Ontology-based mammography annotation and Case-based Retrieval of breast masses

This paper describes ontology-based annotation of mammography and a Case-based Retrieval approach for breast masses from digital mammography archive. We first present our Mammography Annotation Ontology focusing on its main concepts and relationships, as well as the annotation tool. Then, we propose a model for similarity calculation between breast masses based on their high, mid and low-level features. We use Semantic Query-enhanced Web Rule Language (SQWRL) to process retrieval of similar masses from annotated mammography collection in OWL. We give both retrieving process and results we obtained from experimentations, in detail.     

OWL 2: The next step for OWL

Since achieving W3C recommendation status in 2004, the Web Ontology Language (OWL) has been successfully applied to many problems in computer science. Practical experience with OWL has been quite positive in general; however, it has also revealed room for improvement in several areas. We systematically analyze the identified shortcomings of OWL, such as expressivity issues, problems with its syntaxes, and deficiencies in the definition of OWL species. Furthermore, we present an overview of OWL 2—an extension to and revision of OWL that is currently being developed within the W3C OWL Working Group. Many aspects of OWL have been thoroughly reengineered in OWL 2, thus producing a robust platform for future development of the language.     

Modelling and Reasoning in Metamodelling Enabled Ontologies

Ontologies are expected to play an important role in many application domains, as well as in software engineering in general. One problem with using ontologies within software engineering is that while UML, a widely used standard for specifying and constructing the models for a software-intensive system, has a four-layer metamodelling architecture, the standard Web Ontology Language (OWL) does not support reasoning over layered metamodels. OWL 2 provides simple metamodelling by using a punning approach, however, the interpretation function is different based on the context, which leads to non-intuitive results. The OWL FA Language has a well defined metamodelling architecture. However, there is no study and tool for supporting reasoning over OWL FA. In this paper, we discuss some reasoning tasks in OWL FA. We also introduce the OWL FA Tool kit, a simple tool kit for manipulating and reasoning with OWL FA.     

OntoSTEP: Enriching product model data using ontologies

The representation and management of product lifecycle information is critical to any manufacturing organization. Different modeling languages are used at different lifecycle stages, for example STEP’s EXPRESS may be used at a detailed design stage, while UML may be used for initial design stages. It is necessary to consolidate product information created using these different languages to build a coherent knowledge base. In this paper, we present an approach to enable the translation of STEP schema and its instances to Ontology Web Language (OWL). This gives a model–which we call OntoSTEP–that can easily be integrated with any OWL ontologies to create a semantically rich model. As an example, we combine geometry information represented in STEP with non-geometry information, such as function and behavior, represented using the NIST’s Core Product Model (CPM). A plug-in for Protégé is developed to automate the different steps of the translation. As additional benefits, reasoning, inference procedures, and queries can be performed on enriched legacy CAD models. We describe the rules for the translation from EXPRESS to OWL, and illustrate the benefits of OWL translation with an example. We will also describe how these mapping rules can be implemented through meta-model based transformations, which can be used to map other languages to OWL.     

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.     

Expressing inter-link constraints in OWL knowledge bases

Abstract: In this paper the Web Ontology Language (OWL) is examined to instantiate expert system knowledge bases intended for semantic Web applications. In particular, OWL is analyzed for expressing Unified Modeling Language (UML) representations that have been augmented with propositional logic asserted as inter‐link constraints. The motivation is ultimately to provide declarative propositional logic constraints that can be represented in UML and declaratively implemented using OWL and other constructs to realize semantic Web knowledge base repositories and databases to facilitate expert system applications. The results of this paper show that OWL is sufficient for capturing most inter‐link constraints asserted on generalization/specialization instances; however, OWL alone is inadequate for representing some inter‐link constraints asserted on associations. We propose enhancements to OWL via RDF extensions for the reification of associations into classes. These extensions mitigate all concerns that were identified in OWL as part of this study. The result is increased support of declarative constraint representations, which can be expressed in knowledge bases in the context of the semantic Web.     

Bridging the gap between OWL and relational databases

Despite similarities between the Web Ontology Language (OWL) and schema languages traditionally used in relational databases, systems based on these languages exhibit quite different behavior in practice. The schema statements in relational databases are usually interpreted as integrity constraints and are used to check whether the data is structured according to the schema. OWL allows for axioms that resemble integrity constraints; however, these axioms are interpreted under the standard first-order semantics and not as checks. This often leads to confusion and is inappropriate in certain data-centric applications. To explain the source of this confusion, in this paper we compare OWL and relational databases w.r.t. their schema languages and basic computational problems. Based on this comparison, we extend OWL with integrity constraints that capture the intuition behind similar statements in relational databases. We show that, if the integrity constraints are satisfied, they need not be considered while answering a broad range of positive queries. Finally, we discuss several algorithms for checking integrity constraint satisfaction, each of which is suitable to different types of OWL knowledge bases.     

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.     

SUPPORTING THE CAD STRUCTURAL DESIGN PROCESS WITH KNOWLEDGE-BASED TOOLS

We present in this article an architecture with its correspondent system implementation for the exploitation of semantic aspects in the computer assisted design of Steel Detailing Structures (Structural Design). Our architecture is based in a domain specific standardization effort called CIS/2 that was modeled from a knowledge point of view using an OWL (Ontology Web Language) compliant ontology.

As test case we present a collection of applications based in the proposed schema that were developed within the frame of an R&D project in which an actual structural engineering company participated as test user.     

基于关系数据库的本体生成器设计与实现

为了利用已有的关系数据库自动生成本体,分析关系数据库与OWL本体的形式化对应关系,设计一套转换规则,给出一种由关系数据库出发自动建立本体的新方法。在VC＋＋环境下实现一个基于关系数据库的本体生成器,可以将关系数据库转换为OWL本体,并有效缩短本体的构建时间。以一个具体实例对该本体生成器进行了验证。     

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).     

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.     

基于语义分析的OWL文档聚类研究

借助目前丰富的网络资源，将同一主题的现存Ontology知识聚类，提供给领域专家或用户进行二次精化和集成是Ontology研究领域的一个重要课题．OWL是目前用于表示和交换Ontology信息的基本标准．本文从OWL的语义本质出发，考虑了知识之间的继承性及复杂类比较和模糊集运算的相似性，提出一种计算OWL文档语义相似性的方式，并和层次聚类算法集成完成了对OWL文档集的聚类实验．实验结果说明本文提出的算法对自动生成和手工建立的OWL文档集都有很好的效果。     

Recommendation algorithm of the app store by using semantic relations between apps

In this paper, we propose a personalized recommendation system for mobile application software (app) to mobile user using semantic relations of apps consumed by users. To do that, we define semantic relations between apps consumed by a specific member and his/her social members using Ontology. Based on the relations, we identify the most similar social members from the reasoning process. The reasoning is explored from measuring the common attributes between apps consumed by the target member and his/her social members. The more attributes shared by them, the more similar is their preference for consuming apps. We also develop a prototype of our system using OWL (Ontology Web Language) by defining ontology-based semantic relations among 50 mobile apps. Using the prototype, we showed the feasibility of our algorithm that our recommendation algorithm can be practical in the real field and useful to analyze the preference of mobile user.