From “Onto-GeoNoesis” to “Onto-Genesis”: The Design of Geographic Ontologies

An important issue in geographic ontological research is the ability to design new ontologies. In this context, we first explore the desiderata of domain ontologies in terms of their constituting elements: i.e., the lexicon, concepts, relations, and axioms. Furthermore, we touch upon several characteristics of geographic concepts, which have puzzled geographic information scientists, and present critical topics of geographic ontological research. Based on the previous aspects of the problem, and guided by prior work of analyzing existent geographic ontologies, we have identified their qualities and deficiencies with regard to completeness and adequacy. This meta-ontological approach has guided us in presenting herein, a framework for generating robust geographic ontologies, which will comply with the semantics of the concepts of the specific domain.     

OntoPlus: Text-driven ontology extension using ontology content,structure and co-occurrence information

This paper addresses the process of semi-automatic text-driven ontology extension using ontology content, structure and co-occurrence information. A novel OntoPlus methodology is proposed for semi-automatic ontology extension based on text mining methods. It allows for the effective extension of the large ontologies, providing a ranked list of potentially relevant concepts and relationships given a new concept (e.g., glossary term) to be inserted in the ontology. A number of experiments are conducted, evaluating measures for ranking correspondence between existing ontology concepts and new domain concepts suggested for the ontology extension. Measures for ranking are based on incorporating ontology content, structure and co-occurrence information. The experiments are performed using a well known Cyc ontology and textual material from two domains – finances and, fisheries & aquaculture. Our experiments show that the best results are achieved by combining content, structure and co-occurrence information. Furthermore, ontology content and structure seem to be more important than co-occurrence for our data in the financial domain. At the same time, ontology content and co-occurrence seem to have higher importance for our fisheries & aquaculture domain.     

OBSERVER: An Approach for Query Processing in Global Information Systems Based on Interoperation Across Pre-Existing Ontologies

There has been an explosion in the types, availability and volume of data accessible in an information system, thanks to the World Wide Web (the Web) and related inter-networking technologies. In this environment, there is a critical need to replace or complement earlier database integration approaches and current browsing and keyword-based techniques with concept-based approaches. Ontologies are increasingly becoming accepted as an important part of any concept or semantics based solution, and there is increasing realization that any viable solution will need to support multiple ontologies that may be independently developed and managed. In particular, we consider the use of concepts from pre-existing real world domain ontologies for describing the content of the underlying data repositories. The most challenging issue in this approach is that of vocabulary sharing, which involves dealing with the use of different terms or concepts to describe similar information. In this paper, we describe the architecture, design and implementation of the OBSERVER system. Brokering across the domain ontologies is enabled by representing and utilizing interontology relationships such as (but not limited to) synonyms, hyponyms and hypernyms across terms in different ontologies. User queries are rewritten by using these relationships to obtain translations across ontologies. Well established metrics like precision and recall based on the extensions underlying the concepts are used to estimate the loss of information, if any.     

Ontology Design for Scientific Theories That Make Probabilistic Predictions

Scientific theories that make predictions about observable quantities can be evaluated by their fit to existing data and can be used for predictions on new cases. The authors' goal is to publish such theories along with observational data and the ontologies needed to enable the interoperation of the theories and the data. This article is about designing ontologies that take into account the defining properties of classes. The authors show how a multidimensional design paradigm based on Aristotelian definitions is natural, can easily be represented in OWL, and can provide random variables that provide structure for theories that make probabilistic predictions. They also show how such ontologies can be the basis for representing observational data and probabilistic theories in their primary application domain of geology.     

A novel approach to generate MCQs from domain ontology: Considering DL semantics and open-world assumption

Ontologies are structures, used for knowledge representation, which model domain knowledge in the form of concepts, roles, instances and their relationships. This knowledge can be exploited by an assessment system in the form of multiple choice questions (MCQs). The existing approaches, which use ontologies expressed in the Web Ontology Language (OWL) for MCQ generation, are limited to simple concept related questions — “What is C?” or “Which of the following is an example of C?” (where $C$ is a concept symbol) — or analogy type questions involving roles. There are no efforts in the literature which make use of the terminological axioms in the ontology such as existential, universal and cardinality restrictions on concepts and roles for MCQ generation. Also, there are no systematic methods for generating incorrect answers (distractors) from ontologies. Distractor generation process has to be given much importance, since the generated distractors determine the quality and hardness of an MCQ. We propose two new MCQ generation approaches, which generate MCQs that are very useful and realistic in conducting assessment tests, and the corresponding distractor generating techniques. Our distractor generation techniques, unlike other methods, consider the open-world assumption, so that the generated MCQs will always be valid (falsity of distractors is ensured). Furthermore, we present a measure to determine the difficulty level (a value between 0 and 1) of the generated MCQs. The proposed system is implemented, and experiments on specific ontologies have shown the effectiveness of the approaches. We also did an empirical study by generating question items from a real-world ontology and validated our results with the help of domain experts.     

NCBO Resource Index: Ontology-based search and mining of biomedical resources

The volume of publicly available data in biomedicine is constantly increasing. However, these data are stored in different formats and on different platforms. Integrating these data will enable us to facilitate the pace of medical discoveries by providing scientists with a unified view of this diverse information. Under the auspices of the National Center for Biomedical Ontology (NCBO), we have developed the Resource Index – a growing, large-scale ontology-based index of more than twenty heterogeneous biomedical resources. The resources come from a variety of repositories maintained by organizations from around the world. We use a set of over 200 publicly available ontologies contributed by researchers in various domains to annotate the elements in these resources. We use the semantics that the ontologies encode, such as different properties of classes, the class hierarchies, and the mappings between ontologies, in order to improve the search experience for the Resource Index user. Our user interface enables scientists to search the multiple resources quickly and efficiently using domain terms, without even being aware that there is semantics “under the hood.”     

Bridging Ontologies and Conceptual Schemas in Geographic Information Integration

Integration of geographic information has increased in importance because of new possibilities arising from the interconnected world and the increasing availability of geographic information. Ontologies support the creation of conceptual models and help with information integration. In this paper, we propose a way to link the formal representation of semantics (i.e., ontologies) to conceptual schemas describing information stored in databases. The main result is a formal framework that explains a mapping between a spatial ontology and a geographic conceptual schema. The mapping of ontologies to conceptual schemas is made using three different levels of abstraction: formal, domain, and application levels. At the formal level, highly abstract concepts are used to express the schema and the ontologies. At the domain level, the schema is regarded as an instance of a generic data model. At the application level, we focus on the particular case of geographic applications. We also discuss the influence of ontologies in both the traditional and geographic systems development methodologies, with an emphasis on the conceptual design phase.     

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 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.     

Survey of modular ontology techniques and their applications in the biomedical domain

In the past several years, various ontologies and terminologies such as the Gene Ontology have been developed to enable interoperability across multiple diverse medical information systems. They provide a standard way of representing terms and concepts thereby supporting easy transmission and interpretation of data for various applications. However, with their growing utilization, not only has the number of available ontologies increased considerably, but they are also becoming larger and more complex to manage. Toward this end, a growing body of work is emerging in the area of modular ontologies where the emphasis is on either extracting and managing "modules" of an ontology relevant to a particular application scenario (ontology decomposition) or developing them independently and integrating into a larger ontology (ontology composition). In this paper, we investigate state-of-the-art approaches in modular ontologies focusing on techniques that are based on rigorous logical formalisms as well as well-studied graph theories. We analyze and compare how such approaches can be leveraged in developing tools and applications in the biomedical domain. We conclude by highlighting some of the limitations of the modular ontology formalisms and put forward additional requirements to steer their future development.     

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。     

Approaches for semantic interoperability between domain ontologies

Abstract: Domain ontologies and knowledge-based systems have become very important in the agent and semantic web communities. As their use has increased, providing means of resolving semantic differences has also become very important. In this paper we survey the approaches that have been proposed for providing interoperability among domain ontologies. We also discuss some key issues that still need to be addressed if we are to move from semi-automated to fully automated approaches to providing consensus among heterogeneous ontologies.     

Ontology-based concept similarity in Formal Concept Analysis

Both domain ontologies and Formal Concept Analysis (FCA) aim at modeling concepts, although with different purposes. In the literature, a promising research area concerns the role of FCA in ontology engineering, in particular, in supporting the critical task of reusing independently developed domain ontologies. With this regard, the possibility of evaluating concept similarity is acquiring an increasing relevance, since it allows the identification of different concepts that are semantically close. In this paper, an ontology-based method for assessing similarity between FCA concepts is proposed. Such a method is intended to support the ontology engineer in difficult activities that are becoming fundamental in the development of the Semantic Web, such us ontology merging and ontology mapping and, in particular, it can be used in parallel to existing semi-automatic tools relying on FCA.     

Modelling internal logistics systems through ontologies

Industry is facing an era characterised by unpredictable market changes and by a turbulent competitive environment. The key to compete in such a context is to achieve high degrees of responsiveness by means of high flexibility and rapid reconfiguration capabilities. The deployment of modular solutions seems to be part of the answer to face these challenges. Semantic modelling and ontologies may represent the needed knowledge representation to support flexibility and modularity of production systems, when designing a new system or when reconfiguring an existing one. Although numerous ontologies for production systems have been developed in the past years, they mainly focus on discrete manufacturing, while logistics aspects, such as those related to internal logistics and warehousing, have not received the same attention. The paper aims at offering a representation of logistics aspects, reflecting what has become a de-facto standard terminology in industry and among researchers in the field. Such representation is to be used as an extension to the already-existing production systems ontologies that are more focused on manufacturing processes. The paper presents the structure of the hierarchical relations within the examined internal logistics elements, namely Storage and Transporters, structuring them in a series of classes and sub-classes, suggesting also the relationships and the attributes to be considered to complete the modelling. Finally, the paper proposes an industrial example with a miniload system to show how such a modelling of internal logistics elements could be instanced in the real world.     

利用本体和主题词表的集成构造RDF模式

为了减少语义异构性带来的信息发现、集成和存取的困难,论述了语义元数据构造,提出了通过集成现存的本体和主题词表构造元数据模式的一种新方法,即元数据模式构造的两步方法:在主题词T和本体O之间的连接关系规范;概念主题词表的自动构造。这个集成基于主题词术语和本体概念之间的蕴含关系规范,并产生具体应用的元数据模式,同时也给出利用结果元数据模式构造RDF模式的过程。     

Automatic construction and enrichment of informal ontologies: A survey

The conceptualization of knowledge required for an efficient processing of textual data is usually represented as ontologies. Depending on the knowledge domain and tasks, different types of ontologies are constructed: formal ontologies, which involve axioms and detailed relations between concepts; taxonomies, which are hierarchically organized concepts; and informal ontologies, such as Internet encyclopedias created and maintained by user communities. Manual construction of ontologies is a time-consuming and costly process requiring the participation of experts; therefore, in recent years, there have appeared many systems that automate this process in a greater or lesser degree. This paper provides an overview of methods for automatic construction and enrichment of ontologies, with the focus being placed on informal ontologies.     

NKI中的本体、框架和逻辑理论

NKI(国家知识基础设施)是一个大规模知识库,它用框架来表示本体中的概念,用Hom逻辑程序作为自动推理.给出NKI中的本体、框架和逻辑理论的形式表示以及形式表示之间的转换,并证明如果将本体、框架和逻辑理论看作是3个范畴,则这些转换是这3个范畴之间的函子.这个结果保证了在NKI中,基于Horn逻辑程序的推理关于用本体和框架表示的知识库是正确的.     

Identifying relevant concept attributes to support mapping maintenance under ontology evolution

The success of distributed and semantic-enabled systems relies on the use of up-to-date ontologies and mappings between them. However, the size, quantity and dynamics of existing ontologies demand a huge maintenance effort pushing towards the development of automatic tools supporting this laborious task. This article proposes a novel method, investigating different types of similarity measures, to identify concepts’ attributes that served to define existing mappings. The obtained experimental results reveal that our proposed method allows to identify the relevant attributes for supporting mapping maintenance, since we found correlations between ontology changes affecting the identified attributes and mapping changes.