Towards a cognitive foundation for knowledge representation

Abstract. Knowledge engineering, knowledge management and conceptual modelling are concerned with representing knowledge of business and organizational domains. These research areas use ontologies for knowledge representation. Ontologies are understood either in the philosophical sense as firm metaphysical commitments or in the looser sense of dictionaries or taxonomies.
This paper critically examines the understanding and use of ontologies and knowledge representation languages in information systems (IS) research and application. As ontologies are intended to be conceptualizations of a perceived reality, they should reflect the empirically observed reality. This motivates proposing psychology of language as a reference discipline for knowledge engineering and knowledge management. Natural language is argued to reflect the cognitive concepts we use to think about and perceive the world around us. These cognitive concepts are the relevant terms with which to structure and represent knowledge about the world.
Psychology of language can provide empirical justification for a particular set of concepts to represent knowledge. This paper draws on psycho-linguistic research to develop a proposal for a system of cognitive structures. This is argued to provide the relevant concepts on which to found knowledge representation schemata for knowledge engineering, knowledge management and conceptual modelling.     

Automatic extraction of OWL ontologies from UML class diagrams: a semantics-preserving approach

Full implementation of the Semantic Web requires widespread availability of OWL ontologies. Manual ontology development using current OWL editors remains a tedious and cumbersome task that requires significant understanding of the new ontology language and can easily result in a knowledge acquisition bottleneck. On the other hand, abundant domain knowledge has been specified by existing database schemata such as UML class diagrams. Thus developing an automatic tool for extracting OWL ontologies from existing UML class diagrams is helpful to Web ontology development. In this paper we propose an automatic, semantics-preserving approach for extracting OWL ontologies from existing UML class diagrams. This approach establishes a precise conceptual correspondence between UML and OWL through a semantics-preserving schema translation algorithm. The experiments with our implemented prototype tool, UML2OWL, show that the proposed approach is effective and a fully automatic ontology extraction is achievable. The proposed approach and tool will facilitate the development of Web ontologies and the realization of semantic interoperations between existing Web database applications and the Semantic Web.     

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.     

电子商务环境下产品本体构建技术研究

产品本体通过构建产品类层次及其属性描述为交易双方提供了对产品共享的通用的理解。针对目前电子商务中标准产品本体缺乏产品属性描述的问题，根据电子商务网站中产品信息多以表格形式组织和描述的特点，提出以联合国标准产品与服务分类代码（UNSPSC）为核心本体，结合表格处理技术的半自动产品本体构建方法。以Web表格为对象，对Web表格的识别、表格规范化、单元产品本体及全局产品本体建立进行了研究。这种半自动的本体建立方法可以解决电子商务中的产品信息模型因缺乏标准化的属性描述而产生不一致性，实现对核心产品本体的扩展和补充。     

DOF: a generic approach of domain ontology fuzzification

Although recent studies on the Semantic Web have focused on crisp ontologies and knowledge representation, they have paid less attention to imprecise knowledge. However, the results of these studies constitute a Semantic Web that can answer requests almost perfectly with respect to precision. Nevertheless, they ensure low recall. As such, we propose in this research work a new generic approach of fuzzification that which allows a semantic representation of crisp and fuzzy data in a domain ontology. In the framework of our real case study, the obtained illustrate that our approach is highly better than the crisp one in terms of completeness, comprehensiveness, generality, comprehension and shareability.     

OIL: an ontology infrastructure for the Semantic Web

Researchers in artificial intelligence first developed ontologies to facilitate knowledge sharing and reuse. Ontologies play a major role in supporting information exchange across various networks. A prerequisite for such a role is the development of a joint standard for specifying and exchanging ontologies. The authors present OIL, a proposal for such a standard. Ontologies applied to the World Wide Web are creating the Semantic Web.     

Transforming arbitrary tables into logical form with TARTAR

The tremendous success of the World Wide Web is countervailed by efforts needed to search and find relevant information. For tabular structures embedded in HTML documents, typical keyword or link-analysis based search fails. The Semantic Web relies on annotating resources such as documents by means of ontologies and aims to overcome the bottleneck of finding relevant information. Turning the current Web into a Semantic Web requires automatic approaches for annotation since manual approaches will not scale in general. Most efforts have been devoted to automatic generation of ontologies from text, but with quite limited success. However, tabular structures require additional efforts, mainly because understanding of table contents requires the comprehension of the logical structure of the table on the one hand, as well as its semantic interpretation on the other. The focus of this paper is on the automatic transformation and generation of semantic (F-Logic) frames from table-like structures. The presented work consists of a methodology, an accompanying implementation (called TARTAR) and a thorough evaluation. It is based on a grounded cognitive table model which is stepwise instantiated by the methodology. A typical application scenario is the automatic population of ontologies to enable query answering over arbitrary tables (e.g. HTML tables).     

Learning concept hierarchies from textual resources for ontologies construction

Ontologies play a very important role in knowledge management and the Semantic Web, their use has been exploited in many current applications. Ontologies are especially useful because they support the exchange and sharing of information. Ontology learning from text is the process of deriving high-level concepts and their relations. An important task in ontology learning from text is to obtain a set of representative concepts to model a domain and organize them into a hierarchical structure (taxonomy) from unstructured information. In the process of building a taxonomy, the identification of hypernym/hyponym relations between terms is essential. How to automatically build the appropriate structure to represent the information contained in unstructured texts is a challenging task. This paper presents a novel method to obtain, from unstructured texts, representative concepts and their taxonomic relationships in a specific knowledge domain. This approach builds a concept hierarchy from a specific-domain corpus by using a clustering algorithm, a set of linguistic patterns, and additional contextual information extracted from the Web that improves the discovery of the most representative hypernym/hyponym relationships. A set of experiments were carried out using four different corpora. We evaluated the quality of the constructed taxonomies against gold standard ontologies, the experiments show promising results.     

Learning to Share Meaning in a Multi-Agent System

The development of the semantic Web will require agents to use common domain ontologies to facilitate communication of conceptual knowledge. However, the proliferation of domain ontologies may also result in conflicts between the meanings assigned to the various terms. That is, agents with diverse ontologies may use different terms to refer to the same meaning or the same term to refer to different meanings. Agents will need a method for learning and translating similar semantic concepts between diverse ontologies. Only until recently have researchers diverged from the last decade's common ontology paradigm to a paradigm involving agents that can share knowledge using diverse ontologies. This paper describes how we address this agent knowledge sharing problem of how agents deal with diverse ontologies by introducing a methodology and algorithms for multi-agent knowledge sharing and learning in a peer-to-peer setting. We demonstrate how this approach will enable multi-agent systems to assist groups of people in locating, translating, and sharing knowledge using our Distributed Ontology Gathering Group Integration Environment (DOGGIE) and describe our proof-of-concept experiments. DOGGIE synthesizes agent communication, machine learning, and reasoning for information sharing in the Web domain.     

Semi-automatic construction of domain ontology for agent reasoning

One of the key elements of the Semantic Web technologies is domain ontologies and those ontologies are important constructs for multi-agent system. The Semantic Web relies on domain ontologies that structure underlying data enabling comprehensive and transportable machine understanding. It takes so much time and efforts to construct domain ontologies because these ontologies can be manually made by domain experts and knowledge engineers. To solve these problems, there have been many researches to semi-automatically construct ontologies. Most of the researches focused on relation extraction part but manually selected terms for ontologies. These researches have some problems. In this paper, we propose a hybrid method to extract relations from domain documents which combines a named relation approach and an unnamed relation approach. Our named relation approach is based on the Hearst’s pattern and the Snowball system. We merge a generalized pattern scheme into their methods. In our unnamed relation approach, we extract unnamed relations using association rules and clustering method. Moreover, we recommend candidate relation names of unnamed relations. We evaluate our proposed method by using Ziff document set offered by TREC.     

A philosophy‐driven entity classification and enrichment for ontology mapping

Abstract: Ontologies are intended to facilitate semantic interoperability among distributed and intelligent information systems. Because of the distributed nature of the World Wide Web, Web ontologies have been developing in multiple forms of heterogeneity. For interoperating among information systems through heterogeneous ontologies, ontology mapping is a prerequisite process to generate alignment between two ontologies. In order to improve alignment accuracy, our approach is to clarify and enrich the semantics of ontological entities before mapping. For this purpose, we present a semi‐automatic framework of entity classification and enrichment by applying three philosophical notions: identity condition, existential rigidity, and external dependency. Our objective is to supply a set of philosophy‐driven anchors into ontologies for their mapping process by using a sortal taxonomy as a background knowledge model.     

本体演化管理研究

自W3C主席TimBerncrs-Lee首先提出了语义web的概念后,它正在成为计算机信息处理领域当前研究的热点之一。本体将在“语义Web”中起到至关重要的作用,它通过定义精确的共享术语,以提供某一特定领域可重用的知识。但是这些知识并不是静态的,而是随着时问的推移不断演化。领域的改变、自适应不同的任务、或概念模型的改变都要求本体的变更。随着本体开发变成一个泛化的、协同的过程,本体版本控制和演化管理已成为本体研完中一个重要的领域。本文首先对本体演化的原因和所带来的问题进行分析,然后讨论了本体演化管理的关键技术,着重强调了Web上本体标识和本体变化机制的定义,并对今后的研究工作进行了展望。     

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.     

Developing a modular hydrogeology ontology by extending the SWEET upper-level ontologies

Upper-level ontologies comprise general concepts and properties which need to be extended to include more diverse and specific domain vocabularies. We present the extension of NASA's Semantic Web for Earth and Environmental Terminology (SWEET) ontologies to include part of the hydrogeology domain. We describe a methodology that can be followed by other allied domain experts who intend to adopt the SWEET ontologies in their own discipline. We have maintained the modular design of the SWEET ontologies for maximum extensibility and reusability of our ontology in other fields, to ensure inter-disciplinary knowledge reuse, management, and discovery.The extension of the SWEET ontologies involved identification of the general SWEET concepts (classes) to serve as the super-class of the domain concepts. This was followed by establishing the special inter-relationships between domain concepts (e.g., equivalence for vadose zone and unsaturated zone), and identifying the dependent concepts such as physical properties and units, and their relationship to external concepts. Ontology editing tools such as SWOOP and Protégé were used to analyze and visualize the structure of the existing OWL files. Domain concepts were introduced either as standalone new classes or as subclasses of existing SWEET ontologies. This involved changing the relationships (properties) and/or adding new relationships based on domain theories. In places, in the Owl files, the entire structure of the existing concepts needed to be changed to represent the domain concept more meaningfully. Throughout this process, the orthogonal structure of SWEET ontologies was maintained and the consistency of the concepts was tested using the Racer reasoner. Individuals were added to the new concepts to test the modified ontologies. Our work shows that SWEET ontologies can successfully be extended and reused in any field without losing their modular or reference structure, or disrupting their URI links.     

From tables to frames

Turning the current Web into a Semantic Web requires automatic approaches for annotation of existing data since manual approaches will not scale in general. We here present an approach for automatic generation of F-Logic frames out of tables which subsequently supports the automatic population of ontologies from table-like structures. The approach consists of a methodology, an accompanying implementation and a thorough evaluation. It is based on a grounded cognitive table model which is stepwise instantiated by our methodology.     

Ontology learning from biomedical natural language documents using UMLS

The generation of new knowledge is continuous in biomedical domains, thus biomedical literature is becoming harder to understand. Ontologies provide vocabulary standardization, so they can be helpful to facilitate the understanding of biomedical texts. In this work, a methodology for building biomedical ontologies from texts is presented. This approach relies on natural language processing and incremental knowledge acquisition techniques to obtain the relevant concepts and relations to be included in an OWL ontology. Additionally, we provide an algorithm to connect the isolated concepts regions in the ontology using UMLS. We also discuss in this paper the experiment carried out to validate our approach and its positive results in terms of performance and scalability.     

Ontologies: How can They be Built?

Ontologies are an important component in many areas, such as knowledge management and organization, electronic commerce and information retrieval and extraction. Several methodologies for ontology building have been proposed. In this article, we provide an overview of ontology building. We start by characterizing the ontology building process and its life cycle. We present the most representative methodologies for building ontologies from scratch, and the proposed techniques, guidelines and methods to help in the construction task. We analyze and compare these methodologies. We describe current research issues in ontology reuse. Finally, we discuss the current trends in ontology building and its future challenges, namely, the new issues for building ontologies for the Semantic Web.     

A semantic role labelling-based framework for learning ontologies from Spanish documents

Currently, most of the information available in the Web is adapted primarily for human consumption, but there is so much information that can no longer be processed by a person in a reasonable time, either in digital or physical formats. To solve this problem, the idea of the Semantic Web arose. The Semantic Web deals with adding machine-readable information to Web pages. Ontologies represent a very important element of this web, as they provide a valid and robust structure to represent knowledge based on concepts, relations, axioms, etc. The need for overcoming the bottleneck provoked by the manual construction of ontologies has generated several studies and research on obtaining semiautomatic methods to learn ontologies. In this sense, this paper proposes a new ontology learning methodology based on semantic role labeling from digital Spanish documents. The method makes it possible to represent multiple semantic relations specially taxonomic and partonomic ones in the standardized OWL 2.0. A set of experiments has been performed with the approach implemented in educational domain that show promising results.     

一种有效的本体排序算法MIDSRank

本体已经在很多的领域中得到了广泛的应用,网络上的本体也越来越多,为了节约本体构建的成本避免从头构建本体,人们经常首先从网络上获取候选者,然后再以此为基础构建自己的本体.而随之而来的本体排序问题则成为一个研究热点.通过对现有本体排序算法的总结与分析,将现有本体排序算法划分为两大类,分别阐述了其基本思想以及存在的问题.然后,提出了一种经过改进的本体排序方法,该方法结合了原有方法的优点并提高了本体搜索的质量.最后讨论了未来研究的方向和注意事项.