The Semantic Web Service Challenge is one of the major initiative dedicated to work on Semantic Web Service (SWS) discovery and selection. It represents an effective manner for evaluating the functionality of SWS technologies. In this paper, we provide a new SWS-Challenge scenario proposal with new interesting problems to be solved on the basis of an analysis of a real world shipment scenario in the logistic operators domain. In the discussion a number of aspects concerning the discovery and selection processes are emphasized. In particular, we focus on the problem of considering the heterogeneity between the provider and the requester perspectives, and on the differences between functional and non functional specifications both on the requester and provider side.
One of the arguments for choosing description logics as the basis for the Web Ontology Language is the ability to support the development of complex ontologies through logical reasoning. Recently, significant work has been done on developing advanced methods for debugging erroneous ontologies that go beyond the pure identification of inconsistencies. While the theory of these methods has been studied extensively little attention has been paid to the application of these Methods in practice. In this paper, we evaluate existing implementations of advanced methods for debugging description logic ontologies. We show that most existing systems suffer from serious problems with respect to scalability but surprisingly enough also with respect to the correctness of the results on certain test sets. We conclude that there is a need for further improvements of existing systems in particular with respect to bridging the existing gap between theory and practice of debugging ontologies.
We reuse here the framework, the setting, and the semantic modelling for the automated synthesis of the SWS Challenge Mediator presented in the companion paper (5), and show how to exchange the synthesis paradigm from a linear-time-logic proof based algorithm to a runtime, abductive synthesis imple- mented in Prolog, that responds directly to situational changes. Both solutions take advantage of a policy-oriented enterprise management approach.
The goal of the SWS Challenge is to explore the trade-offs of various existing technologies that aim at automation of mediation, choreography and discovery of Web Services. For that reason, the SWS Challenge defines a number of scenarios providing a standard set of problems, based on industrial specifications and requirements. In this paper, we present a model-driven approach for solving the integration problem described in the Purchase Order Mediation scenario of the SWS Challenge. The key feature of our approach is that service models are employed at different abstraction levels to develop end-to-end integration solutions from business requirements to software implementation. Model-driven techniques are used to abstract the integration problem and solution to a higher (platform-independent) level. This way, the problem and solution can be captured in a technology independent manner enabling more active participation of business domain experts.
In this paper we present how Semantic Web Service technology can be used to overcome process and data heterogeneity in a B2B integration scenario. While one partner uses standards like RosettaNet for product purchase and UNIFI ISO 20022 for electronic payments in its message exchange process and message definition, the other one operates on non-standard proprietary solution based on a combination of WSDL and XML Schema. For this scenario we show the benefits of semantic descriptions which are used within the integration process to enable rulebased data and process mediation of services. We illustrate this dynamic integration process on the WSMX – a middleware platform conforming to the principles of a Semantic Service Oriented Architecture.
In this paper we show how to apply a tableau-based software com-position technique to automatically generate the mediator’s service logic. Thisuses an LTL planning (or configuration) algorithm originally embedded in theABC and in the ETI platforms. The algorithm works on the basis of the existingjABC library of available services (SIB library) and of an enhanced descriptionof their semantics given in terms of a taxonomic classification of their behaviour(modules) and abstract interfaces/messages (types). 1 The SWS Challenge Mediator The ongoing Sematic Web Service Challenge [19] proposes a number of increasinglycomplexscenariosforworkflow-basedservicemediationandservicediscovery.Weusehere the technology presented in [10] to synthesise a process that realizes the commu-nication layer for the Challenge’s initial mediation scenario.Inthisscenario,acustomer(technically,aclient)initiatesaPurchaseOrderRequestspecifiedbyaspecialmessageformat(RosettaNetPIP3A4)andwaitsforacorrespond-ingPurchaseOrderConfirmationaccordingtothesameRosettaNetstandard.Thesellerhoweverdoesnotsupportthisstandard.Itsbackendsystemorserverawaitsanorderinaproprietary message format and provides appropriate Web Services to serve the requestin the proprietary format. As client and server here speak different languages, there is aneed for a mediation layer that adapts both the data formats and also the granularity.Of course we can easily define the concrete process within our jABC modellingframework, as we have shown in the past [11,6,7].To provide a more flexible solution framework, especially to accommodate laterdeclarative specification changes on the backend side or on the data flow, we synthe-size the whole mediator using the synthesis technology introduced in [10]. We proceedhere exactly along the lines already presented in that paper.In the following, we show in Sect. 2 how to use the SLTL synthesis methodology togeneratethemediatorworkflowbasedonaknowledgebasethatexpressesthesemantics
This paper describes how semantic bridges realized in terms of rulebased ontology mappings can be incorporated into BPEL processes. The approach is explained by applying it to a semantic system integration scenario in the eBusiness domain defined as the “purchase order mediation” scenario in the context of the Semantic Web Service Challenge. The presented approach relies strongly on the existing Web standards and is based on widely adopted open source software components.
Analysis of concept naming in OWL ontologies with set-theoretic semantics could serve as partial means for understanding their conceptual structure, detecting modelling errors and assessing their quality. We carried out experiments on three existing ontologies from public repositories, concerning the consistency of very simple name patterns—subclass name being a certain kind of parent class name extension, while considering thesaurus relationships. Several probable taxonomic errors were identified in this way.
Detecting quality problems in semantic metadata is crucial for ensuring a high quality semantic web. Current approaches are primarily focused on the algorithms used in semantic metadata generation rather than on the data themselves. They typically require the presence of a gold standard and are not suitable for assessing the quality of semantic metadata. This paper proposes a novel approach, which exploits a range of knowledge sources including both domain and background knowledge to support semantic metadata evaluation without the need of a gold standard. We have conducted a set of preliminary experiments, which show promising results.
As the sheer volume of new knowledge increases, there is a need to find effective ways to convey and correlate emerging knowledge in machine-readable form. The success of the Semantic Web hinges on the ability to formalize distributed knowledge in terms of a varied set of ontologies. We present Pan-Onto-Eval, a comprehensive approach to evaluating an ontology by considering its structure, semantics, and domain. We provide formal definitions of the individual metrics that constitute Pan-Onto-Eval, and synthesize them into an integrated metric. We illustrate its effectiveness by presenting an example based on multiple ontologies for a University.
Ontology matching exists to solve practical problems. Hence, methodologies to find and evaluate solutions for ontology matching should be centered on practical problems. In this paper we propose two statistically-founded evaluation techniques to assess ontology-matching performance that are based on the application of the alignment. Both are based on sampling. One examines the behavior of an alignment in use, the other examines the alignment itself. We show the assumptions underlying these techniques and describe their limitations.
Watson is a gateway to the Semantic Web: it collects, analyzes and gives access to ontologies and semantic data available online with the objective of supporting their dynamic exploitation by semantic applications. We report on the analysis of 25 500 ontologies and semantic documents collected by Watson, giving an account about the way semantic technologies are used to publish knowledge on the Web, about the characteristics of the published knowledge, and about the networked aspects of the Semantic Web. Our main conclusions are 1that the Semantic Web is characterized by a large number of small, lightweight ontologies and a small number of large-scale, heavyweight ontologies, and 2that important efforts still need to be spent on improving the published ontologies (coverage of different topic domains, connectedness of the semantic data, etc.) and the tools that produce and manipulate them.
As more and more ontologies are being published on the Semantic Web, selecting the most appropriate ontology will become an increasingly important subtask in Semantic Web applications. Here we present an approach towards ontology search in the context of OntoSelect, a dynamic web-based ontology library. In OntoSelect, ontologies can be searched by keyword or by document. In keyword-based search only the keyword(s) provided by the user will be used for the search. In document-based search the user can provide either a URL for a web document that represents a specific topic or the user simply provides a keyword as the topic which is then automatically linked to a corresponding Wikipedia page from which a linguistically/statistically derived set of most relevant keywords will be extracted and used for the search. In this paper we describe an experiment in evaluating the document-based ontology search strategy based on an evaluation data set that we constructed specifically for this task.
We describe an approach to create a synthetic workload for large scale extensional query answering experiments. The workload comprises multiple interrelated domain ontologies, data sources which commit to these ontologies, synthetic queries and map ontologies that specify a graph over the domain ontologies. Some of the important parameters of the system are the average number of classes and properties of the source ontology which are mapped with the terms of target ontology and the number of data sources per ontology. The ontology graph is described by various parameters like its diameter, number of ontologies and average out-degree of node ontology. These parameters give a significant degree of control over the graph topology. This graph of ontologies is the central component of our synthetic workload that effectively represents a
Recent developments towards knowledge-based applications in general and Semantic Web applications in particular are leading to an increased interest in ontologies and in dynamic methods for developing and maintaining them. As human language is a primary mode of knowledge transfer, ontology learning from relevant text collections has been among the most successful strategies in this work. Such methods mostly combine a certain level of linguistic analysis with statistical and/or machine learning approaches to find potentially interesting concepts and relations between them. Here, we discuss a formalization of this process (in the specific context of the OntoLT tool for ontology learning from text) in order to arrive at a better definition of this task, which we hope to be of use in a more principled comparison of different approaches. As ontology representation formalisms we will consider those that have a model-theoretic semantics, with OWL (and subsets of OWL) being appropriate candidates.
Collaborative ontology building requires both knowledge integration and knowledge reconciliation. Wiki@nt is an ontology building environment that supports collaborative ontology development. Wiki@nt is based on OSHOQP(D), an extension to SHOQ(D) with O (partial order on axioms) and P (localized axioms in package ) constructors. Wiki@nt supports integration and reconciliation of multiple independently developed, semantically heterogeneous, and very likely inconsistent ontology modules. A web browser based editor interface is provided, with features to support team work, version control, page locking, and navigation. Version: July 30, 2004
Using ontologies is the standard way to achieve interoperability of heterogeneous systems within the Semantic web. However, as the ontologies underlying two systems are not necessarily compatible, they may in turn need to be aligned. Similarity-based approaches to alignment seems to be both powerful and flexible enough to match the expressive power of languages like OWL. We present an alignment tool that follows the similarity-based paradigm, called OLA. OLA relies on a universal measure for comparing the entities of two ontologies that combines in a homogeneous way the entire amount of knowledge used in entity descriptions. The measure is computed by an iterative fixed-point-bound process producing subsequent approximations of the target solution. The alignments produce by OLA on the contest ontology pairs and the way they relate to the expected alignments is discussed and some preliminary conclusions about the relevance of the similarity-based approach as well as about the experimental settings of the contest are drawn.
Ontology tools play a key role in the development and maintenance of the Semantic Web. Hence, we need in one hand to objectively evaluate these tools, in order to analyse whether they can deal with actual and future requirements, and in the other hand to develop benchmark suites for performing these evaluations. In this paper, we describe the method we have followed to design and implement a benchmark suite for evaluating the performance of the WebODE ontology engineering workbench, along with the conclusions obtained after using this benchmark suite for evaluating WebODE.
Objective evaluation and comparison of knowledge-based tools has so far been mostly an elusive goal for researchers and developers. Objective experiments are difficult to perform and require substantial resources. The EON Ontology Alignment Contest attempts to overcome these problems in inviting tool developers to perform a series of experiments in ontology alignment and compare their results to the reference alignments produced by experiment authors. We used our PROMPT suite of tools in the experiment. We briefly describe PROMPT in the paper and present our results. Based on this experience, we share our thoughts on the experiment design, its positive and negative aspects, and talk about lessons learned and ideas for future such experiments and contests.
