Methodologies to find and evaluate solutions for ontology matching should be centered on the practical problems to be solved. In this paper we look at matching from the perspective of a practitioner in search of matching techniques or tools. We survey actual matching use cases, and derive general categories from these. We then discuss the value of existing techniques for these categories.

With the increasing number of ontologies available on the web, the problem of merging ontologies from different sources to interoperate applications becomes important. This paper presents a novel approach for merging of light-weight ontologies based on answer set programming (ASP) and linguistic background knowledge. ASP provides a declarative execution environment for intuitive merging rules. WordNet provides broad linguistic knowledge that is used to identify corresponding concepts. We present a semi-automatic merging algorithm, where users can choose appropriate results from a set of suggestions.

The natural evolution of eGovernment is to go beyond the management of identities and therefore it is necessary to manage people, companies or organizations, and their capabilities to interact with Public Administrations. When developing an application based on an eID management system, this management issue must be tackled within each application (i.e. demonstrate the capability of one person to act, demonstrate the economical reliability, demonstrate his professional status, etc ...) and is normally based on the local jurisdiction. The objective of the present paper is to introduce a distributed system for the privacy-enhanced management of the capabilities associated to a person within the EU framework, independently from the origin and destination EU member state. The core of this system is the intelligence of the Capabilities Resolution Nodes (CRN) to cope with the complexity of the capability resolution and the capability sources discovery in the pan-European scenario. A European Capacity Resolution Network will be able to grow up the interoperability of the digital identities provided and valid in each member state and will answer the question “is this person, identified with this digital identity and who is described by those attributes, allowed to carry out this legal act in this country according to its law?”.

Ontology mapping is the key to data interoperability in the semantic web vision. Computing mappings is the first step to applications such as query rewriting, instance sharing, web-service integration, and ontology merging. This problem has received a lot of attention in recent years, but little is known about how users actually construct mappings. Several ontology-mapping tools have been developed, but which tools do users actually use? What processes are users following to discover, track, and compute mappings? How do teams coordinate when performing mappings? In this paper, we discuss the results from an online user survey where we gathered feedback from the community to help answer these important questions. We discuss the results from the survey and the implications they may have on the mapping research community.

Collaborative e-Science projects commonly require data analysis to be performed on distributed data sets which may contain sensitive information. In addition to the credential-based privacy protection, ensuring proper handling of computerized data for disclosure and analysis is particularly essential in e-Science. In this paper, we propose a semantic approach for enforcing it through workflow systems. We define privacy preservation and analysis-relevant terms as ontologies and incorporate them into a proposed policy framework to represent and enforce the policies. We believe that workflow systems with the proposed privacy-awareness incorporated could ease the scientists in setting up privacy polices that suit for different types of collaborative research projects and can help them in safeguarding the privacy of sensitive data throughout the data analysis lifecycle.

The most common matching applications, e.g., ontology matching, focus on the computation of the correspondences holding between the nodes of graph structures (e.g., concepts in two ontologies). However there are applications, such as matching of web service descriptions, where matching may need to compute the correspondences holding between the full graph structures and to preserve certain structural properties of the graphs being considered. The goal of this paper is to provide an implementation of a new matching operator, that we call structure preserving match. This operator takes two graph-like structures and produces a mapping between those nodes of the structures that correspond semantically to each other, (i) still preserving a set of structural properties of the graphs being matched, (ii) only in the case that the graphs globally correspond semantically to each other. We present an exact and an approximate structure matching algorithm. The latter is based on a formal theory of abstraction and builds upon the well known tree edit distance measures. We have implemented the algorithms and applied them to the web service matchmaking scenario. The evaluation results, though preliminary, show the efficiency and effectiveness of our approach.

A new paradigm in Semantic Web research focuses on the development of a new generation of knowledge-based problem solvers, which can exploit the massive amounts of formally specified information available on the Web, to produce novel intelligent functionalities. An important example of this paradigm can be found in the area of Ontology Matching, where new algorithms, which derive mappings from an exploration of multiple and heterogeneous online ontologies, have been proposed. While these algorithms exhibit very good performance, they rely on merely syntactical techniques to anchor the terms to be matched to those found on the Semantic Web. As a result, their precision can be affected by ambiguous words. In this paper, we aim to solve these problems by introducing techniques from Word Sense Disambiguation, which validate the mappings by exploring the semantics of the ontological terms involved in the matching process. Specifically we discuss how two techniques, which exploit the ontological context of the matched and anchor terms, and the information provided by WordNet, can be used to filter out mappings resulting from the incorrect anchoring of ambiguous terms. Our experiments show that each of the proposed disambiguation techniques, and even more their combination, can lead to an important increase in precision, without having too negative an impact on recall.

Trust can be applied to knowledge sharing on a distributed network of knowledge source agents. Each agent represents a person who trusts some other agents. Based on these trust-relationships, an agent can infer the trustworthiness of an unknown agent by asking trusted agents for recommendations. However, the person represented by an agent may not be willing to share his or her individual opinion about the trustwor-thiness of a particular agent to agents that do not protect information privacy. A solution for this issue is proposed using three kinds of privacy policies: generosity, caution, and non-cooperation. An agent that adopts the caution policy towards another agent will hide the details of the trust recommendation path. An analysis shows the effect of the privacy policies on the calculated reliabilities of the recommended trust values.

We show that the semantic formal model for Open Digital Right Language (ODRL)-based rights delegation policies can be enforced and expressed as a combination of ontologies and rules, e.g., Semantic Web Rule Language (SWRL). Based on ODRL's expressions and data dictionary, a rights delegation ontology is proposed in this study. Furthermore, we express the rights delegation policy as a set of ontology statements, rules, and facts for usage and transfer rights delegation. When verifying ODRL formal semantics, our SWRL approach is superior to the generic restricted First Order Logic (FOL) model because we have an understandable formal semantics of policies for automatic machine processing and a higher expressive power for policy compliance checking. On the other hand, the rights delegation semantics shown as a generic full FOL might have a higher complexity of license verification, which results in a policy compliance checking that is possibly undecidable. A real usage rights delegation scenario for digital content is demonstrated in order to justify the feasibility of our formal semantic model for digital rights delegation. We hope this study will shed some light on future sensitive information usage and delegation rights controlled from a privacy protection perspective.

Personal Information Management (PIM) systems aim to provide convenient access to all data and metadata on a desktop to the user itself as well as the co-workers. Obviously, sharing desktop data with co-workers raises privacy and access control issues which have to be addressed. In this paper we discuss these issues, and present appropriate solutions. In line with the architecture of current PIM systems, our solutions cover all semantic data shared in such a context, i.e. all desktop resources as well as other data structures created by the system, such as metadata in an RDF store and inverted index entries created for efficient textual search. We discuss different kinds of policies to specify protection for desktop data and metadata, and describe our access control system to express and execute these policies efficiently. Additionally, we describe the extension of an existing PIM system, Beagle++, with our approach, as well as our experiments, with convincing results on performance and scalability.

Many methods for automatic and semi-automatic ontology alignment have been proposed, but they remain error prone and labor-intensive. This paper describes a novel generic process for evaluating the mappings' confidence value. This process uses rules extracted through inductive machine learning methods from the matching results proposed by others. Further, the precision and recall of the extracted rules are exploited in order to transform each rule into a mathematical formula that generates the mappings' confidence value. Mappings are then classified not as valid or invalid but through a quantitative confidence value that can be easily managed during the alignment process.

While lots of research in ontology matching is related to the issue of computing and refining similarity measures, only little attention has been paid to question how to extract the final alignment from a matrix of similarity values. In this paper we present a theoretical framework for describing extraction methods and argue that the quality of the final matching result is highly affected by the extraction method. Therefore, we discuss several extraction methods and apply them to some of the results submitted to the OAEI 2006. The results of our experimental study show that the proposed strategies differ with respect to precision and recall. In particular, theoretical considerations as well as emprirical results indicate that methods that additionally make use of information encoded in the ontologies result in better extractions compared to state of the art approaches.

Business needs are nowadays frequently realized by business workflows spanning multiple organizations. Current infrastructures, such as SOA, support this trend on the implementation side. As a side effect these issues of privacy and data protection arise, because data is shipped across organizational boundaries. At the same time increased awareness about protection of privacy and IPR have lead to comprehensive contractual and legal constructs - including the information of services consumers about the ways their data is handled. We propose to solve such information requests in widely distributed workflow executions by gathering the related information during the execution and attaching it directly to the processed data. Together with the data this information is passed through the workflow and at the end it is returned to the service consumer.

A lot of alignment systems providing mappings between the concepts of two ontologies rely on an additional source, called background knowledge, represented most of the time by a third ontology. The objective is to complement others current matching techniques. In this paper, we present the difficulties encountered when using WordNet as background knowledge and we show how the TaxoMap system we implemented can avoid those difficulties.

We introduce in this paper correspondence patterns as a tool to design ontology alignments. Based on existing research on patterns in the fields of software and ontology engineering, we define a pattern template and use it to develop a correspondence patterns library. This library is published in RDF following a structured vocabulary. It is meant to be used in ontology alignment systems, in order to support the user or improve matching algorithms to refine ontology alignments.

In this paper we propose the "Classification-Based Learning of Subsumption Relations for the Alignment of Ontologies" (CSR) method. Given a pair of concepts from two ontologies, the objective of CSR is to identify patterns of concepts’ features (here, properties) that provide evidence for the subsumption relation among these concepts. This is achieved by means of a classification task using decision trees. For the learning of the decision trees, the proposed method generates training datasets from the source ontologies’, considering each ontology in isolation. The paper describes thoroughly the method, provides experimental results for computing subsumption relations over an extended version of the OAEI 2006 benchmarking series and discusses the potential of the method.

Ontology mapping tools typically employ combinations of methods, the mutual effects of which deserve study. We propose an approach to analysis of such combinations using synthetic ontologies. Initial experiments have been carried out for two string-based and one graphbased method. Most important target of the study was the impact of name patterns over taxonomy paths on the mapping results.

In this paper we discuss a book annotation translation application scenario that requires multi-concept alignment - where one set of concepts is aligned to another set. Using books annotated by concepts from two vocabularies which are to be aligned, we explore two statistically-grounded measures (Jaccard and LSA) to build conversion rules which aggregate similar concepts. Different ways of learning and deploying the multi-concept alignment are evaluated, which enables us to assess the usefulness of the approach for this scenario. This usefulness is low at the moment, but the experiment has given us the opportunity to learn some important lessons.

A behavioral study investigated how college students judge similarity between cell pictures. The study indicates that there is a strong tendency to rely on class-inclusion relations in judgments of similarity. This means that biological concepts are likely to be organized and conceptualized with respect to class-inclusion relations even for non-experts.