Search

Because metadata that underlies semantic web applications is gathered from distributed and heterogeneous data sources, it is important to ensure its quality (i.e., reduce duplicates, spelling errors, ambiguities). However, current infrastructures that acquire and integrate semantic data have only marginally addressed the issue of metadata quality. In this paper we present our metadata acquisition infrastructure, ASDI, which pays special attention to ensuring that high quality metadata is derived. Central to the architecture of ASDI is a verification engine that relies on several semantic web tools to check the quality of the derived data. We tested our prototype in the context of building a semantic web portal for our lab, KMi. An experimental evaluation comparing the automatically extracted data against manual annotations indicates that the verification engine enhances the quality of the extracted semantic metadata.

High quality domain ontologies are essential for successful employment of semantic Web services. However, their acquisition is difficult and costly, thus hampering the development of this field. In this paper we report on the first stage of research that aims to develop (semi-)automatic ontology learning tools in the context of Web services that can support domain experts in the ontology building task. The goal of this first stage was to get a better understanding of the problem at hand and to determine which techniques might be feasible to use. To this end, we developed a framework for (semi-)automatic ontology learning from textual sources attached to Web services. The framework exploits the fact that these sources are expressed in a specific sublanguage, making them amenable to automatic analysis. We implement two methods in this framework, which differ in the complexity of the employed linguistic analysis. We evaluate the methods in two different domains, verifying the quality of the extracted ontologies against high quality hand-built ontologies of these domains. Our evaluation lead to a set of valuable conclusions on which further work can be based. First, it appears that our method, while tailored for the Web services context, might be applicable across different domains. Second, we concluded that deeper linguistic analysis is likely to lead to better results. Finally, the evaluation metrics indicate that good results can be achieved using only relatively simple, off the shelf techniques. Indeed, the novelty of our work is not in the used natural language processing methods but rather in the way they are put together in a generic framework specialized for the context of Web services.

Successful employment of semantic Web services depends on the availability of high quality ontologies to describe the domains of these services. Building such ontologies is difficult and costly, thus hampering Web service deployment. As a solution, we describe an automatic extraction method that learns domain ontologies from textual documentations attached to Web services. We evaluate our method in two different domains, that of RDF ontology storage tools and that of bioinformatics services. The quality of the extracted ontologies is verified against high quality hand-built ontologies of the corresponding domains. We conclude that our method allows extracting a considerable amount of information for a domain ontology and that it is applicable across different domains.

Service interface description languages such as WSDL, and related standards, are evolving rapidly to provide a foundation for interoperation between Web services. At the same time, Semantic Web service technologies, such as the Ontology Web Language for Services (OWL-S), are developing the means by which services can be given richer semantic specifications. Richer semantics can enable fuller, more flexible automation of service provision and use, and support the construction of more powerful tools and methodologies. Both sets of technologies can benefit from complementary uses and cross-fertilization of ideas. This paper shows how to use OWL-S in conjunction with Web service standards, and explains and illustrates the value added by the semantics expressed in OWL-S.

Web service composition can provide a value-chain between customers and suppliers. The increasing number of services, and thus possible combinations, demands the development of dynamic and automatic techniques for their composition. Current commercial solutions are limited and are primarily static and manual. Automation requires reasoning about (semantic descriptions of) the services. This paper describes our initial work which brings together agents, Web service and semantic Web technology. Our knowledge-based software engineering approach to the design of agents, known as the Agent Factory, is applied to the composition of Web services. Using semantic descriptions of Web services written in DAML-S, the design process in our Agent Factory derives a Web service configuration. This paper also includes some observations regarding our experiences with DAML-S, UDDI and WSDL for this purpose.

Easy integration of available tools will be a key success factor for the future of the Semantic Web. We envision that semantic descriptions of the tools themselves will play an important part in addressing this issue. Motivated by this vision we used DAML-S (a key initiative to support automated management of Web services) to describe Sesame (an RDF(S) storage and query engine). This paper discusses the major problems that we encountered as well as suggested solutions. This work is relevant to other Semantic Web research groups who wish to annotate their tools or to use annotated tools. Also, we hope to offer helpful input to the DAML-S coalition in the further development of their standard.

The perspective role of UML as a conceptual modelling language for the Semantic Web has become an important research topic. We argue that UML could be a key technology for overcoming the ontology development bottleneck thanks to its wide acceptance and sophisticated tool support. Transformational approaches are a promising way of establishing a connection between UML and web-based ontology languages. We compare some proposals for defining transformations between UML and web ontology languages and discuss the different ways they handle the conceptual differences between these languages. We identify commonalities and differences of the approaches and point out open questions that have not or not satisfyingly been addressed by existing approaches.

Though DAML-S is growing into a de facto standard for semantic web- service markup, we have only found few complete service descriptions and even less papers discussing technical issues about the markup process. We addressed this lack by (1) reporting on our experiences in describing a set of services, (2) concluding several limitations of the latest DAML-S version (v0.7) and (3) making our work accessible to the research communit

Describing software entities using Semantic Web technology is a growing research area. Our work investigates the semantic description of software entities that provide an application programmer’s interface (API) to allow reasoning with the descriptions and their interrelationships. We present an ontology for our Application Server for the Semantic Web [3], where it is used to facilitate implementation tasks and semantic discovery. Building on an emerging standard from the Semantic Web community, our work includes a number of extensions to DAML-S [1] which currently allows semantic description of a particular type of software entities, viz. web-services, in order to facilitate their automatic discovery and integration.

Evaluations of semantic search systems are generally small scale and ad hoc due to the lack of appropriate resources such as test collections, agreed performance criteria and independent judgements of performance. By analysing our work in building and evaluating semantic tools over the last five years, we conclude that the growth of the semantic web led to an improvement in the available resources and the consequent robustness of performance assessments. We propose two directions for continuing evaluation work: the development of extensible evaluation benchmarks and the use of logging parameters for evaluating individual components of search systems.

Blockchain technology promises a sizable potential for executing inter-organizational business processes without requiring a central party serving as a single point of trust (and failure). This paper analyzes its impact on business process management (BPM). We structure the discussion using two BPM frameworks, namely the six BPM core capabilities and the BPM lifecycle. This paper provides research directions for investigating the application of blockchain technology to BPM., Comment: Preprint for ACM TMIS

A fundamental assumption of improvement in Business Process Management (BPM) is that redesigns deliver refined and improved versions of business processes. These improvements can be validated online through sequential experiment techniques like AB Testing, as we have shown in earlier work. Such approaches have the inherent risk of exposing customers to an inferior process version during the early stages of the test. This risk can be managed by offline techniques like simulation. However, offline techniques do not validate the improvements because there is no user interaction with the new versions. In this paper, we propose a middle ground through shadow testing, which avoids the downsides of simulation and direct execution. In this approach, a new version is deployed and executed alongside the current version, but in such a way that the new version is hidden from the customers and process workers. Copies of user requests are partially simulated and partially executed by the new version as if it were running in the production. We present an architecture, algorithm, and implementation of the approach, which isolates new versions from production, facilitates fair comparison, and manages the overhead of running shadow tests. We demonstrate the efficacy of our technique by evaluating the executions of synthetic and realistic process redesigns.

While many authors have argued for the benefits of applying principles of modularization to ontologies, there is not yet a common understanding of how modules are defined and what properties they should have. In the previous section, this question was addressed from a purely logical point of view. In this chapter, we take a broader view on possible criteria that can be used to determine the quality of a modules. Such criteria include logic-based, but also structural and application-dependent criteria, sometimes borrowing from related fields such as software engineering. We give an overview of possible criteria and identify a lack of application-dependent quality measures. We further report some modularization experiments and discuss the role of quality criteria and evaluation in the context of these experiments.

A fundamental assumption of Business Process Management (BPM) is that redesign delivers new and improved versions of business processes. This assumption, however, does not necessarily hold, and required compensatory action may be delayed until a new round in the BPM life-cycle completes. Current approaches to process redesign face this problem in one way or another, which makes rapid process improvement a central research problem of BPM today. In this paper, we address this problem by integrating concepts from process execution with ideas from DevOps. More specifically, we develop a technique called AB-BPM that offers AB testing for process versions with immediate feedback at runtime. We implemented this technique in such a way that two versions (A and B) are operational in parallel and any new process instance is routed to one of them. The routing decision is made at runtime on the basis of the achieved results for the registered performance metrics of each version. AB-BPM provides for ultimate convergence towards the best performing version, no matter if it is the old or the new version. We demonstrate the efficacy of our technique by conducting an extensive evaluation based on both synthetic and real-life data.