Yuan-Fang Li

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.

Measuring design complexity of semantic web ontologies

Ontology languages such as OWL are being widely used as the Semantic Web movement gains momentum. With the proliferation of the Semantic Web, more and more large-scale ontologies are being developed in real-world applications to represent and integrate knowledge and data. There is an increasing need for measuring the complexity of these ontologies in order for people to better understand, maintain, reuse and integrate them. In this paper, inspired by the concept of software metrics, we propose a suite of ontology metrics, at both the ontology-level and class-level, to measure the design complexity of ontologies. The proposed metrics are analytically evaluated against Weyukers criteria. We have also performed empirical analysis on public domain ontologies to show the characteristics and usefulness of the metrics. We point out possible applications of the proposed metrics to ontology quality control. We believe that the proposed metric suite is useful for managing ontology development projects.

Predicting reasoning performance using ontology metrics

A key issue in semantic reasoning is the computational complexity of inference tasks on expressive ontology languages such as OWL DL and OWL 2 DL. Theoretical works have established worst-case complexity results for reasoning tasks for these languages. However, hardness of reasoning about individual ontologies has not been adequately characterised. In this paper, we conduct a systematic study to tackle this problem using machine learning techniques, covering over 350 real-world ontologies and four state-of-the-art, widely-used OWL 2 reasoners. Our main contributions are two-fold. Firstly, we learn various classifiers that accurately predict classification time for an ontology based on its metric values. Secondly, we identify a number of metrics that can be used to effectively predict reasoning performance. Our prediction models have been shown to be highly effective, achieving an accuracy of over 80%.

Scalable Semantics The Silver Lining of Cloud Computing

Semantic inferencing and querying across large-scale RDF triple stores is notoriously slow. Our objective is to expedite this process by employing Googles MapReduce framework to implement scale-out distributed querying and reasoning. This approach requires RDF graphs to be decomposed into smaller units that are distributed across computational nodes. RDF Molecules appear to offer an ideal approach - providing an intermediate level of granularity between RDF graphs and triples. However, the original RDF molecule definition has inherent limitations that will adversely affect performance. In this paper, we propose a number of extensions to RDF molecules (hierarchy and ordering) to overcome these limitations. We then present some implementation details for our MapReduce-based RDF molecule store. Finally we evaluate the benefits of our approach in the context of the Bio-MANTA project - an application that requires integration and querying across large-scale protein-protein interaction datasets.

Verifying DAML+OIL and beyond in Z/EVES

Semantic Web, the next generation of Web, gives data well-defined and machine-understandable meaning so that they can be processed by remote intelligent agents cooperatively. Ontology languages are the building blocks of Semantic Web as they prescribe how data are defined and related. The existing reasoning and verification tools for Semantic Web are improving however still elementary. We believe that Semantic Web can be a novel application domain for software modeling languages and tools. Z is a formal modeling language for specifying software systems and Z/EVES is a proof tool for Z. In this paper, we firstly present Z semantics for ontology language DAML+OIL. This semantic model is embedded as a Z section daml2zin Z/EVES, which serves as an environment for checking and verifying Web ontologies. Then we present a tool for automatically transforming ontology documents into the specialized Z codes understood by Z/EVES. Finally, we use a recent real application, the military plan ontologies, to demonstrate the different reasoning tasks that Z/EVES can perform. Furthermore, undiscovered errors in the original ontologies were found by Z/EVES and some of these errors are even beyond Semantic Web modeling and reasoning capabilities.

A combined approach to checking web ontologies

The understanding of Semantic Web documents is built upon ontologies that define concepts and relationships of data. Hence, the correctness of ontologies is vital. Ontology reasoners such as RACER and FaCT have been developed to reason ontologies with a high degree of automation. However, complex ontology-related properties may not be expressible within the current web ontology languages, consequently they may not be checkable by RACER and FaCT. We propose to use the software engineering techniques and tools, i.e., Z/EVES and Alloy Analyzer, to complement the ontology tools for checking Semantic Web documents.In this approach, Z/EVES is first applied to remove trivial syntax and type errors of the ontologies. Next, RACER is used to identify any ontological inconsistencies, whose origins can be traced by Alloy Analyzer. Finally Z/EVES is used again to express complex ontology-related properties and reveal errors beyond the modeling capabilities of the current web ontology languages. We have successfully applied this approach to checking a set of military plan ontologies.

Two decades of Web application testing-A survey of recent advances

Since its inception of just over two decades ago, the World Wide Web has become a truly ubiquitous and transformative force in our life, with millions of Web applications serving billions of Web pages daily. Through a number of evolutions, Web applications have become interactive, dynamic and asynchronous. The Web?s ubiquity and our reliance on it have made it imperative to ensure the quality, security and correctness of Web applications. Testing is a widely used technique for validating Web applications. It is also a long-standing, active and diverse research area. In this paper, we present a broad survey of recent Web testing advances and discuss their goals, targets, techniques employed, inputs/outputs and stopping criteria. HighlightsA broad survey of high-impact Web application testing techniques.Summary of different aspects of each testing technique (purpose, evaluation methods, inputs/outputs, stopping conditions, etc.) in a number of tables.Discussion of pros and cons of each technique, useful for both researchers and developers.

An ontology-centric architecture for extensible scientific data management systems

Data management has become a critical challenge faced by a wide array of scientific disciplines in which the provision of sound data management is pivotal to the achievements and impact of research projects. Massive and rapidly expanding amounts of data combined with data models that evolve over time contribute to making data management an increasingly challenging task that warrants a new approach. In this paper we present an ontology-centric architecture for data management systems that is extensible and domain independent. In this architecture, the behaviors of domain concepts and objects are captured entirely by ontological entities, around which all data management tasks are carried out. The open and semantic nature of ontology languages also makes this architecture amenable to greater data reuse and interoperability. To evaluate the proposed architecture, we have applied it to the challenge of managing phenomics data. Highlights? A novel, extensible ontology-centric architecture for scientific data management. ? We present the formal definitions of ontology versioning and dynamic composition. ? We demonstrate the feasibility of the architecture through an implemented system.

Semantic Web Languages – Towards an Institutional Perspective

The Semantic Web (SW) is viewed as the next generation of the Web that enables intelligent software agents to process and aggregate data autonomously. Ontology languages provide basic vocabularies to semantically markup data on the SW. We have witnessed an increase of numbers of SW languages in the last years. These languages, such as RDF, RDF Schema (RDFS), the OWL suite of languages, the OWL− − suite, SWRL, are based on different semantics, such as the RDFS-based, description logic-based, Datalog-based semantics. The relationship among the various semantics poses a challenge for the SW community for making the languages interoperable. Institutions provide a means of reasoning about software specifications regardless of the logical system. This makes it an ideal candidate to represent and reason about the various languages in the Semantic Web. In this paper, we construct institutions for the SW languages and use institution morphisms to relate them. We show that RDF framework together with the RDF serializations of SW languages form an indexed institution. This allows the use of Grothendieck institutions to combine Web ontologies described in various languages.

Using semantic web technologies to build a community-driven knowledge curation platform for the skeletal dysplasia domain

In this paper we report on our on-going efforts in building SKELETOME - a community-driven knowledge curation platform for the skeletal dysplasia domain. SKELETOME introduces an ontologydriven knowledge engineering cycle that supports the continuous evolution of the domain knowledge. Newly submitted, undiagnosed patient cases undergo a collaborative diagnosis process that transforms them into well-structured case studies, classified, linked and discoverable based on their likely diagnosis(es). The paper presents the community requirements driving the design of the platform, the underlying implementation details and the results of a preliminary usability study. Because SKELETOME is built on Drupal 7, we discuss the limitations of some of its embedded Semantic Web components and describe a set of new modules, developed to handle these limitations (which will soon be released as open source to the community).