Yuting Zhao

Ontology-Driven Software Development

This book is about a significant step forward in software development. It brings state-of-the-art ontology reasoning into mainstream software development and its languages. Ontology Driven Software Development is the essential, comprehensive resource on enabling technologies, consistency checking and process guidance for ontology-driven software development (ODSD). It demonstrates how to apply ontology reasoning in the lifecycle of software development, using current and emerging standards and technologies. You will learn new methodologies and infrastructures, additionally illustrated using detailed industrial case studies. The book will help you: Learn how ontology reasoning allows validations of structure models and key tasks in behavior models. Understand how to develop ODSD guidance engines for important software development activities, such as requirement engineering, domain modeling and process refinement. Become familiar with semantic standards, such as the Web Ontology Language (OWL) and the SPARQL query language. Make use of ontology reasoning, querying and justification techniques to integrate software models and to offer guidance and traceability supports. This book is helpful for undergraduate students and professionals who are interested in studying how ontologies and related semantic reasoning can be applied to the software development process. In addition, itwill also be useful for postgraduate students, professionals and researchers who are going to embark on their research in areas related to ontology or software engineering.

Implementing and Evaluating a Rule-Based Approach to Querying Regular EL+ Ontologies

Recent years have witnessed the wide recognition of the importance of ontology and rule in the AI research. In this paper, we report our implementation and evaluation of a rule-based approach to querying regular EL+, a restriction of a well known description logics based ontology language EL+, by only allowing regular role axioms. It is known that, without such a restriction, query answering in EL+ in general is undividable. In our approach, a regular EL+ ontology is first translated into a logic program which contains a set of rules, and then by forward chaining reasoning the pseudo model of the above logic program is calculated. Query answering for EL+ is rewritten to instance checking in the pseudo model of a logic program. To the best of our knowledge, this is the first report of implementation and evaluation for regular EL+ ontologism.

Closed world reasoning for OWL2 with NBox

This paper describes the problem of doing description logic (DL) reasoning with partially closed world. The issue was addressed by extending the syntax of DL SROIQ with an NBox, which specifies the predicates to close, extending the semantics with the idea of negation as failure, reducing the closed world reasoning to incremental reasoning on classical DL ontologies, and applying the syntactic approximation technology to improve the reasoning performance. Compared with the existing DBox approach, which corresponds to the relation database, the NBox approach supports deduction on closed concepts and roles. Also, the approximate reasoning can reduce reasoning complexity from N2EXPTIME-complete to PTIME-complete while preserving the correctness of reasoning for ontologies with certain properties.

Tractable approximate deduction for OWL

Todays ontology applications require efficient and reliable description logic (DL) reasoning services. Expressive DLs usually have high worst case complexity while tractable DLs are restricted in terms of expressive power. This brings a new challenge: can users use expressive DLs to build their ontologies and still enjoy the efficient services as in tractable languages? Approximation has been considered as a solution to this challenge; however, traditional approximation approaches have limitations in terms of performance and usability. In this paper, we present a tractable approximate reasoning framework for OWL 2 that improves efficiency and guarantees soundness. Evaluation on ontologies from benchmarks and real-world use cases shows that our approach can do reasoning on complex ontologies efficiently with a high recall.

Ontology Learning from Incomplete Semantic Web Data by BelNet

Recent years have seen a dramatic growth of semantic web on the data level, but unfortunately not on the schema level, which contains mostly concept hierarchies. The shortage of schemas makes the semantic web data difficult to be used in many semantic web applications, so schemas learning from semantic web data becomes an increasingly pressing issue. In this paper we propose a novel schemas learning approach -BelNet, which combines description logics (DLs) with Bayesian networks. In this way BelNet is capable to understand and capture the semantics of the data on the one hand, and to handle incompleteness during the learning procedure on the other hand. The main contributions of this work are: (i)we introduce the architecture of BelNet, and corresponding lypropose the ontology learning techniques in it, (ii) we compare the experimental results of our approach with the state-of-the-art ontology learning approaches, and provide discussions from different aspects.

TBox learning from incomplete data by inference in BelNet

In this work we deal with the problem of TBox learning from incomplete semantic web data. TBox, or conceptual schema, is the backbone of a Description Logic (DL) ontology, but is always difficult to obtain. Existing approaches either fail in getting correct results under incompleteness or learn results that are not enough to resolve the incompleteness. We propose to transform TBox learning in DL into inference in the extension of Bayesian Description Logic Network (abbreviated as BelNet+), whereby the structure in the data is leveraged when evaluating the relationships between two concepts. BelNet+, integrating the probabilistic inference capability of Bayesian Networks with the logical formalism of DL ontologies - Description Logics, supports promising inference. In this paper, we firstly explain the details of BelNet+ and introduce a TBox learning approach based on BelNet+. In order to overcome the drawbacks of current evaluation metrics, we then propose a novel evaluation framework conforming to the Open World Assumption (OWA) generally made in the semantic web. Finally the results from empirical studies on comparisons with the state-of-the-art TBox learners verify the effectiveness of our approach.

Process Refinement Validation and Explanation with Ontology Reasoning

In process engineering, processes can be refined from simple ones to more and more complex ones with decomposition and restructuring of activities. The validation of these refinements and the explanation of invalid refinements are non-trivial tasks. This paper formally defines process refinement validation based on the execution set semantics and presents a suite of refinement reduction techniques and an ontological representation of process refinement to enable reasoning for the validation and explanation of process refinement. Results show that it significantly improves efficiency, quality and productivity of process engineering.

Belief Base Revision for Datalog+/- Ontologies

Datalog+/- is a family of emerging ontology languages that can be used for representing and reasoning over lightweight ontologies in Semantic Web. In this paper, we propose an approach to performing belief base revision for Datalog+/- ontologies. We define a kernel based belief revision operator for Datalog+/- and study its properties using extended postulates, as well as an algorithm to revise Datalog+/- ontologies. Finally, we give the complexity results by showing that query answering for a revised linear Datalog+/- ontology is tractable.

Ontology Reasoning for Consistency-Preserving Structural Modelling

In this chapter, we discuss and demonstrate concrete applications of ontology reasoning for the analysis and validation of structural models in the ODSD infrastructure. We illustrate how the ontology services (see Chaps. 3 and 5 for details) summarised in Chap. 8 are employed to enable consistency-preserving structural modelling by providing means for the specification of consistency constraints, static semantics, or the derivation of suggestions for modellers.

Semantic advertising for web 3.0

Advertising on the World Wide Web is based around automatically matching web pages with appropriate advertisements, in the form of banner ads, interactive adverts, or text links. Traditionally this has been done by manual classification of pages, or more recently using information retrieval techniques to find the most important keywords from the page, and match these to keywords being used by adverts. In this paper, we propose a new model for online advertising, based around lightweight embedded semantics. This will improve the relevancy of adverts on the World Wide Web and help to kick-start the use of RDFa as a mechanism for adding lightweight semantic attributes to the Web. Furthermore, we propose a system architecture for the proposed new model, based on our scalable ontology reasoning infrastructure TrOWL.