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.     

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.     

Personal ontologies

Corporations can suffer from too much information, and it is often inaccessible, inconsistent, and incomprehensible. The corporate solution entails knowledge management techniques and data warehouses. The paper discusses the use of the personal ontology. The promising approach is an organization scheme based on a model of an office and its information, an ontology, coupled with the proper tools for using it     

Contextualizing ontologies

Ontologies are shared models of a domain that encode a view which is common to a set of different parties. Contexts are local models that encode a party’s subjective view of a domain. In this paper, we show how ontologies can be contextualized, thus acquiring certain useful properties that a pure shared approach cannot provide. We say that an ontology is contextualized or, also, that it is a contextual ontology, when its contents are kept local, and therefore not shared with other ontologies, and mapped with the contents of other ontologies via explicit (context) mappings. The result is Context OWL (C-OWL), a language whose syntax and semantics have been obtained by extending the OWL syntax and semantics to allow for the representation of contextual ontologies.     

Matching weak informative ontologies

Dear editor,Most existing ontology matching methods utilize literal in-formation to discover alignments[1,2].However,some lit-eral information in ontologies ma...     

Knowledge processes and ontologies

In this article, we present an approach for ontology-based knowledge management (KM) that includes a tool suite and a methodology for developing ontology-based KM systems. It builds on the distinction between knowledge processes and knowledge metaprocesses, and is illustrated by CHAR (Corporate History AnalyzeR), a KM system for corporate history analysis.     

Defining structural properties of ontologies

To define properties of ontologies exactly, a set of ontology models in the form of marked graphs is proposed. To each class of properties its own graph model is attached with established general scheme, way of interpretation, and rules of extracting the structure from the ontology text. Structural properties of ontologies are unambiguously given in terms of graph models. These definitions can be used to evaluate properties of particular ontologies, which is done in two stages. The first stage deals with constructing internal models of ontologies in the form of graph models. At the second stage, the values of structural properties of ontology are obtained using the corresponding graph models of the evaluated ontology and according to the definitions of these properties.     

Bottom-up Evaluation of Datalog with Negation

Declarative semantics gives the meaning of a logic program in terms of properties,while the procedural semantics gives the meaning in terms of the execution or evaluation of the program.From the database point of view,the procedural semantics of the program is equally important.This paper focuses on the study of the bottom-up evaluation of the WFM semantics of datalog‘ programs.To compute the WFM,first,the stability transformation is revisited,and a new operator Op and its fixpoint are defined. Based on this,a fixpoint semantics,called oscillating fixpoint model semantics,is defined.Then,it is shown that for any datalog‘ program the oscillating fixpoint model is identical to its WFM.So,the oscillating fixpoint model can be viewed as an alternative (constructive) definition of WFM.The underlying operation (or transformation) for reaching the oscillating fixpoint provides a potential of bottom-up evaluation.For the sake of computational feasibility,the strongly range-restricted program is considered,and an algorithm used to compute the oscillating fixpoint is described.     

Fluid ontologies for digital museums

With the advent and accessibility of the Internet, artistic and indigenous communities are beginning to realize how digital technologies can be used as a means for documenting and preserving their histories and cultures. However, it is not yet clear what knowledge architectures are most appropriate for creating a digital museum in order to facilitate an effective collection, organization, conservation, and experience of cultural and artistic heritage. In this paper, we discuss the concept of fluid ontologies, a novel, dynamic structure for organizing and browsing knowledge in a digital museum. Fluid ontologies are flexible knowledge structures that evolve and adapt to communities interest based on contextual information articulated by human contributors, curators, and viewers, as well as artificial bots that are able to track interaction histories and infer relationships among knowledge pieces and preferences of viewers. Fluid ontologies allow for a tighter coupling between communities interests and the browsing structure of a digital museum. We present the key ideas behind the use of fluid ontologies within the context of digital museum design and seminal work in metadata/dynamic ontologies, particularly as it pertains to objects of cultural heritage, and discuss these characteristics in three concrete examples: (1) Village Voice, an online agora that ties together the narratives created by a group of Somali refugees using an iteration of community-designed ontologies, (2) Eventspace, a node-based collaborative archive for design activities, and (3) Tribal Peace, an online digital museum still under construction and evaluation that uses proactive agents to tie distributed Kumeyaay, Luiseno, and Cupeno reservations together in their quest to achieve greater political sovereignty .     

Feature-based opinion mining through ontologies

The idiosyncrasy of the Web has, in the last few years, been altered by Web 2.0 technologies and applications and the advent of the so-called Social Web. While users were merely information consumers in the traditional Web, they play a much more active role in the Social Web since they are now also data providers. The mass involved in the process of creating Web content has led many public and private organizations to focus their attention on analyzing this content in order to ascertain the general public’s opinions as regards a number of topics. Given the current Web size and growth rate, automated techniques are essential if practical and scalable solutions are to be obtained. Opinion mining is a highly active research field that comprises natural language processing, computational linguistics and text analysis techniques with the aim of extracting various kinds of added-value and informational elements from users’ opinions. However, current opinion mining approaches are hampered by a number of drawbacks such as the absence of semantic relations between concepts in feature search processes or the lack of advanced mathematical methods in sentiment analysis processes. In this paper we propose an innovative opinion mining methodology that takes advantage of new Semantic Web-guided solutions to enhance the results obtained with traditional natural language processing techniques and sentiment analysis processes. The main goals of the proposed methodology are: (1) to improve feature-based opinion mining by using ontologies at the feature selection stage, and (2) to provide a new vector analysis-based method for sentiment analysis. The methodology has been implemented and thoroughly tested in a real-world movie review-themed scenario, yielding very promising results when compared with other conventional approaches.     

基于co-location模式和本体的地址选择算法

选址问题是任何一个商业机构都要面临的重大决策问题之一,它受多种因素制约,比如社会经济学、地质学、生态学以及决策者的特定需求等。现有的选址方法（通常被经济学家采用）大多利用主观评价,可扩展性差。空间co-location模式挖掘是空间数据挖掘的一个重要研究方向。一个频繁co-location模式是一组空间特征的子集,它们的实例在空间中频繁关联。利用co-location模式的这种特征间“共存”关系,提出了一种基于co-location模式的地址选择算法,该算法基于本体描述空间数据的分类信息,并在本体的指导下对用户感兴趣的兴趣点（Point of Interest）进行关键co-location模式挖掘,同时针对实际情况对数据进行了预处理以增加算法的有效性。在真实数据集（北京市的兴趣点数据）上的评估实验显示该算法具有较高的准确率,选择的地址具有高可靠性。     

Bottom-up reuse for multi-level testing

Lifecycle models divide the test process into consecutive test levels that are considered independently. This strict separation obstructs the view on the test process as a whole and fails to reflect the commonalities across test levels. Multi-level testing is an emerging approach that addresses the challenge of integrating test levels, putting particular emphasis on embedded systems. In this paper, we introduce a test level integration strategy based on reuse that is called bottom-up reuse. In addition, we present a test level instrument that seamlessly supports this strategy: multi-level test cases. We also provide a case study that reflects the positive results we have obtained in practice so far and demonstrates the feasibility of our test level integration approach. Bottom-up reuse and multi-level test cases lead to testing earlier on in the development process while reducing the effort required by test specification, test design, and test implementation.     

Requirements-oriented methodology for evaluating ontologies

Many applications benefit from the use of a suitable ontology but it can be difficult to determine which ontology is best suited to a particular application. Although ontology evaluation techniques are improving as more measures and methodologies are proposed, the literature contains few specific examples of cohesive evaluation activity that links ontologies, applications and their requirements, and measures and methodologies. In this paper, we present ROMEO, a requirements-oriented methodology for evaluating ontologies, and apply it to the task of evaluating the suitability of some general ontologies (variants of sub-domains of the Wikipedia category structure) for supporting browsing in Wikipedia. The ROMEO methodology identifies requirements that an ontology must satisfy, and maps these requirements to evaluation measures. We validate part of this mapping with a task-based evaluation method involving users, and report on our findings from this user study.     

Bottom-up development of multicellular digital organisms

How multicellular creatures can be developed from single cells into multicellular forms is a basic question in research into artificial life. In this paper, we propose a possible anser to that question by developing multicellular digital organisms from singe-cell digital organisms. We have done experiments on a computer. First we defined a model of a single-cell organism which has open-ended evolvability and a self-replicating mechanism, so these single-cell digital organisms can develop into multicellular creatures which have better adaptability than single-cell ones under certain environments. The phenomena of cellular differentiation and cell self-organization were observed during the development of the multicellular digital organisms. This work was presented in part at the Fourth International Symposium on Artificial Life and Robotics, Oita, Japan, January 19–22, 1999     

Anomalies in ontologies with rules

For the development of practical semantic applications, ontologies are commonly used with rule extensions. Prominent examples of semantic applications not only are Semantic Wikis, Semantic Desktops, but also advanced Web Services and agents. The application of rules increases the expressiveness of the underlying knowledge in many ways. Likewise, the integration not only creates new challenges for the design process of such ontologies, but also existing evaluation methods have to cope with the extension of ontologies by rules.Since the verification of Owl ontologies with rule extensions is not tractable in general, we propose to verify ontologies at the symbolic level by using a declarative approach: With the new language Datalog^?, known anomalies can be easily specified and tested in a compact manner. We introduce supplements to existing verification techniques to support the design of ontologies with rule enhancements, and we focus on the detection of anomalies that especially occur due to the combined use of rules and ontological definitions.     

Mining Bayesian networks out of ontologies

Probabilistic reasoning is an essential feature when dealing with many application domains. Starting with the idea that ontologies are the right way to formalize domain knowledge and that Bayesian networks are the right tool for probabilistic reasoning, we propose an approach for extracting a Bayesian network from a populated ontology and for reasoning over it. The paper presents the theory behind the approach, its design and examples of its use.