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.     

本体推理机及应用

随着语义web的迅速发展,基于本体的应用越来越多,本体推理机的应用也越来越为重要。本体推理机可用于推理和查询语义,是实现语义Web的关键技术之一。介绍了本体推理机及其分类、结构以及相关技术,介绍了几种典型的本体推理机;分析了本体推理机的应用及其发展的趋势。     

混合的本体原子分解方法

原子分解是理解本体内部模块结构的有效途径。以局部化模块抽取为基本操作的原子分解方法可用于强表达力的SROIQ本体,但效率较低。基于有向超图的本体模型能够显式地反映原子的依赖关系,但只局限于弱表达力的EL本体。提出一种混合的原子分解算法,首先利用有向超图表示EL子本体,形成部分原子分解,利用模块抽取方法添加剩余非EL公理,得到本体的全部原子分解。以生物医学本体作为测试数据,实验表明,这种混合的原子分解算法能够有效减少运行时间。与传统的基于模块抽取的方法相比,原子分解效率平均提高6.7倍。     

使用OWL DL形式化表达对象角色建模模型

对象角色建模(ORM)方法已应用于本体工程,因此需要将ORM模型转换为OWL DL公理,以便将ORM本体发布到语义Web上,同时还可使用支持DL的推理机来检查ORM本体的语义一致性和冗余问题。通过模型语义分析、模型等价转换、引入新的运算符和特性等方法,提出将ORM模型形式化表达为OWL DL公理的规则。除了外部唯一约束等四种约束外,其他形态的ORM模型都可以形式化表达为OWL DL公理。     

Reasoning of fuzzy relational databases with fuzzy ontologies

A significant interest developed regarding the problem of describing databases with expressive knowledge representation techniques in recent years, so that database reasoning may be handled intelligently. Therefore, it is possible and meaningful to investigate how to reason on fuzzy relational databases (FRDBs) with fuzzy ontologies. In this paper, we first propose a formal approach and an automated tool for constructing fuzzy ontologies from FRDBs, and then we study how to reason on FRDBs with constructed fuzzy ontologies. First, we give their respective formal definitions of FRDBs and fuzzy Web Ontology Language (OWL) ontologies. On the basis of this, we propose a formal approach that can directly transform an FRDB (including its schema and data information) into a fuzzy OWL ontology (consisting of the fuzzy ontology structure and instance). Furthermore, following the proposed approach, we implement a prototype construction tool called FRDB2FOnto. Finally, based on the constructed fuzzy OWL ontologies, we investigate how to reason on FRDBs (e.g., consistency, satisfiability, subsumption, and redundancy) through the reasoning mechanism of fuzzy OWL ontologies, so that the reasoning of FRDBs may be done automatically by means of the existing fuzzy ontology reasoner.© 2012 Wiley Periodicals, Inc.     

A parallel computing architecture for high-performance OWL reasoning

The Web Ontology Language (OWL) is a widely used knowledge representation language for describing knowledge in application domains by using classes, properties, and individuals. Ontology classification is an important and widely used service that computes a taxonomy of all classes occurring in an ontology. It can require significant amounts of runtime, but most OWL reasoners do not support any kind of parallel processing. We present a novel thread-level parallel architecture for ontology classification, which is ideally suited for shared-memory SMP servers, but does not rely on locking techniques and thus avoids possible race conditions. We evaluated our prototype implementation with a set of real-world ontologies. Our experiments demonstrate a very good scalability resulting in a speedup that is linear to the number of available cores.     

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     

A Rule-Based Object-Oriented OWL Reasoner

In this paper, we describe O-DEVICE, a memory-based knowledge-based system for reasoning and querying OWL ontologies by implementing RDF/OWL entailments in the form of production rules in order to apply the formal semantics of the language. Our approach is based on a transformation procedure of OWL ontologies into an object-oriented schema and the application of inference production rules over the generated objects in order to implement the various semantics of OWL. In order to enhance the performance of the system, we introduce a dynamic approach of generating production rules for ABOX reasoning and an incremental approach of loading ontologies. O-DEVICE is built over the CLIPS production rule system, using the object-oriented language COOL to model and handle ontology concepts and RDF resources. One of the contributions of our work is that we enable a well-known and efficient production rule system to handle OWL ontologies. We argue that although native OWL rule reasoners may process ontology information faster, they lack some of the key features that rule systems offer, such as the efficient manipulation of the information through complex rule programs. We present a comparison of our system with other OWL reasoners, showing that O-DEVICE can constitute a practical rule environment for ontology manipulation.     

Extracting a justification for OWL ontologies by critical axioms

Extracting justifications for web ontology language (OWL) ontologies is an important mission in ontology engineering. In this paper, we focus on black-box techniques which are based on ontology reasoners. Through creating a recursive expansion procedure, all elements which are called critical axioms in the justification are explored one by one. In this detection procedure, an axiom selection function is used to avoid testing irrelevant axioms. In addition, an incremental reasoning procedure has been proposed in order to substitute series of standard reasoning tests w.r.t. satisfiability. It is implemented by employing a pseudo model to detect “obvious” satisfiability directly. The experimental results show that our proposed strategy for extracting justifications for OWL ontologies by adopting incremental expansion is superior to traditional Black-box methods in terms of efficiency and performance.     

A multiplatform energy-aware OWL reasoner benchmarking framework

Performance evaluation is increasingly relevant for Web Ontology Language (OWL) reasoners, due to the expanding availability of knowledge corpuses on the Web, the growing variety of applications, and the rise to prominence of mobile and pervasive computing. Motivated mainly by the difficulty of comparing reasoning engines in the Semantic Web of Things (SWoT), this paper introduces evOWLuator, a novel approach and a multiplatform framework devised to be both flexible and expandable. It features integration of traditional and mobile/embedded engines as well as ontology dataset management, reasoning test execution, and report generation. A case study consisting of an experimental setting for time, memory peak and energy footprint evaluation with eight reasoners and four different platforms allows showcasing usage and validating features and usability of the tool.     

OWL rules: A proposal and prototype implementation

Although the OWL Web Ontology Language adds considerable expressive power to the Semantic Web it does have expressive limitations, particularly with respect to what can be said about properties. We present the Semantic Web Rule Language (SWRL), a Horn clause rules extension to OWL that overcomes many of these limitations. SWRL extends OWL in a syntactically and semantically coherent manner: the basic syntax for SWRL rules is an extension of the abstract syntax for OWL DL and OWL Lite; SWRL rules are given formal meaning via an extension of the OWL DL model-theoretic semantics; SWRL rules are given an XML syntax based on the OWL XML presentation syntax; and a mapping from SWRL rules to RDF graphs is given based on the OWL RDF/XML exchange syntax. We discuss the expressive power of SWRL, showing that the ontology consistency problem is undecidable, provide several examples of SWRL usage, and discuss a prototype implementation of reasoning support for SWRL.     

基于互补概念和搜索图的MUPS求解优化方法

本体调试是人工智能中非标准推理任务之一,对于本体工程具有很重要的意义.本文结合互补概念与基于术语集的搜索图提出极小不可满足子术语集求解的优化方法.首先,通过判断扩展的术语集是否包含互补概念,确定该子术语集是否需要进行概念可满足性检测,可以有效减少推理机的调用次数.接着,根据术语集扩展过程构造一个术语集搜索图,分别采用宽度优先搜索和深度优先搜索策略快速查找不可满足子术语集.该优化方法一方面减少了待测子术语集的规模,另一方面提高了查找不可满足子术语集对应的节点的查找效率.最后,实现了本文给出的各类优化算法并与现有的黑盒优化算法进行了比较.实验效果表明,本文方法从推理机调用次数和待测术语集规模方面均优于现有的MUPS求解方法,能够有效提高求解术语集MUPS的效率.