as a filtering tool and develop hash-based join processing algorithms in the graph DBMS. Finally, we experimentally show that GOMS can process various types of queries efficiently.

Ontology based systemazing of the science information devoted to waste utilizing by methanogenesys

Quantity of the scientific materials in the field of biogas production is growing. Thus, it is actual to provide informational management of it. Previously text documents and not-interactive pictures were used to describe it. The approach to systematization of scientific information on the production of biogas based on the ontological IT platform of TODOS is developed. It is proposed to separate the semantic characteristics of each work for their further introduction it into the IT platform of the TODOS. To construct a system of ranking of previous studies, we have identified the semantic characteristics of scientific research devoted to the production of biogas from chicken manure. Temperature (°C), volume of reactor (l), chicken manure content (%), moisture content (%), active sludge content (%), final solids content (%), biogas production and methane (ml/g TS), methane content (%), year of the research, ammonium nitrogen content (mg/l), VFA (mg/l), pH is final, initial pH, minimum pH, maximum pH were used to create ontology management system of the previous researches devoted to biogas production. The characteristics of the microorganisms were used to create the ontology based system of the microorganism selection. Google sheets was used to create base of knowledge and previous researches. An ontological graph with a ranking function for previous scientific research and a system of selection of microorganisms has been developed. Thus, the proposed systems allow to systematize previous research and theoretical information devoted to help of ontological graphs and provide information management in this field. Developed approaches allow us to analyze the results of previous research and theoretical information which provides systemizing of information and information management in general.

Developing a community-based knowledge system: a case study using Sri Lankan agriculture

The Agriculture sector plays a vital role in Sri Lanka’s economy. Not having an agricultural knowledge repository that can be easily accessed by people in agriculture community in Sri Lanka within their own context, is a major problem. As a solution, a large user centred ontology for Sri Lankan farmers was developed to provide required information/knowledge not only in a structured and complete way, but also in a context-specific manner. Since this problem is not only limited to farmers, we extend this for every one working in the agriculture domain. We validate the ontology in terms of accuracy and quality. The online knowledge base based on the ontology with a SPARQL endpoint was created to share and reuse the domain knowledge that can be queried based on user context. A Mobile based application and a Web based application were developed to provide information/knowledge by using this ontology. These applications are also used to evaluate the ontology by getting the feedbacks from users to the knowledge in the ontology. It is very difficult to maintain a large complex ontology. To maintain our ontology, we identified various processes that are required to develop and maintain ontology as a collaborative process. A semi-automatic end-to-end ontology management system was developed to manage the developed ontology and the knowledge base. It provides the facilities to reuse, share, modify, extendand prune the ontology components as required. The facilities to capture users’ information needs and search domain information in user context are also included. In this paper, we present a summary of the overall development process of the ontology including the end-to-end ontology management system.

Trust and Privacy Solutions Based on Holistic Service Requirements.

The products and services designed for Smart Cities provide the necessary tools to improve the management of modern cities in a more efficient way. These tools need to gather citizens’ information about their activity, preferences, habits, etc. opening up the possibility of tracking them. Thus, privacy and security policies must be developed in order to satisfy and manage the legislative heterogeneity surrounding the services provided and comply with the laws of the country where they are provided. This paper presents one of the possible solutions to manage this heterogeneity, bearing in mind these types of networks, such as Wireless Sensor Networks, have important resource limitations. A knowledge and ontology management system is proposed to facilitate the collaboration between the business, legal and technological areas. This will ease the implementation of adequate specific security and privacy policies for a given service. All these security and privacy policies are based on the information provided by the deployed platforms and by expert system processing.

Automated concept and relationship extraction for the semi-automated ontology management (SEAM) system.

BackgroundWe develop medical-specialty specific ontologies that contain the settled science and common term usage. We leverage current practices in information and relationship extraction to streamline the ontology development process. Our system combines different text types with information and relationship extraction techniques in a low overhead modifiable system. Our SEmi-Automated ontology Maintenance (SEAM) system features a natural language processing pipeline for information extraction. Synonym and hierarchical groups are identified using corpus-based semantics and lexico-syntactic patterns. The semantic vectors we use are term frequency by inverse document frequency and context vectors.Clinical documents contain the terms we want in an ontology. They also contain idiosyncratic usage and are unlikely to contain the linguistic constructs associated with synonym and hierarchy identification. By including both clinical and biomedical texts, SEAM can recommend terms from those appearing in both document types. The set of recommended terms is then used to filter the synonyms and hierarchical relationships extracted from the biomedical corpus.We demonstrate the generality of the system across three use cases: ontologies for acute changes in mental status, Medically Unexplained Syndromes, and echocardiogram summary statements.ResultsAcross the three uses cases, we held the number of recommended terms relatively constant by changing SEAM’s parameters. Experts seem to find more than 300 recommended terms to be overwhelming. The approval rate of recommended terms increased as the number and specificity of clinical documents in the corpus increased. It was 60% when there were 199 clinical documents that were not specific to the ontology domain and 90% when there were 2879 documents very specific to the target domain.We found that fewer than 100 recommended synonym groups were also preferred. Approval rates for synonym recommendations remained low varying from 43% to 25% as the number of journal articles increased from 19 to 47. Overall the number of recommended hierarchical relationships was very low although approval was good. It varied between 67% and 31%.ConclusionSEAM produced a concise list of recommended clinical terms, synonyms and hierarchical relationships regardless of medical domain.

On Design of Ontology Management Systems

An ontology management system (OMS) is a software system which manages ontologies through their lifecycle. It simplifies ontologies building and applications by providing a mechanism for querying ontologies and an easy-to-use programming interface to ontologies and instances. OMSs are powerful tools to create ontologies and extend their applications and scope. This paper explains the basic design principles of an OMS and designs a general framework of OMSs. It devotes to promoting systematically research and development of OMSs.

Knowledge Mining Wikipedia

The organization of information in the knowledge economy has become a primary business process in many enterprises. The better information is organized and stored, the easier it can be retrieved, so that the most relevant information will always be available. Ontology is a versatile technology for organizing information; however, the main obstacle that prevents ontology prevailing is the difficulty in its management, that is, the building and maintaining of ontology. To solve this problem, a structured knowledge source is needed. In this paper, a thorough review on the feasibility of using Wikipedia as the source for ontology management is presented. Different approaches that are based on the use of Wikipedia for such purposes are also discussed. Finally, an ontology management system, based on the use of Wikipedia as the knowledge source, is proposed.

An Evolutive and Multilingual CIM Ontology Management System

CIM(Common Information Model) descrybed by IEC 61970/61968 standards is recognized as the Smart Grid ontology, while Chinese localized standards of the IEC 61970 series are published as DL/T 890 series and are widely applied to power management systems throughout China. The CIM series standards define an application program interface (API) for energy management system (EMS). One core part of the standards is IEC 61970-301, which defines the CIM base set of packages which provide a logical view of the functional aspects of an EMS. Also, there are other parts of the standards which define logical views of the parts related to EMS, such as IEC 61970-302 and IEC 61968-11. Although each part of the standards is defined as a standalone UML (Unified Modeling Language) model, a unified UML model in one UML file is maintained by IEC TC57. However, as each part of the standards and its partitive ontology model are developed by different working groups of the committee and are at different standard statuses, it is necessary to provide a continuous maintenance for the CIM ontology publicly and worldwide. Difficulties to realize the continuous CIM ontology management are caused by the present CIM model. Some typical insufficiencies of the existing CIM model are described in [1]. E.g., CIM model has a lack of version information and no version control mechanism for each entity is provided in the CIM standards so that the data integrity between ontologies of new version and old version is not guaranteed. In this paper, to achieve a continuous CIM ontology management, at first, a CIM model is transformed to the IEC 62656 model which provides a methodology to overcome the current CIM weakness. This paper will focus on the principle of transformation of CIM UML model to the IEC 62656 model. In addition the prime advantages brought about by the CIM on the IEC 62656 model such as CIM localization, customization and validation are explained. Furthermore, an evolution- mechanism integrated CIM ontology database manage- ment system via the IEC 62656 model is introduced.

Ontology-based simulation in agricultural systems modeling

A methodology and applications of ontology-based simulation are presented. An environment for building simulations based on the Lyra ontology management system is described which includes web-based visual design tools for constructing models and automatically generating simulation code. The ontology is used for representing all equations and all symbols appearing in these equations that are needed to describe a model. The example applications presented are models of soil, water, and nutrient management in citrus and sugarcane. Results thus far show that the ontology-based approach has advantages for representing the model structure, equations, and symbols, that complex models can be described in this format, and that efficient simulation code can be generated automatically from the ontology definition of the model. Potential applications, not yet fully explored, include ability to automatically connect models and data sources, using the ontology to organize model bases containing many models and model components, and using ontology reasoners to search for models, automatically discover model similarities and differences, and generate model instances from general principles.

Advanced ontology management system for personalised e-Learning

The use of ontologies to model the knowledge of specific domains represents a key aspect for the integration of information coming from different sources, for supporting collaboration within virtual communities, for improving information retrieval, and more generally, it is important for reasoning on available knowledge. In the e-Learning field, ontologies can be used to model educational domains and to build, organize and update specific learning resources (i.e. learning objects, learner profiles, learning paths, etc.). One of the main problems of educational domains modeling is the lacking of expertise in the knowledge engineering field by the e-Learning actors. This paper presents an integrated approach to manage the life-cycle of ontologies, used to define personalised e-Learning experiences supporting blended learning activities, without any specific expertise in knowledge engineering.

The impact of purpose, people and technology on the virtual project team

PurposeThe purpose of this paper is to contribute to the growing body of research into virtual teams by using empirical data to validate existing theory. It analyses a project in the travel and tourism industry to determine the key elements that contributed to a successful outcome.Design/methodology/approachAn ethnographic approach is used in this case of a multi‐national, multi‐organisational virtual project tasked with market validation of an ontology management system. Participant observation is supported by a range of primary and secondary data.FindingsThe importance of key factors changes over the time of the project. The early emphasis on constructs relating to technology and purpose moves towards those related to people as the project matured. The caretaker/gatekeeper role of the project leader made a significant contribution to overcoming problems of complexity. A lack of commitment to a range of information technologies heightened the reliance on periodic face‐to‐face meetings to support team interactions.Research limitations/implicationsCultural diversity is common in the travel sector and related issues may play a more significant role in other industries. Further research is needed to fully assess the impact of context on virtual team outcomes.Practical implicationsRecommendations for practitioners include the need to establish a framework of rules and identify training needs. A socialising period, virtual or face‐to‐face, is an essential requirement for successful virtual teamwork.Originality/valueA conceptual framework, drawn from previous studies, adds clarity to the importance and relevance of the constructs of people, purpose and technology in virtual teamwork.

Infrastructure for a clinical-decision-intelligence system

Clinical decision intelligence (CDI) is an emerging area in health care, covering a broad range of subjects, from clinical data integration and data analysis to knowledge management and application development. The goal of CDI systems is to improve health-care quality and reduce costs through the discovery, management, and application of clinical intelligence from heterogeneous and rapidly expanding data sources. These sources include data from clinical practice, nursing, health-care management, health-care administration, and medical research. In this paper, we discuss the functional requirements and reference architecture for CDI systems and their clinical applications. This architecture includes an integrated framework for managing the entire CDI process, a standardized enterprise ontology management system, and a clinical knowledge representation platform. The CDI approach has the potential to transform the health-care management process through the integration of business intelligence, business rule management, and business process management in a clinical setting, and to help health-care organizations move closer to an on demand model.

A methodology for constructing of philosophy ontology based on philosophical texts

An ontology is a crucial factor for the success of the Semantic Web and other knowledge-based systems in terms of share and reuse of domain knowledge. However, there are a few concrete ontologies within actual knowledge domains including learning domains. In this paper, we develop an ontology which is an explicit formal specification of concepts and semantic relations among them in philosophy. We call it a philosophy ontology. Our philosophy is a formal specification of philosophical knowledge including knowledge of contents of classical texts of philosophy. We propose a methodology, which consists of detailed guidelines and templates, for constructing text-based ontology. Our methodology consists of 3 major steps and 14 minor steps. To implement the philosophy ontology, we develop an ontology management system based on Topic Maps. Our system includes a semi-automatic translator for creating Topic Map documents from the output of conceptualization steps and other tools to construct, store, retrieve ontologies based on Topic Maps. Our methodology and tools can be applied to other learning domain ontologies, such as history, literature, arts, and music.

Ontology management for large-scale enterprise systems

Semantic markup languages such as RDF (Resource Description Framework) [ Resource Description Framework (RDF), http://www.w3.org/RDF/] and OWL (Web Ontology Language) [ Web Ontology Language (OWL), http://www.w3.org/2004/OWL/] are increasingly being used to externalize meta-data or ontologies about data, software and services in a declarative form. Such externalized descriptions in ontological format are used for purposes ranging from search and retrieval to information integration and to service composition [Resource Description Framework (RDF): Projects and Applications, http://w3c.org/RDF/#projects, Web Ontology Language (OWL): Tools, Projects and Applications, http://w3c.org/2004/OWL/#projects]. Ontologies could significantly reduce the costs of deploying, integrating and maintaining enterprise systems. The barrier to more wide-spread use of ontologies for such applications is the lack of support in the currently available middleware stacks used in enterprise computing. This paper presents our work on developing an enterprise-scale ontology management system that will provide APIs and query languages, and scalability and performance that enterprise applications demand. We describe the design and implementation of the management system that programmatically supports the ontology needs of enterprise applications in a similar way a database management system supports the data needs of applications. In addition, we present a novel approach to representing ontologies in relational database tables to address the scalability and performance issues. The state of the art ontology management systems are either memory-based or use ad hoc solutions for persisting data, and so provide limited scalability.

SIMILARITY OF XML-SCHEMA ELEMENTS SUPPORTED BY DOMAIN ONTOLOGIES

XML-Schemas and domain ontologies are two key features of the Semantic Web. The possibility of assessing similarity between concepts is becoming a fundamental support in e-commerce and business transactions. In this paper a method for evaluating similarity of XML-Schema elements is proposed, by making use of domain ontologies. The method is inspired by a previous proposal concerning concept similarity within an enterprise ontology management system developed at IASI.

Text Mining Techniques to Automatically Enrich a Domain Ontology

Though the utility of domain ontologies is now widely acknowledged in the IT (Information Technology) community, several barriers must be overcome before ontologies become practical and useful tools. A critical issue is the ontology construction, i.e., the task of identifying, defining, and entering the concept definitions. In case of large and complex application domains this task can be lengthy, costly, and controversial (since different persons may have different points of view about the same concept). To reduce time, cost (and, sometimes, harsh discussions) it is highly advisable to refer, in constructing or updating an ontology, to the documents available in the field. Text mining tools may be of great help in this task. The work presented in this paper illustrates the guidelines of SymOntos, ontology management system, and the text mining approach adopted herein to support ontology building. The latter operates by extracting, from the related literature, the prominent domain concepts and the semantic relations among them.

Ontologies for conceptual modeling: their creation, use, and management

Although ontologies have been proposed as an important and natural means of representing real world knowledge for the development of database designs, most ontology creation is not carried out systematically. To be truly useful, a repository of ontologies, organized by application domain is needed, along with procedures for creating and integrating ontologies into database design methodologies. This research proposes a methodology for creating and managing domain ontologies. An architecture for an ontology management system is presented and implemented in a prototype. Empirical validation of the prototype demonstrates the effectiveness of the research.

Back-end technology for high-performance knowledge-representation systems

PARKA-DB is a scalable ontology management system that uses relational database techniques and supports the large ontologies needed in medical database system applications (among others). The medical system lets doctors load information about their patients' conditions (specifically microbiological data) into a hybrid ontology. The doctor uses pure ontological information to load a KB containing a very large number of assertions about the patient. PARKA-DB supports a frame based AI language that uses high performance and scalable computing techniques.