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.     

Verification of conceptual schemata based on hybrid object-oriented and logic paradigm

Contemporary conceptual modelling languages are concerned with the representational adequacy of knowledge about a universe of discourse and with the efficient organization of this knowledge in structures that help overcome the problems of size and complexity in the modelled reality. In the paper it is argued that a conceptual modelling language should also facilitate the verification of captured requirements by exercising the conceptual schemata derived from the use of such a language. A conceptual modelling language is presented that is based on a hybrid representation scheme that makes use of object-oriented and logic approaches, and it is shown how this language can be used to verify requirements during the development of information systems.     

Definition of a problem-sensitive conceptual modelling language: foundations and application to software engineering

A conceptual modelling language should provide constructors that can be used to represent the conceptualisation of a problem considering the problem domain. However, software engineering has traditionally focused on implementation concepts.This paper considers the appropriate generic conceptualisation theoretical aspects to identify the conceptual elements for which constructors have to be provided in a problem-sensitive conceptual modelling language. These elements match the formal definition of any conceptualisation and are derived from natural language. By looking at these elements, we have defined a conceptual modelling language that has been successfully applied in knowledge engineering and software engineering.     

A SKOS-based framework for Subject Ontologies to improve learning experiences

Subject Ontologies represent conceptualizations of disciplinary domains in which concepts symbolize topics that are relevant for the considered domain and are associated each other by means of specific relations. Usually, these kind of lightweight ontologies are adopted in knowledge-based educational environments to enable semantic organization and search of resources and, in other cases, to support personalization and adaptation features for learning and teaching experiences. For this reason, applying effective management methodologies for Subject Ontologies is a crucial aspect in engineering the environments. In particular, this paper proposes an approach to use SKOS (a Semantic Web-based vocabulary providing a standard way to represent knowledge organization systems) for modelling subject ontologies. Moreover, the paper underlines the main benefits of SKOS. It focuses on alternative strategies for storing and accessing ontologies in order to support the knowledge sharing, knowledge reusing, planning, assessment, customization and adaptation processes related to learning scenarios. The results of an early experimentation allowed the authors defining a framework able to support, from both methodological and technological viewpoints, the use of Subject Ontologies in the context of a Semantic Web-based Educational System. The defined framework has high performances in terms of response and this may really improve the user experience.     

A knowledge acquisition methodology to ontology construction for information retrieval from medical documents

Abstract: Vast amounts of medical information reside within text documents, so that the automatic retrieval of such information would certainly be beneficial for clinical activities. The need for overcoming the bottleneck provoked by the manual construction of ontologies has generated several studies and research on obtaining semi-automatic methods to build ontologies. Most techniques for learning domain ontologies from free text have important limitations. Thus, they can extract concepts so that only taxonomies are generally produced although there are other types of semantic relations relevant in knowledge modelling. This paper presents a language-independent approach for extracting knowledge from medical natural language documents. The knowledge is represented by means of ontologies that can have multiple semantic relationships among concepts.     

Towards Ontology Generation from Tables

At the heart of today's information-explosion problems are issues involving semantics, mutual understanding, concept matching, and interoperability. Ontologies and the Semantic Web are offered as a potential solution, but creating ontologies for real-world knowledge is nontrivial. If we could automate the process, we could significantly improve our chances of making the Semantic Web a reality. While understanding natural language is difficult, tables and other structured information make it easier to interpret new items and relations. In this paper we introduce an approach to generating ontologies based on table analysis. We thus call our approach TANGO (Table ANalysis for Generating Ontologies). Based on conceptual modeling extraction techniques, TANGO attempts to (i) understand a table's structure and conceptual content; (ii) discover the constraints that hold between concepts extracted from the table; (iii) match the recognized concepts with ones from a more general specification of related concepts; and (iv) merge the resulting structure with other similar knowledge representations. TANGO is thus a formalized method of processing the format and content of tables that can serve to incrementally build a relevant reusable conceptual ontology.     

Axiom-based ontology matching

Abstract: Managing multiple ontologies is now a core question in most of the applications that require semantic interoperability. The semantic web is surely the most significant application of this report: the current challenge is not to design, develop and deploy domain ontologies but to define semantic correspondences among multiple ontologies covering overlapping domains. In this paper, we introduce a new approach of ontology matching named axiom-based ontology matching. As this approach is founded on the use of axioms, it is mainly dedicated to heavyweight ontologies, but it can also be applied to lightweight ontologies as a complementary approach to the current techniques based on the analysis of natural language expressions, instances and/or taxonomical structures of ontologies. This new matching paradigm is defined in the context of the conceptual graphs model, where the projection (i.e. the main operator for reasoning with conceptual graphs which corresponds to homomorphism of graphs) is used as a means to semantically match the concepts and the relations of two ontologies through the explicit representation of the axioms in terms of conceptual graphs. We also introduce an ontology of representation, called MetaOCGL, dedicated to the reasoning of heavyweight ontologies at the meta-level.     

Action-oriented conceptual modelling

The aim of this paper is to show how speech act theory can be used in systems development as a theoretical foundation for conceptual modelling. With the traditional notion of the conceptual model as an image of reality, the predominant modelling problem is to analyse how the external reality should be mapped into, and represented in, the system in a ‘true’ way. In contrast to this, we maintain that the main modelling problem should be to analyse the communication acts performed by use of the system within its business context. This implies an integration of traditional conceptual modelling with action-oriented business modelling based on speech act theory. With such an approach, it is possible to reconcile traditional conceptual modelling and the pragmatic aspects of language and computer use. It is argued that such reconciliation is essential to arrive at systems that provide relevant information to users and in which users can trace responsibilities for information, actions and commitments made.     

Frame-based representation of philosophical systems using a knowledge engineering tool

This article addresses the methodological problem of the non-linear representation of philosophical systems in a computerized knowledge base. It is a problem of knowledge representation as defined in the field of artificial intelligence. Instead of a purely theoretical discussion of the issue, we present selected results of a practical experiment which has in itself some theoretical significance. We show how one can represent different philosophies using CODE, a knowledge engineering system developed by artificial intelligence researchers. The hypothesis is that such a computer based representation of philosophical systems can give insight into their conceptual structure. We argue that computer aided text analysis can apply knowledge representation tools and techniques developed in artificial intelligence and we estimate how philosophers as well as knowledge engineers could gain from this cross-fertilization. This paper should be considered as an experiment report on the use of knowledge representation techniques in computer aided text analysis. It is part of a much broader project on the representation of conceptual structures in an expert system. However, we intentionally avoided technical issues related to either Computer Science or History of Philosophy to focus on the benefit to enhance traditional humanistic studies with tools and methods developed in AI on the one hand and the need to develop more appropriate tools on the other. Gilbert Boss is professor of Philosophy at Université Laval, Québec. He is the author of several books, including Les machines à penser. L'homme et l'ordinateur,Zurich: Grand Midi, 1987, and John Stuart Mill. Induction et utilité,Paris: PUF, 1990. His main fields of research are modern philosophy, philosophy of culture, philosophical discourse and systems, artificial intelligence. Maryvonne Longeart is professor of Computer Science at UQAH, Hull, Québec. Her research interests include object oriented design methodologies and knowledge representation. She received a PhD in Philosophy from the University of Ottawa in 1978 and a BSc in computer science in 1987. She contributed to the Encyclopédie philosophique universelle,PUF, 1992 and published several papers on the representation of complex conceptual systems.Douglas Skuce is professor of computer science at the University of Ottawa. He has worked in the area of knowledge engineering since his PhD (McGill, 1977). During 1978-present he has been developing the CODE system for various applications, including terminology and software development. Currently, his interests include designing ontologies for knowledge exchange and coupling large corpora to systems such as CODE.     

A generic, object-oriented case-knowledge representation scheme, and its integration into a wider information management scenario

Abstract: A knowledge base management system (KBMS) realises a combination of techniques found in database management systems and knowledge-based systems. At the data model and knowledge representation level, many systems of this kind constitute a marriage of the relational data model and the rule-based reasoning. Experience has shown that either approach is restricted in the way it can express the demanding information and knowledge structures required for applications like decision support systems. Two new technologies offer an exciting new integrated approach to knowledge management. Object-oriented database management systems (OODBMS) provide an object model that supports powerful abstraction mechanisms to facilitate the modelling of highly structured information. Whereas case-based reasoning (CBR) systems are knowledge bases which organise their capabilities around a memory of past cases and the notion of similarity. Both types of system are built upon two fundamental concepts: 1) the retrieval of entities with potentially complex structure, called objects in the former, and cases in the latter type of system; 2) the organisation of those entities in collections with common characteristics. In an OODBMS such collections are termed extents, and in CBR they are usually called categories. In either system, the conceptual meta notion to represent both, objects as well as extents, and cases as well as categories, is the class.
Revolving around a Conceptual Case Class and extending a standard object model, this paper proposes a novel and general approach to represent case-knowledge and to build KBMSs. The work presented here is a spin-off of the design of an object query language within the ESPRIT project Lynx.     

一种本体知识文摘方法的研究和实现

本体作为领域知识的表示方法，已经成为语义Web的基础。本体通常由领域专家建立，用于表示领域中概念以及概念与概念之间的关系。但这也使得普通用户难以理解本体中描述的信息。普通用户往往希望本体中的信息能够以自然语言的形式描述。这正是本文讨论的主要问题。本文采用分治策略，利用基于嵌套复杂模板的解决方案，设计并实现了本体知识文摘的算法。我们开发了一个原型系统SWARMS，并将该文摘算法进行了运用。初步的实验表明，本文提出的方法取得较好的结果。     

Wordnet as a resource for NLP: Yorick Wilks’ reflections on the ontological debate—and how to cope with ambiguity and vagueness of natural language

The paper argues that Guarino is right that ontologies are different from thesauri and similar objects, but not in the ways he believes: they are distinguished from essentially linguistic objects like thesauri and hierarchies of conceptual relations because they unpack, ultimately, in terms of sets of objects and individuals. However this is a lonely status, and without much application outside strict scientific and engineering disciplines, and of no direct relevance to language processing (NLP). More interesting structures, of NLP relevance, that encode conceptual knowledge, cannot be subjected to the “cleaning up” techniques that Guarino advocates, because his conditions are too strict to be applicable, and because the terms used in such structures retain their language-like features of ambiguity and vagueness, and in a way that cannot be eliminated by reference to sets of objects, as it can be in ontologies in the narrow sense. Wordnet is a structure that remains useful to NLP, and has within it features of both types (ontologies and conceptual hierarchies) and its function and usefulness will remain, properly, resistant to Guarino’s techniques, because those rest on a misunderstanding about concepts. The ultimate way out of such disputes can only come from automatic construction and evaluation procedures for conceptual and ontological structures from data, which is to say, corpora.     

国家知识基础设施中的数学知识表示

数学知识表示是知识表示中的一个重要方面,是数学知识检索、自动定理机器证明、智能教学系统等的基础.根据在设计NKI(national knowledge infrastructure)的数学知识表示语言中遇到的问题,并在讨论了数学对象的本体论假设的基础上提出了两种数学知识的表示方法:一种是以一个逻辑语言上的公式为属性值域的描述逻辑;另一种是以描述逻辑描述的本体为逻辑语言的一部分的一阶逻辑.在前者的表示中,如果对公式不作任何限制,那么得到的知识库中的推理不是可算法化的;在后者的表示中,以描述逻辑描述的本体中的推理是可算法化的,而以本体为逻辑语言的一部分的一阶逻辑所表示的数学知识中的推理一般是不可算法化的.因此,在表示数学知识时,需要区分概念性的知识(本体中的知识)和非概念性的知识(用本体作为语言表示的知识).框架或者描述逻辑可以表示和有效地推理概念性知识,但如果将非概念性知识加入到框架或知识库中,就可能使得原来可以有效推理的框架所表示的知识库不存在有效的推理算法,甚至不存在推理算法.为此,建议在表示数学知识时,用框架或描述逻辑来表示概念性知识;然后,用这样表示的知识库作为逻辑语言的一部分,以表示非概念性知识.     

炮兵军事概念模型语义描述方法研究

炮兵军事概念模型是对炮兵领域知识的第一次抽象,它具有极其丰富的语义。描述炮兵军事概念模型时引入语义网技术,按知识工程的过程与步骤,逐步描述相关炮兵概念模型的语义,并在Prot6g6工具的支持下以本体的形式表示概念模型及其相关知识。然后用推理器对具有语义的概念模型进行语义的一致性检验、约束冲突检查并报告检查结果,通过检查维护语义的一致性。该方法能够全面、严格、简便地表示与管理、维护炮兵军事概念模型。     

Ontological Engineering and Mathematical Knowledge Management: A Formalization of Projective Geometry

The work presented in this paper deals with the formalization of the ontology underlying projective geometry. This formalization is done by using the conceptual graph model which has been defined in the Artificial Intelligence community. Through this experiment, we endeavour to show that applying knowledge representation techniques to mathematical fields is a relevant way to improve the reliability and efficiency of tools dedicated to mathematical knowledge management. Our proposal is based on the construction of knowledge bases (defined according to ontologies) which must be considered as the core of any mathematical knowledge management tool such as mathematical search engines on the web, mathematical intelligent tutoring systems, mathematical theorem provers, etc. This paper also aims at highlighting the contributions provided by ontological engineering when dealing with mathematical knowledge management.     

网络时代人工智能研究与发展

50多年来,人工智能在模式识别、知识工程、机器人等领域已经取得重大成就,但是离真正的人类智能还相差甚远.当今网络时代,人工智能科学要在学科交叉研究中实现人工智能的发展与创新,会更加关注认知科学、脑科学、生物智能、物理学、网络科学、计算机科学与人工智能之间的交叉渗透,重视认知物理学的研究;自然语言是人工智能研究知识表示无法回避的直接对象,要对语言中的概念建立起能够定量表示的不确定性转换模型,发展不确定性人工智能;要利用现实生活中复杂网络的小世界模型和无标度特性,把网络拓扑作为知识表示的一种新方法,研究网络拓扑的演化与网络动力学行为,研究网络智能.对这3个重要方向进行了阐述,并提出了具体建议.     

Supporting command and control training functions in the emergency management domain using cognitive systems engineering

The design and implementation of MERMAIDS, a computer-based training system in the domain of emergency command and control, is described. The research investigates the use of cognitive systems engineering and information management tools for modelling and representing training knowledge of emergency system operators. We propose a decision-centric human-computer interface as a new method of supporting computer-based modelling in the domain of emergency systems. Several interacting themes in information management relevant to emergency response planning are discussed.     

Ontology languages for the Semantic Web

Ontologies have proven to be an essential element in many applications. They are used in agent systems, knowledge management systems, and e-commerce platforms. They can also generate natural language, integrate intelligent information, provide semantic-based access to the Internet, and extract information from texts in addition to being used in many other applications to explicitly declare the knowledge embedded in them. However, not only are ontologies useful for applications in which knowledge plays a key role, but they can also trigger a major change in current Web contents. This change is leading to the third generation of the Web-known as the Semantic Web-which has been defined as the conceptual structuring of the Web in an explicit machine-readable way. New ontology-based applications and knowledge architectures are developing for this new Web. A common claim for all of these approaches is the need for languages to represent the semantic information that this Web requires-solving heterogeneous data exchange in this heterogeneous environment. Our goal is to help developers find the most suitable language for their representation needs.     

Ontological emergence scheme in self-organized and emerging systems

This paper presents the concept of “Ontological Emergence”, a process that seeks to adapt an ontology to the changes and new components in a self-organized and emergent system, through the application of a set of rules that allows the emergence of a new conceptualization (emerging concepts). The Ontological Emergence provides the structuration of the information and knowledge that could be generated in the system, creating conceptual models that can adequately represent the new behavior that is emerging. It arises from the need to represent ontologically a conceptualization of a reality that is dynamic, which cannot be pre-defined or pre-determined, in order to generate emerging knowledge models that follows the scalability and the evolution of it. In that sense, in this paper is proposed an “Ontological Emergence Scheme” based on a set of processes of registration, monitoring, analysis and adaptation of the various conceptual models that interact in the system, as well as on some processing rules in regard to requirements and information of the context, in order to allow the ontological emergence. In this proposal scheme, the Meta-ontologies guide the ontological emergence process through the definition of general categories, to facilitate the integration of concepts from different ontologies or data sources. Finally, the paper presents some case studies, showing its utility in self-organized and emergent systems.