Ontology Management Research Articles

Research of Modern Information Technology on Tree Growth Management Decision Making Model

Tree growth management decision-making model can simulate growth management of tree and perform quantitative analysis of tree growth conditions. This paper explores the feasibility of modern information technology in management assessment of tree growth, information technology include neural network, ontology and expert system technology, then ontology technology is used to establish ontology database and knowledge base of tree growth management resource, the growth simulation and tree growth management ontology technology are used to build simulation models of tree growth, then expert systems and neural network technology are combined to simulate tree growth development process of decision-making model. The practice has proved that the research can not only predict the growth conditions of tree and dynamic grasp the growth process of the tree, but also can provide theoretical basis for the analysis and evaluation of tree growth management, greatly improving the level of tree growth management.

Development of an Obesity Management Ontology Based on the Nursing Process for the Mobile-Device Domain

BackgroundLifestyle modification is the most important factor in the management of obesity. It is therefore essential to enhance client participation in voluntary and continuous weight control.ObjectiveThe aim of this study was to develop an obesity management ontology for application in the mobile-device domain. We considered the concepts of client participation in behavioral modification for obesity management and focused on minimizing the amount of information exchange between the application and the database when providing tailored interventions.MethodsAn obesity management ontology was developed in seven phases: (1) defining the scope of obesity management, (2) selecting a foundational ontology, (3) extracting the concepts, (4) assigning relationships between these concepts, (5) evaluating representative layers of ontology content, (6) representing the ontology formally with Protégé, and (7) developing a prototype application for obesity management.ResultsBehavioral interventions, dietary advice, and physical activity were proposed as obesity management strategies. The nursing process was selected as a foundation of ontology, representing the obesity management process. We extracted 127 concepts, which included assessment data (eg, sex, body mass index, and waist circumference), inferred data to represent nursing diagnoses and evaluations (eg, degree of and reason for obesity, and success or failure of lifestyle modifications), and implementation (eg, education and advice). The relationship linking concepts were “part of”, “instance of”, “derives of”, “derives into”, “has plan”, “followed by”, and “has intention”. The concepts and relationships were formally represented using Protégé. The evaluation score of the obesity management ontology was 4.5 out of 5. An Android-based obesity management application comprising both agent and client parts was developed.ConclusionsWe have developed an ontology for representing obesity management with the nursing process as a foundation of ontology.

Multiple Ontologies in Pain Management: Toward a Postplural Rhetoric of Science

This article uses data obtained from a 2-year study—observation, survey, written- and verbal-artifact analysis, and interviews—of an interdisciplinary organization of pain management professionals to illustrate the analytic advantages of Mol and Latour's multiple-ontologies theories over incommensurability theory in understanding interdisciplinary practice. We demonstrate that pain science and medicine encompass a variety of practices that transcend disciplinary boundaries in ways not accounted for with incommensurability theory. After explicating multiple ontology theory and illustrating its analytic potential, we conclude by recommending a postplural model for inquiry into rhetoric of science.

An Ontology-Based Collaborative Inter-Organisational Knowledge Management Network (CIK-NET)

Ontologies are widely used to represent knowledge explicitly but it is impractical to expect all individuals and organisations to agree on using one or a small subset of ontologies. The adoption of multiple ontologies causes ontology mismatches which make their inherent vocabularies and relationships become inconsistent, resulting in difficulty for one system to understand and reuse these ontologies. To achieve knowledge sharing and reuse, ontology mediation is required to reconcile mismatches between heterogeneous ontologies. In this paper, we investigate the application of ontology in knowledge management (KM). Many KM approaches have been developed with the purpose of managing organisational knowledge. However, these approaches only focus on managing intra-organisational knowledge, which is inadequate in current business environment because users are often required to access inter-organisational knowledge to complete their tasks. These approaches also fail to collaborate with each other as their designs are based on their own business and KM requirement in managing organisational knowledge. We argue that ontology and its mediation methods can be used to overcome limitation of non-collaborative problem in which individual organisation is unable to reuse inter-organisational knowledge. An ontology-based inter-organisational KM network is therefore proposed to allow organisations accessing and retrieving inter-organisational knowledge of common domain.

A semantic metrics suite for evaluating modular ontologies

Ontologies, which are formal representations of knowledge within a domain, can be used for designing and sharing conceptual models of enterprises information for the purpose of enhancing understanding, communication and interoperability. For representing a body of knowledge, different ontologies may be designed. Recently, designing ontologies in a modular manner has emerged for achieving better reasoning performance, more efficient ontology management and change handling. One of the important challenges in the employment of ontologies and modular ontologies in modeling information within enterprises is the evaluation of the suitability of an ontology for a domain and the performance of inference operations over it. In this paper, we present a set of semantic metrics for evaluating ontologies and modular ontologies. These metrics measure cohesion and coupling of ontologies, which are two important notions in the process of assessing ontologies for enterprise modeling. The proposed metrics are based on semantic-based definitions of relativeness, and dependencies between local symbols, and also between local and external symbols of ontologies. Based on these semantic definitions, not only the explicitly asserted knowledge in ontologies but also the implied knowledge, which is derived through inference, is considered for the sake of ontology assessment. We present several empirical case studies for investigating the correlation between the proposed metrics and reasoning performance, which is an important issue in applicability of employing ontologies in real-world information systems.

A dynamic composition of ontology modules approach: application to web query reformulation

As the semantic web emerges, the problems related to the management of ontologies are gaining relevance. Ontology management is considered as one of the main issues in artificial intelligence and knowledge engineering. Ontology modularisation aims at structuring ontologies to facilitate its reuse and maintenance. Compositional methodologies intend to integrate a set of modules into a larger ontology. This paper presents a dynamic approach for ontology modules composition based on semantic similarity measures between concepts on the web, and aiming to improve the resulting modular ontology’s structure and content. The proposed approach takes place in three main phases, namely: cooccurrence graph construction of ontology modules, cooccurrence graph clustering of modular ontologies and hierarchical modules clusters construction. To evaluate our approach, we compare the obtained results with those of other compositional approaches in the literature, and we also show how these modular ontologies can be used to improve web query reformulation.

Ontology for vector surveillance and management.

Ontologies, which are made up by standardized and defined controlled vocabulary terms and their interrelationships, are comprehensive and readily searchable repositories for knowledge in a given domain. The Open Biomedical Ontologies (OBO) Foundry was initiated in 2001 with the aims of becoming an "umbrella" for life-science ontologies and promoting the use of ontology development best practices. A software application (OBO-Edit; *.obo file format) was developed to facilitate ontology development and editing. The OBO Foundry now comprises over 100 ontologies and candidate ontologies, including the NCBI organismal classification ontology (NCBITaxon), the Mosquito Insecticide Resistance Ontology (MIRO), the Infectious Disease Ontology (IDO), the IDOMAL malaria ontology, and ontologies for mosquito gross anatomy and tick gross anatomy. We previously developed a disease data management system for dengue and malaria control programs, which incorporated a set of information trees built upon ontological principles, including a "term tree" to promote the use of standardized terms. In the course of doing so, we realized that there were substantial gaps in existing ontologies with regards to concepts, processes, and, especially, physical entities (e.g., vector species, pathogen species, and vector surveillance and management equipment) in the domain of surveillance and management of vectors and vector-borne pathogens. We therefore produced an ontology for vector surveillance and management, focusing on arthropod vectors and vector-borne pathogens with relevance to humans or domestic animals, and with special emphasis on content to support operational activities through inclusion in databases, data management systems, or decision support systems. The Vector Surveillance and Management Ontology (VSMO) includes >2,200 unique terms, of which the vast majority (>80%) were newly generated during the development of this ontology. One core feature of the VSMO is the linkage, through the has vector relation, of arthropod species to the pathogenic microorganisms for which they serve as biological vectors. We also recognized and addressed a potential roadblock for use of the VSMO by the vector-borne disease community: the difficulty in extracting information from OBO-Edit ontology files (*.obo files) and exporting the information to other file formats. A novel ontology explorer tool was developed to facilitate extraction and export of information from the VSMO*.obo file into lists of terms and their associated unique IDs in *.txt or *.csv file formats. These lists can then be imported into a database or data management system for use as select lists with predefined terms. This is an important step to ensure that the knowledge contained in our ontology can be put into practical use.

Semantic Ontology Speech Information Extraction using Non-parametric Correlation Coefficient

Abstract On retrieving high frequency keywords in information retrieval system, mismatchings to user's request are problems because of the various meanings of keywords in the existing ontology configuration. In this paper, it is to construct personnel selection ontology and rules in personnel management which are composed of various concepts and knowledges based on semantic web technology and suggest selection procedures to support these rules and knowledge retrieval system to verify suitability of selection results. This system utilizes a method of extraction of speech features by using non-parametric correlation coefficient. This proposed method has been validated by showing that the result average SNR of the experiment evaluation of the proposed techniques was shown to be decreased by .752dB. Key Words : ontology, semantic web, non-parametric correlation coefficient * 이 논문은 2013년도 가천대학교의 교내연구비 지원에 의한 결과임.GCU-2013-R188)Received 8 July 2013, Revised 2 August 2013Accepted 20 September 2013Corresponding Author: Byungwook Lee(The Society of Digital Policy) Email: leebw@gachon.ac.krⒸ The Society of Digital Policy & Management. All rights reserved. This is an open-access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.otg/licenses/by-nc/3.0), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is ISSN: 1738-1916 properly cited.

PLM Ontology Exploitation through Inference and Statistical Analysis A Case Study for LCC

In a time of growing complexity and amount of a product related data, ontology has show to be an efficient and convenient method for structuring and modeling the domain of interest. It provides a clear picture of all relevant concepts, their relationships and the rules they follow. It is a structured, centralized data base, that allows fast updates and prevents redundancy. Still, fast, real-time functions of today's companies impose additional requirements on a operational domain model and in this paper, we argue that product life-cycle management (PLM) ontology can meet those requirements. In that sense, tools that we propose for ontology exploitation are mainly inference on ontology rules on one side, and ontology instances extraction for the purpose of visualization and data mining on the other side. We implement these tools for the case of manufacturing company on a real life data provided and analyze benefits from the aspect of non-expert end-user. This research is done as a part of FP7 project LinkedDesign.

ASSISTED KNOWLEDGE BASE GENERATION, MANAGEMENT AND COMPETENCE RETRIEVAL

Despite the presence of many systems for developing and managing structured taxonomies and/or SKOS models for a given domain for which small documents set are accessible, the production and maintenance of these domain knowledge bases is still a very expensive and time consuming process. This paper proposes a solution for assisting expert users in the development and management of knowledge base, including SKOS and ontologies modeling structures and relationships. The proposed solution accelerates the knowledge production by crawling and exploiting different kinds of sources (in multiple languages and with several inconsistencies among them). The proposed tool supports the experts in defining relationships among the most recurrent concepts, reducing the time to SKOS production and allowing assisted production. The validity of the produced knowledge base has been assessed by using SPARQL query interface and a precision and recall model. The results have demonstrated better performance with respect to the state of the art. The solution has been developed for Open Space Innovative Mind project, with the aim of creating a portal to allow industries at posing semantic queries to discover potential competences in a large institution such as the University of Florence, in which several distinct domains are associated with its own departments.

Introducing the new GRASS module g.infer for data-driven rule-based applications

This paper introduces the new GRASS GIS add-on module g.infer. The module enables rule-based analysis and workflow management in GRASS GIS, via data-driven inference processes based on the expert system shell CLIPS. The paper discusses the theoretical and developmental background that will help prepare the reader to use the module for Knowledge Engineering applications. In addition, potential application scenarios are sketched out, ranging from the rule-driven formulation of nontrivial GIS-classification tasks and GIS workflows to ontology management and intelligent software agents.

A formal ontology of knowing and knowledge

The paper presents a formalisation of one of the leading ontologies in Knowledge Management by the name of Formal Knowledge Management Ontology. After elaborating why the latter ontology is not a formal ontology in the sense of logic and how it might benefit from being logic-based, we detail a logical theory Formalised Formal Knowledge Management Ontology (F2KMO) that incorporates its main ideas and tenets. In particular, we specify the primitive, that is, undefined terms we assume, the axioms whose role is to fix the meanings of those terms, and a number of definitions that correspond to the definitions one may find in the latest exposition of Formal Knowledge Management Ontology. We show that F2KMO is consistent and prove its usefulness by defining the logical schema of a database based on the theory.

Ontology-driven data collection and validation framework for the diagnosis of vehicle healthmanagement

The current warranty data collection processes exhibit several data quality issues – the level of detail and precision is missing in the collected data, the semantic heterogeneity is observed and no systematic data quality validation mechanism to automatically certify the data quality. Such data cannot be translated seamlessly into the knowledge assets to perform business functions, for example, fault diagnosis. An ontology-driven structured data collection framework is proposed to acquire the necessary data in the warranty domain. The proposed framework uses the integrated vehicle health management ontology as an information model to populate necessary data acquisition fields of the framework. A novel three-dimensional data quality metric is proposed to validate the completeness, correctness and relevance of newly collected data. We also evaluate the performance of the tool by using the real-life data. The data accuracy precision after using the framework has been improved from 0.30 to 0.80, whereas the recall is improved from 0.28 to 0.70. Furthermore, the precision and recall of the tool is evaluated for the 500 real-life field failure cases and it was greater than 90% for data completeness and relevance. Throughout this paper we will use the words ‘correctness’ and ‘accuracy’ interchangeably.

THE APPLICATION OF A HIERARCHICAL TREE METHOD TO ONTOLOGY KNOWLEDGE ENGINEERING

Ontology technologies have been widely studied for their capabilities of semantic description and knowledge inference in a variety of application domains. The main obstacle for the advancement of ontologies is the immature building methods and construction principles. It is challenging to discover how to tune a large complete ontology to small applications and build domain ontologies pragmatically. This paper proposes a hierarchical tree method that provides guidelines to the building of a hierarchical geo-ontology and corresponding construction rules for different levels of ontologies. This hierarchical ontology construction method can be used to realize lower-level ontology building based on the restrictions of the upper-level ontology. It can also connect different levels of ontologies into a hierarchical structure. Based on the resulting hierarchical tree structure, the management and usage of ontologies becomes feasible and more efficient. Furthermore, to better elaborate the hierarchical relationships among the concepts surrounding the basic tree structure, this paper introduces Class Meta-Information (CMI) to OWL (Ontology Web Language) and uses Hierarchical Identification (HID) to draw conclusion on concepts' hierarchical relations. Examples, experimental simulation and comparative analyses are given to explain further the applications of this hierarchical tree method in the areas of ontology engineering.

Enhancing Service Discovery via Multi-Ontology Management and Annotation

Ontology, which adds semantic information to services, is an effective tool to automatize service discovery and composition. Though service discovery systems have been developed extensively, most possible scenario would still be that all the services adhere to a single ontology. However, even in a same domain, different domain experts or users can conceptualize the same real world entities in different views which will lead to multiple domain ontologies. Therefore how to manage multi-ontology and then how to use them to annotate service are the key technologies in service discovery system. In this paper based on ISO SC32 19763-3: MFI-3 (Metamodel Framework for Interoperability: Metamodel for Ontology Registration) we discuss in detail the mechanism of multi-ontology management and annotation to enhance the service discovery. Finally a prototype of Ontology Management Platform (OMP) is realized to serves annotation.

JPSearch: New international standard providing interoperable framework for image search and sharing

For the last 15 years, there have been various research and great advances in the field of content-based search and retrieval of images. In this paper, we present an overview of the JPSearch Standard, which is a recently published set of international standards providing interoperable framework for image search and sharing. The JPSearch standard is composed of six parts, which are Part 1: System framework and components, Part 2: Registration, identification and management of schema and ontology, Part 3: Query format, Part 4: File format for metadata embedded in image data (JPEG and JPEG 2000), Part 5: Data interchange format between image repositories, Part 6: Reference software. The Part 1 provides motivation and overview of the JPSearch framework. The Part 2 supports interoperability among various metadata specifications and social tagging using the specification of core metadata and translation rule description language. The Part 3 provides powerful image query language for interoperability among multiple image databases. The Part 4 specifies a file format based on JPEG and JPEG 2000 file format in which arbitrary number of metadata description of a image can be embedded. The Part 5 supports interoperability in interchanging images with metadata between image repositories. Finally, the Part 6 provides reference and utility softwares based on which the JPSearch compliant systems can be easily developed. In this paper, summaries and usage examples for each part of the JPSearch standard are presented.

Large-Scale Ontology Development and Agricultural Application Based on Knowledge Domain Framework

The key activity to build semantic web is to build ontologies. But today, the theory and methodology of ontology construction is still far from ready. This paper proposed a theoretical framework for massive knowledge management – the knowledge domain framework (KDF), and introduces an integrated development environment (IDE) named large-scale ontology development environment (LODE), which implements the proposed theoretical framework. We also compared LODE with other popular ontology development environments in this paper. The practice of using LODE on management and development of agriculture ontologies shows that knowledge domain framework can handle the development activities of large scale ontologies. Application studies based on the principle of knowledge domain framework and LODE was described briefly.

A Comparative Study of Recent Ontology Visualization Tools with a Case of Diabetes Data

Ontology is a conceptualization of a domain into machine readable format. Ontologies are becoming increasingly popular modelling schemas for knowledge management services and applications. Focus on developing tools to graphically visualise ontologies is rising to aid their assessment and analysis. Graph visualisation helps to browse and comprehend the structure of ontologies. A number of ontology visualizations exist that have been embedded in ontology management tools. The primary goal of this paper is to analyze recently implemented ontology visualization tools and their contributions in the enrichment of users’ cognitive support. This work also presents the preliminary results of an evaluation of three visualization tools to determine the suitability of each method for end user applications where ontologies are used as browsing aids with a case of Diabetes data..

A risk management ontology for Quality-by-Design based on a new development approach according GAMP 5.0

A new approach to the development of a risk management ontology is presented. This method meets the requirements of a pharmaceutical Quality by Design approach, good manufacturing practice and good automated manufacturing practice. The need for a risk management ontology for a pharmaceutical environment is demonstrated, and the term “ontology” is generally defined and described with regard to the knowledge domain of quality risk management.To fulfill software development requirements defined by good manufacturing practice regulations and good automated manufacturing practice 5.0 for the novel development approach, we used a V-model as a process model, which is discussed in detail. The development steps for the new risk management ontology, such as requirement specification, conceptualization, formalization, implementation and validation approach, are elaborated.

Collaborative Testing of Web Services

Software testers are confronted with great challenges in testing Web Services (WS) especially when integrating to services owned by other vendors. They must deal with the diversity of implementation techniques used by the other services and to meet a wide range of test requirements. However, they are in lack of software artifacts, the means of control over test executions and observation on the internal behavior of the other services. An automated testing technique must be developed to be capable of testing on-the-fly nonintrusively and nondisruptively. Addressing these problems, this paper proposes a framework of collaborative testing in which test tasks are completed through the collaboration of various test services that are registered, discovered, and invoked at runtime using the ontology of software testing STOWS. The composition of test services is realized by using test brokers, which are also test services but specialized in the coordination of other test services. The ontology can be extended and updated through an ontology management service so that it can support a wide open range of test activities, methods, techniques, and types of software artifacts. The paper presents a prototype implementation of the framework in semantic WS and demonstrates the feasibility of the framework by running examples of building a testing tool as a test service, developing a service for test executions of a WS, and composing existing test services for more complicated testing tasks. Experimental evaluation of the framework has also demonstrated its scalability.