Using OWL-VisMod through a decision-making process for reusing OWL ontologies

Knowledge representation (KR) can be defined as a set of ontological commitments, provided with the capabilities of performing inference. The knowledge can be represented using an ontology, which provides a shared insight into a certain domain. The use of ontologies to represent knowledge also allows interoperation among knowledge-based systems. The process of building ontologies can be tedious and sometimes exhaustive. A possible solution in order to avoid this problem would be to reuse the ontologies previously created by others. This paper describes a case study of reusability using OWL-VisMod, a tool designed for developing ontological engineering based on visual conceptual modelling for OWL ontologies. A workflow performed with OWL-VisMod is described; including a decision-making process in order to decide whether or not it could be desirable to reuse an ontology, according to the requirements of a certain project.     

Human-centered ontology engineering: The HCOME methodology

The fast emergent and continuously evolving areas of the Semantic Web and Knowledge Management make the incorporation of ontology engineering tasks in knowledge-empowered organizations and in the World Wide Web more than necessary. In such environments, the development and evolution of ontologies must be seen as a dynamic process that has to be supported through the entire ontology life cycle, resulting to living ontologies. The aim of this paper is to present the Human-Centered Ontology Engineering Methodology (HCOME) for the development and evaluation of living ontologies in the context of communities of knowledge workers. The methodology aims to empower knowledge workers to continuously manage their formal conceptualizations in their day-to-day activities and shape their information space by being actively involved in the ontology life cycle. The paper also demonstrates the Human Centered ONtology Engineering Environment, HCONE, which can effectively support this methodology. George VOUROS (B.Sc. Ph.D.) holds a B.Sc. in Mathematics, and a Ph.D. in Artificial Intelligence all from the University of Athens, Greece. Currently he is a Professor and Head of the Department of Information and Communication Systems Engineering, University of the Aegean, Greece, Director of the AI Lab and head of the Intelligent and Cooperative Systems Group (InCoSys). He has done research in the areas of Expert Systems, Knowledge management, Collaborative Systems, Ontologies, and Agent-based Systems. His published scientific work includes more than 80 book chapters, journal and national and international conference papers in the above-mentioned themes. He has served as program chair and chair and member of organizing committees of national and international conferences on related topics. Konstantinos KOTIS (B.Sc. Ph.D.) holds a B.Sc. in Computation from the University of Manchester, UK (1995), and a Ph.D. in Information Management from University of the Aegean, Greece (May, 2005). Currently, he is a member of the Intelligent and Cooperative Systems Group (InCoSys) and director of the Information Technology Department of the Prefecture of Samos, Greece. His research and published work concerns Knowledge management, Ontology Engineering and Semantic Web. He has lectured in several IT seminars and has served as member of program committees in international workshops.     

Onyx: A new Canvas-based tool for visualizing biomedical and health ontologies

Ontologies provide formal, machine-readable, and human-interpretable representations of domain knowledge. Therefore, ontologies have come into question with the development of Semantic Web technologies. People who want to use ontologies need an understanding of the ontology, but this understanding is very difficult to attain if the ontology user lacks the background knowledge necessary to comprehend the ontology or if the ontology is very large. Thus, software tools that facilitate the understanding of ontologies are needed. Ontology visualization is an important research area because visualization can help in the development, exploration, verification, and comprehension of ontologies. This paper introduces the design of a new ontology visualization tool, which differs from traditional visualization tools by providing important metrics and analytics about ontology concepts and warning the ontology developer about potential ontology design errors. The tool, called Onyx, also has advantages in terms of speed and readability. Thus, Onyx offers a suitable environment for the representation of large ontologies, especially those used in biomedical and health information systems and those that contain many terms. It is clear that these additional functionalities will increase the value of traditional ontology visualization tools during ontology exploration and evaluation.     

Collaborative Brushing and Linking for Co-located Visual Analytics of Document Collections

Many real-world analysis tasks can benefit from the combined efforts of a group of people. Past research has shown that to design visualizations for collaborative visual analytics tasks, we need to support both individual as well as joint analysis activities. We present Cambiera, a tabletop visual analytics tool that supports individual and collaborative information foraging activities in large text document collections. We define collaborative brushing and linking as an awareness mechanism that enables analysts to follow their own hypotheses during collaborative sessions while still remaining aware of the group's activities. With Cambiera, users are able to collaboratively search through documents, maintaining awareness of each others' work and building on each others' findings.     

A semantic knowledge management system for laminated composites

The engineering of laminated composite structures is a complex task for design engineers and manufacturers, requiring significant management of manufacturing process and materials information. Ontologies are becoming increasingly commonplace for semantically representing knowledge in a formal manner that facilitates sharing of rich information between people and applications. Moreover, ontologies can support first-order logic and reasoning by rule engines that enhance automation. To support the engineering of laminated composite structures, this work developed a novel Semantic LAminated Composites Knowledge management System (SLACKS) that is based on a suite of ontologies for laminated composites materials and design for manufacturing (DFM) and their integration into a previously developed engineering design framework. By leveraging information from CAD/FEA tools and materials data from online public databases, SLACKS uniquely enables software tools and people to interoperate, to improve communication and automate reasoning during the design process. With SLACKS, this paper shows the power of integrating relevant domains of the product life cycle, such as design, analysis, manufacturing and materials selection through the engineering case study of a wind turbine blade. The integration reveals a usable product-life-cycle knowledge tool that can facilitate efficient knowledge creation, retrieval and reuse from design inception to manufacturing of the product.     

基于protégé的课程内容本体的构建

本体作为一种有效表现概念层次结构和相互关系的模型,能够有效地表示课程内容框架,以visualbasic课程为例,使用protege工具进行建模,初步探讨了课程内容本体的构建原则和步骤．     

Trust‐annotated ontology integration using social modelling*

Abstract: As ontologies become more prevalent for information management the need to manage the ontologies increases. Multiple organizations, within a domain, often combine to work on specific projects. When separate organizations come together to communicate, an alignment of terminology and semantics is required. Ontology creation is often privatized for these individual organizations to represent their view of the domain. This creates problems with alignment and integration, making it necessary to consider how much each ontology should influence the current decision to be made. To assist with determining influence a trust‐based approach on authors and their ontologies provides a mechanism for ranking reasoning results. A representation of authors and the individual resources they provide for the merged ontology becomes necessary. The authors are then weighted by trust and trust for the resources they provide the ontology is calculated. This is then used to assist the integration process allowing for an evolutionary trust model to calculate the level of credibility of resources. Once the integration is complete semantic agreement between ontologies allows for the revision of the authors' trust.     

The State‐of‐the‐Art in Predictive Visual Analytics

Predictive analytics embraces an extensive range of techniques including statistical modeling, machine learning, and data mining and is applied in business intelligence, public health, disaster management and response, and many other fields. To date, visualization has been broadly used to support tasks in the predictive analytics pipeline. Primary uses have been in data cleaning, exploratory analysis, and diagnostics. For example, scatterplots and bar charts are used to illustrate class distributions and responses. More recently, extensive visual analytics systems for feature selection, incremental learning, and various prediction tasks have been proposed to support the growing use of complex models, agent‐specific optimization, and comprehensive model comparison and result exploration. Such work is being driven by advances in interactive machine learning and the desire of end‐users to understand and engage with the modeling process. In this state‐of‐the‐art report, we catalogue recent advances in the visualization community for supporting predictive analytics. First, we define the scope of predictive analytics discussed in this article and describe how visual analytics can support predictive analytics tasks in a predictive visual analytics (PVA) pipeline. We then survey the literature and categorize the research with respect to the proposed PVA pipeline. Systems and techniques are evaluated in terms of their supported interactions, and interactions specific to predictive analytics are discussed. We end this report with a discussion of challenges and opportunities for future research in predictive visual analytics.     

Visual requirements analytics: a framework and case study

For many software projects, keeping requirements on track needs an effective and efficient path from data to decision. Visual analytics creates such a path that enables the human to extract insights by interacting with the relevant information. While various requirements visualization techniques exist, few have produced end-to-end value to practitioners. In this paper, we advance the literature on visual requirements analytics by characterizing its key components and relationships in a framework. We follow the goal–question–metric paradigm to define the framework by teasing out five conceptual goals (user, data, model, visualization, and knowledge), their specific operationalizations, and their interconnections. The framework allows us to not only assess existing approaches, but also create tool enhancements in a principled manner. We evaluate our enhanced tool support through a case study where massive, heterogeneous, and dynamic requirements are processed, visualized, and analyzed. Working together with practitioners on a contemporary software project within its real-life context leads to the main finding that visual analytics can help tackle both open-ended visual exploration tasks and well-structured visual exploitation tasks in requirements engineering. In addition, the study helps the practitioners to reach actionable decisions in a wide range of areas relating to their project, ranging from theme and outlier identification, over requirements tracing, to risk assessment. Overall, our work illuminates how the data-to-decision analytical capabilities could be improved by the increased interactivity of requirements visualization.     

基于语义分析的OWL文档聚类研究

借助目前丰富的网络资源，将同一主题的现存Ontology知识聚类，提供给领域专家或用户进行二次精化和集成是Ontology研究领域的一个重要课题．OWL是目前用于表示和交换Ontology信息的基本标准．本文从OWL的语义本质出发，考虑了知识之间的继承性及复杂类比较和模糊集运算的相似性，提出一种计算OWL文档语义相似性的方式，并和层次聚类算法集成完成了对OWL文档集的聚类实验．实验结果说明本文提出的算法对自动生成和手工建立的OWL文档集都有很好的效果。     

基于RDFS的本体集成方法

现实中的数据源一般具有半结构化、异构性和分布性等特点,而许多应用需要对不同的相关数据源进行联合操作。本体集成是解决知识共享、异构数据源语义互操作的有力工具。首先分析了本体集成的原因,提出了本体集成时应遵循的4条基本原则;然后提出了一种基于RDFS图闭包的本体集成方法,该方法将RDFS本体抽象为图模型,根据RDFS推理规则和扩展规则生成RDFS本体的图闭包,在此基础上进行本体集成,同时提出了几种计算实体间相似度的方法。最后,将该方法与FCA-merge和COMA++进行实验对比。     

Reasoning of fuzzy relational databases with fuzzy ontologies

A significant interest developed regarding the problem of describing databases with expressive knowledge representation techniques in recent years, so that database reasoning may be handled intelligently. Therefore, it is possible and meaningful to investigate how to reason on fuzzy relational databases (FRDBs) with fuzzy ontologies. In this paper, we first propose a formal approach and an automated tool for constructing fuzzy ontologies from FRDBs, and then we study how to reason on FRDBs with constructed fuzzy ontologies. First, we give their respective formal definitions of FRDBs and fuzzy Web Ontology Language (OWL) ontologies. On the basis of this, we propose a formal approach that can directly transform an FRDB (including its schema and data information) into a fuzzy OWL ontology (consisting of the fuzzy ontology structure and instance). Furthermore, following the proposed approach, we implement a prototype construction tool called FRDB2FOnto. Finally, based on the constructed fuzzy OWL ontologies, we investigate how to reason on FRDBs (e.g., consistency, satisfiability, subsumption, and redundancy) through the reasoning mechanism of fuzzy OWL ontologies, so that the reasoning of FRDBs may be done automatically by means of the existing fuzzy ontology reasoner.© 2012 Wiley Periodicals, Inc.     

Co-located collaborative visual analytics around a tabletop display

Co-located collaboration can be extremely valuable during complex visual analytics tasks. We present an exploratory study of a system designed to support collaborative visual analysis tasks on a digital tabletop display. Fifteen participant pairs employed Cambiera, a visual analytics system, to solve a problem involving 240 digital documents. Our analysis, supported by observations, system logs, questionnaires, and interview data, explores how pairs approached the problem around the table. We contribute a unique, rich understanding of how users worked together around the table and identify eight types of collaboration styles that can be used to identify how closely people work together while problem solving. We show how the closeness of teams’ collaboration and communication influenced how they performed on the task overall. We further discuss the role of the tabletop for visual analytics tasks and derive design implications for future co-located collaborative tabletop problem solving systems.     

A method for building ontology-based electronic document management systems for quality standards—the case study of the ISO/TS 16949:2002 automotive standard

Different methods and methodologies have been developed for building ontologies. However, neither of these methods consider to build an ontology with characteristics of an electronic document management system (EDMS) nor define the basic classes to begin the ontology. In this paper we propose a method, called “OntoDocMan”, to build an ontology-based EDMS which captures the knowledge associated with a company’s processes related to a quality standard. We leverage the use of ontology tools, such as Protégé, to obtain the functionality of an EDMS. Our method complements and details the On-To-Knowledge methodology during its ontology development phase. The essential point of our method is to make it easy to develop an EDMS for quality standards by means of an ontology-based system. The OntoDocMan method is illustrated by a case study for developing an ontology based on the ISO/TS 16949, which is a Technical Specification (TS) and we show that the Ontology system is friendly and easy to use as any EDMS. Different from common EDMS, our ontology-based EDMS is developed without any programming. In addition, we have discovered that this kind of ontology-based EDMS is an excellent tool for helping auditors to search and validate information, besides new employees can learn about the company’s processes.     

Crowdsourcing techniques to create a fuzzy subset of SNOMED CT for semantic tagging of medical documents

Ontologies and other schemes are useful for allowing semantic tagging of documents for many applications on the semantic web. Representing uncertainty on the semantic web is becoming increasingly common, using ontologies and other techniques. Ontology and declarative tools allow documents using concepts contained in these ontologies to be reasoned about using computer systems. Very large ontologies and vocabularies have been created; however, users may find it difficult to select the correct concept or term when there are large numbers of items that on face value appear to represent the same idea. Creating subsets of ontologies is a popular approach to solve this problem but this may not fit well with the need to deal with complex domains. However, crowdsourcing techniques, which harness the power of large groups, may be more effective than document analysis or expert opinion. In crowdsourcing, large numbers of people collaborate by performing relatively simple tasks usually using applications distributed via the World Wide Web. This approach is being tested in the medical domain using a very large clinical vocabulary, SNOMED CT.