A novel approach to ontology classification

Ontology classification–the computation of the subsumption hierarchies for classes and properties–is a core reasoning service provided by all OWL reasoners known to us. A popular algorithm for computing the class hierarchy is the so-called Enhanced Traversal (ET) algorithm. In this paper, we present a new classification algorithm that attempts to address certain shortcomings of ET and improve its performance. Apart from classification of classes, we also consider object and data property classification. Using several simple examples, we show that the algorithms commonly used to implement these tasks are incomplete even for relatively weak ontology languages. Furthermore, we show that property classification can be reduced to class classification, which allows us to classify properties using our optimised algorithm. We implemented all our algorithms in the OWL reasoner HermiT. The results of our performance evaluation show significant performance improvements on several well-known ontologies.     

Integrating Concepts and Knowledge in Large Content Networks

Large content networks like the World Wide Web contain huge amounts of information that have the potential of being integrated because their components fit within common concepts and/or are connected through hidden, implicit relationships. One attempt at such an integration is the program called the “Web of Data,” which is an evolution of the Semantic Web. It targets semi-structured information sources such as Wikipedia and turns them into fully structured ones in the form of Web-based databases like DBpedia and then integrates them with other public databases such as Geonames. On the other hand, the vast majority of the information residing on the Web is still totally unstructured, which is the starting point for our approach that aims to integrate unstructured information sources. For this purpose, we exploit techniques from Probabilistic Topic Modeling, in order to cluster Web pages into concepts (topics), which are then related through higher-level concept networks; we also make implicit semantic relationships emerge between single Web pages. The approach has been tested through a number of case studies that are here described. While the applicative focus of the research reported here is on knowledge integration on the specific and relevant case of the WWW, the wider aim is to provide a framework for integration generally applicable to all complex content networks where information propagates from multiple sources.     

ComR: a combined OWL reasoner for ontology classification

Ontology classification, the problem of computing the subsumption hierarchies for classes (atomic concepts), is a core reasoning service provided by Web Ontology Language (OWL) reasoners. Although general-purpose OWL 2 reasoners employ sophisticated optimizations for classification, they are still not efficient owing to the high complexity of tableau algorithms for expressive ontologies. Profile-specific OWL 2 EL reasoners are efficient; however, they become incomplete even if the ontology contains only a small number of axioms that are outside the OWL 2 EL fragment. In this paper, we present a technique that combines an OWL 2 EL reasoner with an OWL 2 reasoner for ontology classification of expressive SROIQ. To optimize the workload, we propose a task decomposition strategy for identifying the minimal non-EL subontology that contains only necessary axioms to ensure completeness. During the ontology classification, the bulk of the workload is delegated to an efficient OWL 2 EL reasoner and only the minimal non- EL subontology is handled by a less efficient OWL 2 reasoner. The proposed approach is implemented in a prototype ComR and experimental results show that our approach offers a substantial speedup in ontology classification. For the wellknown ontology NCI, the classification time is reduced by 96.9% (resp. 83.7%) compared against the standard reasoner Pellet (resp. the modular reasoner MORe).     

The WBC's semantic Web activity: an update

The World Wide Web consortium is a group of about 370 international companies working together to develop recommendations, or Web standards, for the World Wide Web. W3G announced final approval of two key semantic Web technologies: the revised RDF and OWL. RDF and OWL are semantic Web standards. These standard formats for data sharing span application, enterprise, and community boundaries share the same information, even if they don't share the same software.     

Evaluation of artificial intelligent techniques to secure information in enterprises

Information security paradigm is under a constant threat in enterprises particularly. The extension of World Wide Web and rapid expansion in size and types of documents involved in enterprises has generated many challenges. Extensive research has been conducted to determine the effective solutions to detect and respond but still the space is felt for improvement. Factors that hinder the development of an accurate detection and response techniques have shown links to the amount of data processing involved, number of protocols and application running across and variation in users’ requirements and responses. This paper is aimed at discussing the current issue in artificial intelligent (A.I.) techniques that could help in developing a better threat detection algorithm to secure information in enterprises. It is also investigated that the current information security techniques in enterprises have shown an inclination towards A.I. Conventional techniques for detection and response mostly requires human efforts to extract characteristics of malicious intent, investigate and analyze abnormal behaviors and later encode the derived results into the detection algorithm. Instead, A.I. can provide a direct solution to these requirements with a minimal human input. We have made an effort in this paper to discuss the current issues in information security and describe the benefits of artificially trained techniques in security process. We have also carried out survey of current A.I. techniques for IDS. Limitations of the techniques are discussed to identify the factors to be taken into account for efficient performance. Lastly, we have provided a possible research direction in this domain.     

FRESG:一种模糊描述逻辑推理机

作为语义Web的逻辑基础,描述逻辑可为其提供推理支持,因而描述逻辑推理机是语义Web付诸应用的根本载体.基于模糊描述逻辑F-ALC(G),设计并实现了模糊描述逻辑推理机FRESG1.0,它支持含有模糊用户定制数据类型谓词的模糊数据类型信息的表示和推理.简要介绍了FRESG1.0的主要推理功能以及所使用的编程语言;详细描述了FRESG1.0的总体结构及其主要组成部分的设计与实现,其中着重阐述了FRESG1.0推理机的特色和设计实现过程中所采用的算法、实现技术.通过测试案例可以看出,FRESG1.0推理机具备较强的推理能力,尤其具备目前其他推理机所不具备的推理模糊用户定制数据信息的能力.FRESG1.0具有较强的模块化结构,有很好的可扩展性,为今后对其进行深入研究和扩展奠定了基础.     

A Flexible Ontology Reasoning Architecture for the Semantic Web

Knowledge-based systems in the semantic Web era can make use of the power of the semantic Web languages and technologies, in particular those related to ontologies. Recent research has shown that user-defined data types are very useful for semantic Web and ontology applications. The W3C semantic Web best practices and development working group has set up a task force to address this issue. Very recently, OWL-Eu and OWL-E, two decidable extensions of the W3C standard ontology language OWL DL, have been proposed to support customized data types and customized data type predicates, respectively. In this paper, we propose a flexible reasoning architecture for these two expressive semantic Web ontology languages and describe our prototype implementation of the reasoning architecture, based on the well-known FaCT DL reasoner, which witnesses the two key flexibility features of our proposed architecture: 1) It allows users to define their own data types and data type predicates based on built-in ones and 2) new data type reasoners can be added into the architecture without having to change the concept reasoner     

Verifying feature models using OWL

Feature models are widely used in domain engineering to capture common and variant features among systems in a particular domain. However, the lack of a formal semantics and reasoning support of feature models has hindered the development of this area. Industrial experiences also show that methods and tools that can support feature model analysis are badly appreciated. Such reasoning tool should be fully automated and efficient. At the same time, the reasoning tool should scale up well since it may need to handle hundreds or even thousands of features a that modern software systems may have. This paper presents an approach to modeling and verifying feature diagrams using Semantic Web OWL ontologies. We use OWL DL ontologies to precisely capture the inter-relationships among the features in a feature diagram. OWL reasoning engines such as FaCT++ are deployed to check for the inconsistencies of feature configurations fully automatically. Furthermore, a general OWL debugger has been developed to tackle the disadvantage of lacking debugging aids for the current OWL reasoner and to complement our verification approach. We also developed a CASE tool to facilitate visual development, interchange and reasoning of feature diagrams in the Semantic Web environment.     

DLEJena: A practical forward-chaining OWL 2 RL reasoner combining Jena and Pellet

This paper describes DLEJena, a practical reasoner for the OWL 2 RL profile that combines the forward-chaining rule engine of Jena and the Pellet DL reasoner. This combination is based on rule templates, instantiating at run-time a set of ABox OWL 2 RL/RDF Jena rules dedicated to a particular TBox that is handled by Pellet. The goal of DLEJena is to handle efficiently, through instantiated rules, the OWL 2 RL ontologies under direct semantics, where classes and properties cannot be at the same time individuals. The TBox semantics are treated by Pellet, reusing in that way efficient and sophisticated TBox DL reasoning algorithms. The experimental evaluation shows that DLEJena achieves more scalable ABox reasoning than the direct implementation of the OWL 2 RL/RDF rule set in the Jena’s production rule engine, which is the main target of the system. DLEJena can be also used as a generic framework for applying an arbitrary number of entailments beyond the OWL 2 RL profile.     

基于Ontology和描述逻辑推理的Web题目资源检索

Web网上存在着大量题目资源，学生在学习过程中需要准确找到与其所学知识真正相吻合的题目。但是从题目的语言表述往往很难获得其语义信息，合适的题目难以找到。该文提出了一种基于Ontology和描述逻辑推理的Web题目资源检索方案。该方案通过为Web题目资源添加语义注释，并通过描述逻辑推理完成基于语义的题目资源检索，使学生获得与其所学知识语义相关的题目。采用OWL描述Ontology、使用推理机RACER实现描述逻辑推理。     

Combining OWL ontologies using -Connections

The standardization of the Web Ontology Language (OWL) leaves (at least) two crucial issues for Web-based ontologies unsatisfactorily resolved, namely how to represent and reason with multiple distinct, but linked ontologies, and how to enable effective knowledge reuse and sharing on the Semantic Web.In this paper, we present a solution for these fundamental problems based on -Connections. We aim to use -Connections to provide modelers with suitable means for developing Web ontologies in a modular way and to provide an alternative to the owl:imports construct.With such motivation, we present in this paper a syntactic and semantic extension of the Web Ontology language that covers -Connections of OWL-DL ontologies. We show how to use such an extension as an alternative to the owl:imports construct in many modeling situations. We investigate different combinations of the logics , and for which it is possible to design and implement reasoning algorithms, well-suited for optimization.Finally, we provide support for -Connections in both an ontology editor, SWOOP, and an OWL reasoner, Pellet.     

An MDE-based methodology for closed-world integrity constraint checking in the semantic web

Ontology-based data-centric systems support open-world reasoning. Therefore, for these systems, Web Ontology Language (OWL) and Semantic Web Rule Language (SWRL) are not suitable for expressing integrity constraints based on the closed-world assumption. Thus, the requirement of integrating the open-world assumption of OWL/SWRL with closed-world integrity constraint checking is inevitable. SPARQL, recommended by World Wide Web (W3C), is a query language for RDF graphs, and many research studies have shown that it is a perfect candidate for closed-world constraint checking for ontology-based data-centric applications. In this regard, many research studies have been performed to transform integrity constraints into SPARQL queries where some studies have shown the limitations of partial expressivity of knowledge bases while performing the indirect transformations, whereas others are limited to a platform-specific implementation. To address these issues, this paper presents a flexible and formal methodology that employs Model-Driven Engineering (MDE) to model closed-world integrity constraints for open-world reasoning. The proposed approach offers semantic validation of data by expressing integrity constraints at both the model level and the code level. Moreover, straightforward transformations from OWL/SWRL to SPARQL can be performed. Finally, the methodology is demonstrated via a real-world case study of water observations data.