The Semantic Web – ISWC 2005

We are familiar with the idea of e-Commerce – the electronic trading between consumers and suppliers. In recent years there has been a commensurate paradigm shift in the way that science is conducted. e-Science is science performed through distributed global collaborations between scientists and their resources enabled by electronic means, in order to solve scientific problems. No one scientific laboratory has the resources or tools, the raw data or derived understanding or the expertise to harness the knowledge available to a scientific community. Real progress depends on pooling know-how and results. It depends on collaboration and making connections between ideas, people, and data. It depends on finding and interpreting results and knowledge generated by scientific colleagues you do not know and who do not know you, to be analysed in ways they did not anticipate, to generate new hypotheses to be pooled in their turn. The importance of e-Science has been highlighted in the UK, for example, by an investment of over £240 million pounds over the past five years to specifically address the research and development issues that have to be tacked to develop a sustainable and effective e-Science e-Infrastructure.

The Semantic Web envisions a future where applications (computer programs) can make sense and therefore more productive use of all the information on the web by assigning common “meaning” to the millions of terms and phrases used in billions of documents. AI and knowledge representation must rise to the occasion and work with decentralized representations, imprecision and incompleteness. Standard web-based representations are an essential enabler and we have made good progress in their design. But we still rely on humans to assign semantics and here there is a big leap of faith: The World Wide Web has grown at startling rates because humans are prolific at producing enormous volumes of unstructured information, that is, information without explicit semantics; on the other hand navigating this mass of information has proven to be both possible and profitable to the point that there is a $6 B search advertising industry. It’s is not practical to expect the same will automatically happen for semantically enriched content. And yet we need semantics to better leverage the huge value on the web.

The Practical Web is about confronting this challenge. Its about realizing that we will need to automate the assignment of semantics to unstructured content to ultimately realize the vision of the Semantic Web. If well done the results will be synergistic with the motors of web expansion: user value and commercial value.

The growing inferencing and knowledge linking power of the Semantic Web will, we all hope, make the world a better place: enrich democratic discourse, support more rapid scientific discovery, enable new forms of personal communication and culture, and generally enhance critical analysis of information. However, with this greater inferencing power comes daunting social and public policy questions that must be faced as first class technical design challenges, not just as issues to be resolved in courts and legislatures. How will we maintain fundamental privacy values in the face of inferencing and searching power that can systematically uncover sensitive facts about us even has we try to keep such data secret? Today’s Web has enabled a departure from traditional editorial control and historically-trusted information sources. Will attention to provenance on the Semantic Web enable us to develop new mechanisms for assessing the reliability of information? What new challenges to already frayed intellectual property regimes will the Semantic Web bring? Finally, how will we assert and represent personal identity on the Semantic Web? At this early stage of the development of the Semantic Web, it’s hard enough to have problems in focus, much less solutions. However, we believe that transparent reasoning and accountability mechanisms will play a critical role in enabling systems and services built on the Semantic Web to be more responsive to social and policy needs.

Much data is published on the Web in XML format satisfying schemas, and to make the Semantic Web a reality, such data needs to be interpreted with respect to ontologies. Interpretation is achieved through a

semantic mapping

between the XML schema and the ontology. We present work on the heuristic construction of

complex

such semantic mappings, when given an initial set of simple correspondences from XML schema attributes to datatype properties in the ontology. To accomplish this, we first offer a mapping formalism to capture the semantics of XML schemas. Second, we present our heuristic mapping construction algorithm. Finally, we show through an empirical study that considerable effort can be saved when constructing complex mappings by using our prototype tool.

Ontologies and automated reasoning are the building blocks of the Semantic Web initiative. Derivation rules can be included in an ontology to define derived concepts based on base concepts. For example, rules allow to define the extension of a class or property based on a complex relation between the extensions of the same or other classes and properties. On the other hand, the inclusion of negative information both in the form of negation-as-failure and explicit negative information is also needed to enable various forms of reasoning. In this paper, we extend RDF graphs with weak and strong negation, as well as derivation rules. The

ERDF stable model semantics

of the extended framework (

Extended RDF

) is defined, extending RDF(S) semantics. A distinctive feature of our theory, which is based on partial logic, is that both truth and falsity extensions of properties and classes are considered, allowing for truth value gaps. Our framework supports both closed-world and open-world reasoning through the explicit representation of the particular closed-world assumptions and the ERDF ontological categories of total properties and total classes.

In this paper, we apply automatic tools to the verification of interaction protocols of Web services described in OWL-S. Specifically, we propose a modeling procedure that preserves the control flow and the data flow of OWL-S Process Models. The result of our work provides complete modeling and verification of OWL-S Process Models.

In many Web service composition problems, information may be needed from Web services during the composition process. Existing research on Web service composition (WSC) procedures has generally assumed that this information will not change. We describe two ways to take such WSC procedures and systematically modify them to deal with volatile information.

The

black-box

approach requires no knowledge of the WSC procedure’s internals: it places a wrapper around the WSC procedure to deal with volatile information. The

gray-box

approach requires partial information of those internals, in order to insert coding to perform certain bookkeeping operations.

We show theoretically that both approaches work correctly. We present experimental results showing that the WSC procedures produced by the gray-box approach can run much faster than the ones produced by the black-box approach.

Matching hierarchical structures, like taxonomies or web directories, is the premise for enabling interoperability among heterogenous data organizations. While the number of new matching solutions is increasing the evaluation issue is still open. This work addresses the problem of comparison for pairwise matching solutions. A methodology is proposed to overcome the issue of scalability. A large scale dataset is developed based on real world case study namely, the web directories of Google, Looksmart and Yahoo!. Finally, an empirical evaluation is performed which compares the most representative solutions for taxonomy matching. We argue that the proposed dataset can play a key role in supporting the empirical analysis for the research effort in the area of taxonomy matching.

Semantic consequence (entailment) in RDF is ususally computed using Pat Hayes Interpolation Lemma. In this paper, we reformulate this mechanism as a graph homomorphism known as projection in the conceptual graphs community.

Though most of the paper is devoted to a detailed proof of this result, we discuss the immediate benefits of this reformulation: it is now easy to translate results from different communities (

e.g.

conceptual graphs, constraint programming, ...) to obtain new polynomial cases for the NP-complete

RDF entailment

problem, as well as numerous algorithmic optimizations.

In this paper, a system, RitroveRAI, addressing the general problem of enriching a multimedia news stream with semantic metadata is presented. News metadata here are explicitly derived from transcribed sentences or implicitly expressed into a topical category automatically detected. The enrichment process is accomplished by searching the same news expressed by different agencies reachable over the Web. Metadata extraction from the alternative sources (i.e. Web pages) is similarly applied and finally integration of the sources (according to some heuristic of pertinence) is carried out. Performance evaluation of the current system prototype has been carried out on a large scale. It confirms the viability of the RitroveRAI approach for realistic (i.e. 24 hours) applications and continuous monitoring and metadata extraction from multimedia news data.

The semantic web presents the vision of a distributed, dynamically growing knowledge base founded on formal logic. Common users, however, seem to have problems even with the simplest Boolean expressions. As queries from web search engines show, the great majority of users simply do not use Boolean expressions. So how can we help users to query a web of logic that they do not seem to understand? We address this problem by presenting a natural language interface to semantic web querying. The interface allows formulating queries in Attempto Controlled English (ACE), a subset of natural English. Each ACE query is translated into a discourse representation structure – a variant of the language of first-order logic – that is then translated into an N3-based semantic web querying language using an ontology-based rewriting framework. As the validation shows, our approach offers great potential for bridging the gap between the logic-based semantic web and its real-world users, since it allows users to query the semantic web without having to learn an unfamiliar formal language. Furthermore, we found that users liked our approach and designed good queries resulting in a very good retrieval performance (100% precision and 90% recall).

Visiting museums is an increasingly popular pastime. Studies have shown that visitors can draw on their museum experience, long after their visit, to learn new things in practical situations. Rather than viewing a visit as a single learning event, we are interested in ways of extending the experience to allow visitors to access online resources tailored to their interests. Museums typically have extensive archives that can be made available online, the challenge is to match these resources to the visitor’s interests and present them in a manner that facilitates exploration and engages the visitor. We propose the use of knowledge level resource descriptions to identify relevant resources and create structured presentations. A system that embodies this approach, which is in use in a UK museum, is presented and the applicability of the approach to the broader semantic web is discussed.

Decentralized case-based reasoning (DzCBR) is a reasoning framework that addresses the problem of adaptive reasoning in a multi-ontology environment. It is a case-based reasoning (CBR) approach which relies on contextualized ontologies in the C-OWL formalism for the representation of domain knowledge and adaptation knowledge. A context in C-OWL is used to represent a particular viewpoint, containing the knowledge needed to solve a particular local problem. Semantic relations between contexts and the associated reasoning mechanisms allow the CBR process in a particular viewpoint to reuse and share information about the problem and the already found solutions in the other viewpoints.

Swoogle helps software agents and knowledge engineers find Semantic Web knowledge encoded in RDF and OWL documents on the Web. Navigating such a Semantic Web on the Web is difficult due to the paucity of explicit

hyperlinks

beyond the namespaces in URIrefs and the few inter-document links like rdfs:seeAlso and owl:imports. In order to solve this issue, this paper proposes a novel Semantic Web navigation model providing additional navigation paths through Swoogle’s search services such as the

Ontology Dictionary

. Using this model, we have developed algorithms for ranking the importance of Semantic Web objects at three levels of granularity: documents, terms and RDF graphs. Experiments show that Swoogle outperforms conventional web search engine and other ontology libraries in finding more ontologies, ranking their importance, and thus promoting the use and emergence of consensus ontologies.

In this paper we describe how we handle heterogeneity in web service interaction through a choreography mechanism that we have developed for IRS-III. IRS-III is a framework and platform for developing semantic web services which utilizes the WSMO ontology. The overall design of our choreography framework is based on: the use of ontologies and state, IRS-III playing the role of a broker, differentiating between communication direction and which actor has the initiative, having representations which can be executed, a formal semantics, and the ability to suspend communication. Our framework has a full implementation which we illustrate through an example application.

Ontology alignment is a prerequisite in order to allow for interoperation between different ontologies and many alignment strategies have been proposed to facilitate the alignment task by (semi-)automatic means. Due to the complexity of the alignment task, manually defined methods for (semi-)automatic alignment rarely constitute an optimal configuration of substrategies from which they have been built. In fact, scrutinizing current ontology alignment methods, one may recognize that most are not optimized for given ontologies. Some few include machine learning for automating the task, but their optimization by machine learning means is mostly restricted to the extensional definition of ontology concepts. With APFEL (Alignment Process Feature Estimation and Learning) we present a machine learning approach that explores the user validation of initial alignments for optimizing alignment methods. The methods are based on extensional and intensional ontology definitions. Core to APFEL is the idea of a generic alignment process, the steps of which may be represented explicitly. APFEL then generates new hypotheses for what might be useful features and similarity assessments and weights them by machine learning approaches. APFEL compares favorably in our experiments to competing approaches.

The paper reports on the development of the formal framework to design strategies for multi-issue non-symmetric meaning negotiations among software agents in a distributed information retrieval system. The advancements of the framework are the following. A resulting strategy compares the contexts of two background domain theories not concept by concept, but the whole context to the other context by accounting the relationships among concepts, the properties, the constraints over properties, and the available instances. It contains the mechanisms for measuring contextual similarity through assessing propositional substitutions and to provide argumentation through generating extra contexts. It uses presuppositions for choosing the best similarity hypotheses and to make the mutual concession to the common sense monotonic. It provides the means to evaluate the possible eagerness to concede through semantic commitments and related notions of knowledgeability and degree of reputation.

It is generally acknowledged that any Knowledge Base (KB) should be able to adapt itself to new information received. This problem has been extensively studied in the field of belief change, the dominating approach being the AGM theory. This theory set the standard for determining the rationality of a given belief change mechanism but was placed in a certain context which makes it inapplicable to logics used in the Semantic Web, such as Description Logics (DLs) and OWL. We believe the Semantic Web community would benefit from the application of the AGM theory to such logics. This paper is a preliminary study towards the feasibility of this application. Our approach raises interesting theoretical challenges and has an important practical impact too, given the central role that DLs and OWL play in the Semantic Web.

The effective debugging of ontologies is an important prerequisite for their successful application and impact on the semantic web. The heart of this debugging process is the diagnosis of faulty knowledge bases. In this paper we define general concepts for the diagnosis of ontologies. Based on these concepts, we provide correct and complete algorithms for the computation of minimal diagnoses of knowledge bases. These concepts and algorithms are broadly applicable since they are independent of a particular variant of an underlying logic (with monotonic semantics) and independent of a particular reasoning system. The practical feasibility of our method is shown by extensive test evaluations.

We introduce an experience in building a public semantic web server maintaining annotations about the actors of a Telecom Valley. We then focus on an example of inference used in building one type of cartography of the competences of the economic actors of the Telecom Valley. We detailed how this inference exploits the graph model of the semantic web using ontology-based metrics and conceptual clustering. We prove the characteristics of theses metrics and inferences and we give the associated interpretations.

The paper presents a framework for introducing design patterns that facilitate or improve the techniques used during ontology lifecycle. Some distinctions are drawn between kinds of ontology design patterns. Some content-oriented patterns are presented in order to illustrate their utility at different degrees of abstraction, and how they can be specialized or composed. The proposed framework and the initial set of patterns are designed in order to function as a pipeline connecting domain modelling, user requirements, and ontology-driven tasks/queries to be executed.

Ontology tools performance and scalability are critical to both the growth of the Semantic Web and the establishment of these tools in the industry. In this paper, we present briefly the benchmarking methodology used to improve the performance and the scalability of ontology development tools. We focus on the definition of the infrastructure for evaluating the performance of these tools’ ontology management APIs in terms of its execution efficiency. We also present the results of applying the methodology for evaluating the API of the WebODE ontology engineering workbench.

Recommender algorithms have been quite successfully employed in a variety of scenarios from filtering applications to recommendations of movies and books at Amazon.com. However, all these algorithms focus on single item recommendations and do not consider any more complex recommendation structures. This paper explores how semantically rich complex recommendation structures, represented as RDF graphs, can be exchanged and shared in a distributed social network. After presenting a motivating scenario we define several annotation ontologies we use in order to describe context information on the user’s desktop and show how our ranking algorithm can exploit this information. We discuss how social distributed networks and interest groups are specified using extended FOAF vocabulary, and how members of these interest groups share semantically rich recommendations in such a network. These recommendations transport shared context as well as ranking information, described in annotation ontologies. We propose an algorithm to compute these rankings which exploits available context information and show how rankings are influenced by the context received from other users as well as by the reputation of the members of the social network with whom the context is exchanged.

In this paper a method for Partial RDF Encryption (PRE) is proposed in which sensitive data in an RDF-graph is encrypted for a set of recipients while all non-sensitive data remain publicly readable. The result is an RDF-compliant self-describing graph containing encrypted data, encryption metadata, and plaintext data. For the representation of encrypted data and encryption metadata, the XML-Encryption and XML-Signature recommendations are used. The proposed method allows for fine-grained encryption of arbitrary subjects, predicates, objects and subgraphs of an RDF-graph. An XML vocabulary for specifying encryption policies is introduced.

To date on-line processes (

i.e.

workflows) built in e-Science have been the result of collaborative team efforts. As more of these workflows are built, scientists start sharing and reusing stand-alone compositions of services, or

workflow fragments

. They

repurpose

an existing workflow or workflow fragment by finding one that is close enough to be the basis of a new workflow for a different purpose, and making small changes to it. Such a “workflow by example” approach complements the popular view in the Semantic Web Services literature that on-line processes are constructed automatically from scratch, and could help bootstrap the Web of Science. Based on a comparison of e-Science middleware projects, this paper identifies seven bottlenecks to scalable reuse and repurposing. We include some thoughts on the applicability of using OWL for two bottlenecks: workflow fragment discovery and the ranking of fragments.

In this paper, we investigate the (in)dependence among OWL documents with respect to the logical consequence when they are combined, in particular the inference of concept and role assertions about individuals. On the one hand, we present a systematic approach to identifying those documents that affect the inference of a given fact. On the other hand, we consider ways for fast detection of independence. First, we demonstrate several special cases in which two documents are independent of each other. Secondly, we introduce an algorithm for checking the independence in the general case. In addition, we describe two applications in which the above results have allowed us to develop novel approaches to overcome some difficulties in reasoning with large scale OWL data. Both applications demonstrate the usefulness of this work for improving the scalability of a practical Semantic Web system that relies on the reasoning about individuals.

One of the major problems of large scale, distributed and evolving ontologies is the potential introduction of inconsistencies. In this paper we survey four different approaches to handling inconsistency in DL-based ontologies: consistent ontology evolution, repairing inconsistencies, reasoning in the presence of inconsistencies and multi-version reasoning. We present a common formal basis for all of them, and use this common basis to compare these approaches. We discuss the different requirements for each of these methods, the conditions under which each of them is applicable, the knowledge requirements of the various methods, and the different usage scenarios to which they would apply.

We introduce a framework, based on logic programming, for preferential reasoning with agents on the Semantic Web. Initially, we encode the knowledge of an agent as a logic program equipped with call literals. Such call literals enable the agent to pose yes/no queries to arbitrary knowledge sources on the Semantic Web, without conditions on, e.g., the representation language of those sources. As conflicts may arise from reasoning with different knowledge sources, we use the extended answer set semantics, which can provide different strategies for solving those conflicts. Allowing, in addition, for an agent to express its preference for the satisfaction of certain rules over others, we can then induce a preference order on those strategies. However, since it is natural for an agent to believe its own knowledge (encoded in the program) but consider some sources more reliable than others, it can alternatively express preferences on call literals. Finally, we show how an agent can learn preferences on call literals if it is part of a web of trusted agents.

We propose a new technique for approximate ABox reasoning with OWL DL ontologies. Essentially, we obtain substantially improved reasoning performance by disregarding non-Horn features of OWL DL. Our approach comes as a side-product of recent research results concerning a new transformation of OWL DL ontologies into negation-free disjunctive datalog [1, 2, 3, 4], and rests on the idea of performing standard resolution over disjunctive rules by treating them as if they were non-disjunctive ones. We analyse our reasoning approach by means of non-monotonic reasoning techniques, and present an implementation, called

Screech

.

In this paper we propose a framework for reasoning with multi-version ontology, in which a temporal logic is developed to serve as its semantic foundation. We show that the temporal logic approach can provide a solid semantic foundation which can support various requirements on multi-version ontology reasoning. We have implemented the prototype of MORE (Multi-version Ontology REasoner), which is based on the proposed framework. We have tested MORE with several realistic ontologies. In this paper, we also discuss the implementation issues and report the experiments with MORE.

The Semantic Web Initiative envisions a Web wherein information is offered free of presentation, allowing more effective exchange and mixing across web sites and across web pages. But without substantial Semantic Web content, few tools will be written to consume it; without many such tools, there is little appeal to publish Semantic Web content.

To break this chicken-and-egg problem, thus enabling more flexible informa-tion access, we have created a web browser extension called Piggy Bankthat lets users make use of Semantic Web content within Web content as users browse the Web. Wherever Semantic Web content is not available, Piggy Bank can invoke screenscrapers to re-structure information within web pages into Semantic Web format. Through the use of Semantic Web technologies, Piggy Bank provides direct, immediate benefits to users in their use of the existing Web. Thus, the ex-istence of even just a few Semantic Web-enabled sites or a few scrapers already benefits users. Piggy Bank thereby offers an easy, incremental upgrade path to users without requiring a wholesale adoption of the Semantic Web’s vision.

To further improve this Semantic Web experience, we have created Semantic Bank, a web server application that lets Piggy Bank users share the Semantic Web information they have collected, enabling collaborative efforts to build so-phisticated Semantic Web information repositories through simple, everyday’s use of Piggy Bank.

Discovery of semantic associations in Semantic Web ontologies is an important task in various analytical activities. Several query languages and storage systems have been designed and implemented for storage and retrieval of information in RDF ontologies. However, they are inadequate for semantic association discovery. In this paper we present the design and implementation of BRAHMS, an efficient RDF storage system, specifically designed to support fast semantic association discovery in large RDF bases. We present memory usage and timing results of several tests performed with BRAHMS and compare them to similar tests performed using Jena, Sesame, and Redland, three of the well-known RDF storage systems. Our results show that BRAHMS handles basic association discovery well, while the RDF query languages and even the low-level APIs in the other three tested systems are not suitable for the implementation of semantic association discovery algorithms.

The Intelligence Community, among others, is increasingly using document metadata to improve document search and discovery on intranets and extranets. Document markup is still often incomplete, inconsistent, incorrect, and limited to keywords via HTML and XML tags. OWL promises to bring semantics to this markup to improve its machine understandability. A usable markup tool is becoming a barrier to the more widespread use of OWL markup in operational settings. This paper describes some of our attempts at building markup tools, lessons learned, and our latest markup tool, the Semantic Markup Tool (SMT). SMT uses automatic text extractors and templates to hide ontological complexity from end users and helps them quickly specify events and relationships of interest in the document. SMT automatically generates correct and consistent OWL markup. This comes at a cost to expressivity. We are evaluating SMT on several pilot semantic web efforts.

Recently, there have been a number of proposals for languages for expressing web service constraints and capabilities, with WS-Policy and WSPL leading the way. The proposed languages, although relatively inexpressive, suffer from a lack of formal semantics. In this paper, we provide a mapping of WS-Policy to the description logic fragment species of the Web Ontology Language (OWL-DL), and describe how standard OWL-DL reasoners can be used to check policy conformance and perform an array of policy analysis tasks. OWL-DL is much more expressive than WS-Policy and thus provides a framework for exploring richer policy languages.

One of the main goals of the semantic web services effort is to enable automated composition of web services. An end-to-end view of the service composition process involves automation of composite service creation, development of executable workflows and deployment on an execution environment. However, the main focus in literature has been on the initial part of formally representing web service capabilities and reasoning about their composition using AI techniques. Based upon our experience in building an end-to-end composition tool for application integration, we bring out issues that have an impact on information modeling aspects of the composition process. In this paper, we present approaches for solving problems relating to scalability and manageability of service descriptions and data flow construction for operationalizing the composed services.

Often the challenge of finding relevant information is reduced to find the ‘right’ people who will answer our question. In this paper we present innovative algorithms called INGA (Interest-based Node Grouping Algorithms) which integrate personal routing indices into semantic query processing to boost performance. Similar to social networks peers in INGA cooperate to efficiently route queries for documents along adaptive shortcut-based overlays using only local, but semantically well chosen information. We propose active and passive shortcut creation strategies for index building and a novel algorithm to select the most promising content providers depending on each peer index with respect to the individual query. We quantify the benefit of our indexing strategy by extensive performance experiments in the SWAP simulation infrastructure. While obtaining high recall values compared to other state-of-the-art algorithms, we show that INGA improves recall and reduces the number of messages significantly.

We propose a declarative update language for RDF graphs which is based on the paradigms of query and view languages RQL and RVL. Our language, called RUL, ensures that the execution of the update primitives on nodes and arcs neither violates the semantics of the RDF model nor the semantics of the given RDFS schema. In addition, RUL supports fine-grained updates at the class and property instance level, set-oriented updates with a deterministic semantics and takes benefit of the full expressive power of RQL for restricting the range of variables to nodes and arcs of RDF graphs.

In our work we extend the traditional bipartite model of ontologies with the social dimension, leading to a tripartite model of actors, concepts and instances. We demonstrate the application of this representation by showing how community-based semantics emerges from this model through a process of graph transformation. We illustrate ontology emergence by two case studies, an analysis of a large scale folksonomy system and a novel method for the extraction of community-based ontologies from Web pages.

Most existing ontology mapping tools are inexact. Inexact ontology mapping rules, if not rectified, result in imprecision in the applications that use them. We describe a framework to probabilistically improve existing ontology mappings using a Bayesian Network.

Omen

, an Ontology Mapping ENhancer, is based on a set of meta-rules that captures the influence of the ontology structure and the existing matches to match nodes that are neighbours to matched nodes in the two ontologies. We have implemented a protype ontology matcher that can either map concepts across two input ontologies or enhance existing matches between ontology concepts. Preliminary experiments demonstrate that

Omen

enhances existing ontology mappings in our test cases.

A common practice in conceptual modeling is to separate the intensional from the extensional model. Although very intuitive, this approach is inadequate for many complex domains, where the borderline between the two models is not clear-cut. Therefore, OWL-Full, the most expressive of the Semantic Web ontology languages, allows combining the intensional and the extensional model by a feature we refer to as

metamodeling

. In this paper, we show that the semantics of metamodeling adopted in OWL-Full leads to undecidability of basic inference problems, due to free mixing of logical and metalogical symbols. Based on this result, we propose two alternative semantics for metamodeling: the

contextual

and the

HiLog

semantics. We show that

$\mathcal{SHOIQ}$

— a description logic underlying OWL-DL — extended with metamodeling under either semantics is decidable. Finally, we show how the latter semantics can be used in practice to axiomatize the logical interaction between concepts and metaconcepts.

This paper presents our ongoing effort on developing a principled methodology for automatic ontology mapping based on

BayesOWL

, a probabilistic framework we developed for modeling uncertainty in semantic web. In this approach, the source and target ontologies are first translated into Bayesian networks (BN); the concept mapping between the two ontologies are treated as evidential reasoning between the two translated BNs. Probabilities needed for constructing conditional probability tables (CPT) during translation and for measuring semantic similarity during mapping are learned using text classification techniques where each concept in an ontology is associated with a set of semantically relevant text documents, which are obtained by ontology guided web mining. The basic ideas of this approach are validated by positive results from computer experiments on two small real-world ontologies.

In this article, we propose a new ontology evolution approach that combines a top-down and a bottom-up approach. This means that the manual request for changes (top-down) by the ontology engineer is complemented with an automatic change detection mechanism (bottom-up). The approach is based on keeping track of the different versions of ontology concepts throughout their lifetime (called virtual versions). In this way, changes can be defined in terms of these virtual versions.

Domain ontologies very rarely model verbs as relations holding between concepts. However, the role of the verb as a central connecting element between concepts is undeniable. Verbs specify the interaction between the participants of some action or event by expressing relations between them. In parallel, it can be argued from an ontology engineering point of view that verbs express a relation between two classes that specify domain and range. The work described here is concerned with relation extraction for ontology extension along these lines. We describe a system (RelExt) that is capable of automatically identifying highly relevant triples (pairs of concepts connected by a relation) over concepts from an existing ontology. RelExt works by extracting relevant verbs and their grammatical arguments (i.e. terms) from a domain-specific text collection and computing corresponding relations through a combination of linguistic and statistical processing. The paper includes a detailed description of the system architecture and evaluation results on a constructed benchmark. RelExt has been developed in the context of the SmartWeb project, which aims at providing intelligent information services via mobile broadband devices on the FIFA World Cup that will be hosted in Germany in 2006. Such services include location based navigational information as well as question answering in the football domain.

Semantic query optimization (SQO) has been proved to be quite useful in various applications (e.g., data integration, graphical query generators, caching, etc.) and has been extensively studied for relational, deductive, object, and XML databases. However, less attention to SQO has been devoted in the context of the Semantic Web. In this paper, we present sound and complete algorithms for the containment and minimization of RDF/S query patterns. More precisely, we consider two widely used RDF/S query fragments supporting pattern matching at the data, but also, at the schema level. To this end, we advocate a logic framework for capturing the RDF/S data model and semantics and we employ well-established techniques proposed in the relational context, in particular, the Chase and Backchase algorithms.

Ontologies are today a key part of every knowledge based system. They provide a source of shared and precisely defined terms, resulting in system interoperability by knowledge sharing and reuse. Unfortunately, the variety of ways that a domain can be conceptualized results in the creation of different ontologies with contradicting or overlapping parts. For this reason ontologies need to be brought into mutual agreement (aligned). One important method for ontology alignment is the comparison of class and property names of ontologies using string-distance metrics. Today quite a lot of such metrics exist in literature. But all of them have been initially developed for different applications and fields, resulting in poor performance when applied in this new domain. In the current paper we present a new string metric for the comparison of names which performs better on the process of ontology alignment as well as to many other field matching problems.

Coordination is the process of managing the possible interactions between activities and processes; a mechanism to handle such interactions is known as a coordination regime. A successful coordination regime will prevent negative interactions occurring (e.g., by preventing two processes from simultaneously accessing a non-shareable resource), and wherever possible will facilitate positive interactions (e.g., by ensuring that activities are not needlessly duplicated). We start from the premise that effective coordination mechanisms require the sharing of knowledge about activities, resources and their properties, and hence, that in a heterogeneous environment, an ontological approach to coordination is appropriate. After surveying recent work on dynamic coordination, we describe an ontology for coordination that we have developed with the goal of coordinating semantic web processes. We then present a implementation of our ideas, which serves as a proof of concept for how this ontology can be used for dynamic coordination. We conclude with a summary of the presented work, illustrate its relation to the Semantic Web, and provide insights into future extensions.

This paper presents

serse

– SEmantic Routing SystEm– a distributed multi-agent system composed of specialised agents that provides robust and efficient gathering and aggregation of digital content from diverse resources. The agents composing

serse

use ontological descriptions to search and retrieve semantically annotated knowledge sources, by maintaining a

semantic index

of the instances of the annotation ontology. The efficient retrieval is made it possible through the semantic routing mechanism, that permits to identify the agent indexing the resources requested by a user query without having to maintain a central index, and by reducing the number of messages broadcasted to the system. The system is also capable of exhibiting autonomic behaviour. Autonomic behaviour is characterised by self configuration and self healing capabilities, aimed at permitting the system to manage the failure of one of its agents and ensure continuous functioning.

This paper extends the model theory of RDF with rules, placing an emphasis on integration with OWL and decidability of entailment. We start from an abstract syntax that views a rule as a pair of rule graphs which generalize RDF graphs by also allowing rule variables in subject, predicate and object positions. We include RDFS as well as a decidable part of OWL that weakens

D

-entailment and OWL Full. Classes can be used as instances. Almost all examples in the DAML set of test rules are covered by our approach.

For a set of rules

R

, we define a general notion of

R

-entailment. Extending earlier results on RDFS and OWL, we prove a general completeness result for

R

-entailment. This result shows that a restricted form of application of rules that introduce blank nodes is sufficient to determine

R

-entailment. For rules that do not introduce blank nodes, we prove that

R

-entailment and

R

-consistency are decidable and in PSPACE. For rules that do not introduce blank nodes and that satisfy a bound on the size of rule bodies, we prove that

R

-consistency is in P, that

R

-entailment is in NP, and that

R

-entailment is in P if the target RDF graph is ground.

In this paper we benchmark three popular database representations of RDF/S schemata and data: (a) a schema-aware (i.e., one table per RDF/S class or property) with explicit (

ISA

) or implicit (

NOISA

) storage of subsumption relationships, (b) a schema-oblivious (i.e., a single table with triples of the form 〈subject-predicate-object〉), using (

ID

) or not (

URI

) identifiers to represent resources and (c) a hybrid of the schema-aware and schema-oblivious representations (i.e., one table per RDF/S meta-class by distinguishing also the range type of properties). Furthermore, we benchmark two common approaches for evaluating taxonomic queries either on-the-fly (

ISA

,

NOISA

,

Hybrid

), or by precomputing the transitive closure of subsumption relationships (

MatView

,

URI

,

ID

). The main conclusion drawn from our experiments is that the evaluation of taxonomic queries is most efficient over RDF/S stores utilizing the

Hybrid

and

MatView

representations. Of the rest, schema-aware representations (

ISA

,

NOISA

) exhibit overall better performance than

URI

, which is superior to that of

ID

, which exhibits the overall worst performance.

In many practical applications, ontologies tend to be very large and complicated. In order for users to quickly understand and analyze large-scale ontologies, in this paper we propose a novel ontology visualization approach, which aims to complement existing approaches like the hierarchy graph. Specifically, our approach produces a holistic “imaging” of the ontology which contains a semantic layout of the ontology classes. In addition, the distributions of the ontology instances and instance relations are also depicted in the “imaging”. We introduce at length the key techniques and algorithms used in our approach. Then we examine the resulting user interface and find it facilitates tasks like ontology navigation, ontology retrieval and ontology instance analysis.

OntoClean is a unique approach towards the formal evaluation of ontologies, as it analyses the intensional content of concepts. Although it is well documented in numerous publications, and its importance is widely acknowledged, it is still used rather infrequently due to the high costs for applying OntoClean, especially on tagging concepts with the correct meta-properties. In order to facilitate the use of OntoClean and to enable proper evaluation of it in real-world cases, we provide AEON , a tool which automatically tags concepts with appropriate OntoClean meta-properties. The implementation can be easily expanded to check the concepts for other abstract meta-properties, thus providing for the first time tool support in order to enable intensional ontology evaluation for concepts. Our main idea is using the web as an embodiment of objective world knowledge, where we search for patterns indicating concepts meta-properties. We get an automatic tagging of the ontology, thus reducing costs tremendously. Moreover, AEON lowers the risk of having subjective taggings. As part of the evaluation we report our experiences from creating a middle-sized OntoClean-tagged reference ontology.

We discuss four linguistic ontology-mapping techniques and evaluate them on real-life ontologies in the domain of food. Furthermore we propose a method to combine ontology-mapping techniques with high Precision and Recall to reduce the necessary amount of manual labor and computation.

After becoming a W3C Recommendation, OWL is becoming increasingly widely accepted and used. However most people still find it difficult to create and use OWL ontologies. On major difficulty is “debugging” the ontologies – discovering why a reasoners has inferred that a class is “unsatisfiable” (inconsistent). Even for people who do understand OWL and the logical meaning of the underlining description logic, discovering why concepts are unsatisfiable can be difficult. Most modern tableaux reasoners do not provide any explanation as to why the classes are unsatisfiable. This paper presents a ‘black boxed’ heuristic approach based on identifying common errors and inferences.

We present a method for rapid development of benchmarks for Semantic Web knowledge base systems. At the core, we have a synthetic data generation approach for OWL that is scalable and models the real world data. The data-generation algorithm learns from real domain documents and generates benchmark data based on the extracted properties relevant for benchmarking. We believe that this is important because relative performance of systems will vary depending on the structure of the ontology and data used. However, due to the novelty of the Semantic Web, we rarely have sufficient data for benchmarking. Our approach helps overcome the problem of having insufficient real world data for benchmarking and allows us to develop benchmarks for a variety of domains and applications in a very time efficient manner. Based on our method, we have created a new Lehigh BibTeX Benchmark and conducted an experiment on four Semantic Web knowledge base systems. We have verified our hypothesis about the need for representative data by comparing the experimental result to that of our previous Lehigh University Benchmark. The difference in both experiments has demonstrated the influence of ontology and data on the capability and performance of the systems and thus the need of using a representative benchmark for the intended application of the systems.

Triple20 is a ontology manipulation and visualization tool for languages built on top of the Semantic-Web RDF triple model. In this article we explain how a triple-centered design compares to the use of a separate proprietary internal data model. We show how to deal with the problems of such a low-level data model and show that it offers advantages when dealing with inconsistent or incomplete data as well as for integrating tools.

We show how state-of-the-art Semantic Web technology can be used in e-Science, in particular, to automate the classification of proteins in biology. We show that the resulting classification was of comparable quality to that performed by a human expert, and how investigations using the classified data even resulted in the discovery of significant information that had previously been overlooked, leading to the identification of a possible drug-target.

E-Science experiments typically involve many distributed services maintained by different organisations. After an experiment has been executed, it is useful for a scientist to verify that the execution was performed correctly or is compatible with some existing experimental criteria or standards. Scientists may also want to review and verify experiments performed by their colleagues. There are no exsiting frameworks for validating such experiments in today’s e-Science systems. Users therefore have to rely on error checking performed by the services, or adopt other ad hoc methods. This paper introduces a platform-independent framework for validating workflow executions. The validation relies on reasoning over the documented

provenance

of experiment results and

semantic descriptions

of services advertised in a registry. This validation process ensures experiments are performed correctly, and thus results generated are meaningful. The framework is tested in a bioinformatics application that performs protein compressibility analysis.

Water resource management is becoming increasingly difficult due to the interaction of conflicting factors such as environmental sustainability and economic constraints. In Australia, the introduction of a water rights management framework is an administrative attempt to facilitate the resolution of this complex and multi-faceted problem. Policies relating to water allocation and trading have already advanced beyond our abilities to monitor, measure, report and enforce these policies. Mismanagement of this valued resource can have severe damaging long term environmental and economic effects. We believe that Semantic Web Services technologies will help decision makers minimise the risk of mismanagement. In this paper, we discuss the potential application of our dynamic service composition approach and its compatibility with other solutions. We identify the benefits for the different categories of users and discuss how ontologies can help to bridge the gap between specialists and non-specialists, or specialists focusing on separate aspects of the overall problem.

Killer apps are highly transformative technologies that create new markets and widespread patterns of behaviour. IT generally, and the Web in particular, has benefited from killer apps to create new networks of users and increase its value. The Semantic Web community on the other hand is still awaiting a killer app that proves the superiority of its technologies. There are certain features that distinguish killer apps from other ordinary applications. This paper examines those features in the context of the Semantic Web, in the hope that a better understanding of the characteristics of killer apps might encourage their consideration when developing Semantic Web applications.

This paper describes the design of and the deployment options for the Federal Enterprise Architecture Reference Model Ontology (FEA-RMO). The goal of any reference model is to provide a basis or starting point for some design process. While this is a laudable goal, it poses an immediate problem for representation; how can a model be represented in such a way that it can be extended in certain ways (for application to a particular problem), but not without regard to the advice that it gives? Reference models are usually expressed in natural language. At their best, such models provide a starting point for designers, and a checklist for their designs, to see that they conform to industry best practices. At worst, reference models expressed in natural language become a source of busy work; designers do not use the models during the design process, instead they spend time after the fact writing up an explanation of how and why they are compliant with the reference framework they’ve never seriously considered. In this paper, we have used Semantic Web technologies (in particular, RDF and OWL) to represent a reference mode for enterprise architecture in the US government. The content of the model comes from the recent Federal Enterprise Architecture Reference Model effort. We use the capability of RDF to distribute structured information to allow the reference model to be extended (as intended in its design). We use OWL to maintain the consistency of those extensions. The model has been used as the basis for an implementation of an FEA registry, a web-based system for managing enterprise architectures based on the FEA. The work of representing the FEA as formal ontologies was funded in part by GSA.

This paper is aimed at documenting the role of Web services and specifically Semantic Web Services in serving the needs of the entertainment industry by enabling the users to easily research and explore the large volume meta-content (content about content e.g. entertainment news, articles, reviews, interviews, trailers etc) and eventually leading them to FIND right content (Music, Movies TV program etc). In this scenario, semantic web techniques are used to not only develop and populate the ontology from different meta-content sources, but also to annotate them semantically to provide personalize meta content based Search-Find experience for main content. The paper outlines an application scenario where this is applied in a demonstrated proof of concept and articulates the next steps in the evolution of this architecture.

Semantic Web, using formal languages to represent document content and providing facilities for aggregating information spread around, can improve the functionalities provided nowadays by KM tools. This paper describes a Knowledge Management system, targeted at lawyers, which has been enhanced using Semantic Web technologies. The system assists lawyers during their everyday work, and allows them to manage their information and knowledge. A semantic layer has been added to the system, providing capabilities that make system usage easier and much more powerful, adding new and advanced means for create, share and access knowledge.

Structured Clinical Documentation is a fundamental component of the healthcare enterprise, linking both clinical (e.g., electronic health record, clinical decision support) and administrative functions (e.g., evaluation and management coding, billing). Documentation templates have proven to be an effective mechanism for implementing structured clinical documentation. The ability to create and manage definitions, i.e.,

definitions management,

for various concepts such as diseases, drugs, contraindications, complications, etc. is crucial for creating and maintaining documentation templates in a consistent and cohesive manner across the organization. Definitions management involves the creation and management of concepts that may be a part of controlled vocabularies, domain models and ontologies. In this paper, we present a real-world implementation of a semantics-based approach to automate structured clinical documentation based on a description logics (DL) system for ontology management. In this context we will introduce the ontological underpinnings on which clinical documents are based, namely the domain, document and presentation ontologies. We will present techniques that leverage these ontologies to render static and dynamic templates that contain branching logic. We will also evaluate the role of these ontologies in the context of managing the impact of definition changes on the creation and rendering of these documentation templates, and the ability to retrieve documentation templates and their instances precisely in a given clinical context.

Metadata have been already given to most of the data and objects in the real world, such as books, foods, digital contents like movie, electric devices, and so forth. Further, they can be accumulated electronically by barcodes and RFIDs, which is expected to spread explosively in 2005. On the other hand, web services are getting popular in the internet, and UPnP services and ECHONET are penetrating into the home network. In our project, we propose a new handheld application called Ubiquitous Service Finder, in which user can intuitively browse as icons the metadata around him/her in a cellular phone, then invoke the services semantically related to the metadata by simple drag and drop operation.

Web services can be presented to end-users via user interfaces (UIs) that facilitate the invocation of these services. Standardized, interoperable mechanisms for describing Web service interfaces enable the generation of UIs automatically and dynamically, at least in principle; the emergence of

Semantic Web services

opens the possibility of improving the generation process. In this paper, we propose a scheme that extends the OWL-S ontology, an emerging standard for Semantic Web services, to better enable the creation of such dynamic interfaces.

Semantic Web services go beyond “classical” Web services in enabling enhanced

discovery

,

invocation

and

composition

. In our scheme, the integration of semantic descriptions of Web services with semantic models of the user’s locally available data enables context-based personalization of dynamically created user interfaces, allowing us to minimize the number of necessary inputs. The need for this is compelling on mobile devices with limitations on input methods and screen size and where context data is readily available. The use of an underlying semantic model enables better accuracy than traditional form-filling techniques.

We propose an architecture for the creation and personalization of dynamic UIs from Web service descriptions. The key idea is to exploit the semantic relationships between type information of Web service input fields, and their association with information the system has about the user (such as the user’s current context, PIM data, context history, usage history, corporate data etc.), in order to personalize and simplify the invocation of Web services.

The uptake of Semantic Web technology by industry is progressing slowly. One of the problems is that academia is not always aware of the concrete problems that arise in industry. Conversely, industry is not often well informed about the academic developments that can potentially meet its needs. In this paper we present a first step towards a successful transfer of knowledge-based technology from academia to industry. In particular, we present a collection of use cases from enterprises which are interested in Semantic Web technology. We provide a detailed analysis of the use cases, identify their technology locks, discuss the appropriateness of knowledge-based technology and possible solutions. We summarize industrial knowledge processing requirements in the form of a typology of knowledge processing tasks and a library of high level components for realizing those tasks. Eventually these results are intended to focus academia on the development of plausible knowledge-based solutions for concrete industrial problems, and therefore, facilitate the uptake of Semantic Web technology within industry.

Situation awareness involves the identification of relationships among objects participating in an evolving situation. This problem in general is intractable and thus requires additional constraints and guidance defined by the user if there is to be any hope of creating practical situation awareness systems. This paper describes a Situation Awareness Assistant (SAWA) based on Semantic Web technologies that facilitates the development of user-defined domain knowledge in the form of formal ontologies and rule sets and then permits the application of the domain knowledge to the monitoring of relevant relations as they occur in a situations. SAWA includes tools for developing ontologies in OWL and rules in SWRL and provides runtime components for collecting event data, storing and querying the data, monitoring relevant relations and viewing the results through a graphical user interface. An application of SAWA to a scenario from the domain of supply logistics is presented along with a discussion of the challenges encountered in using SWRL for this task.

Since mobile Internet services are rapidly proliferating, finding the most appropriate service or services from among the many offered requires profound knowledge about the services which is becoming virtually impossible for ordinary mobile users. We propose a system that assists non-expert mobile users in finding the appropriate services that solve the real-world problems encountered by the user. Key components are a task knowledge base of tasks that a mobile user performs in daily life and a service knowledge base of services that can be used to accomplish user tasks. We present the architecture of the proposed system including a knowledge modeling framework, and a detailed description of a prototype system. We also show preliminary user test results; they indicate that the system allows a user to find appropriate services quicker with fewer loads than conventional commercial methods.

Rule languages and rule systems are widely used in business applications including computer-aided training, diagnostic fact finding, compliance monitoring, and process control. However, there is little interoperability between current rule-based systems. Interoperation is one of the main goals of the Semantic Web, and developing a language for sharing rules is often seen as a key step in reaching this goal. The Semantic Web Rule Language (SWRL) is an important first step in defining such a rule language. This paper describes the development of a configurable interoperation environment for SWRL built in Protégé-OWL, the most widely-used OWL development platform. This environment supports both a highly-interactive, full-featured editor for SWRL and a plugin mechanism for integrating third party rule engines. We have integrated the popular Jess rule engine into this environment, thus providing one of the first steps on the path to rule integration on the Web.

In this paper, we present a demonstrator system which applies semantic web services technology to business-to-business integration, focussing specifically on a logistics supply chain. The system is able to handle all stages of the service lifecycle – discovery, service selection and service execution. One unique feature of the system is its approach to protocol mediation, allowing a service requestor to dynamically modify the way it communicates with aprovider, based on a description of the provider’s protocol. We present the architecture of the system, together with an overview of the key components (discovery and mediation) and the implementation.

The Royal Institute Elcano (Real Instituto Elcano) in Spain is a prestigious independent political institute whose mission is to comment on the political situation in the world focusing on its relation to Spain. As part of its dissemination strategy it operates a public website. In this paper we present and evaluate the application of a

semantic

search engine to improve access to the Institute’s content: instead of retrieving documents based on user queries of keywords, the system accepts queries in natural language and returns answers rather than links to documents. Topics that will be discussed include ontology construction, automatic ontology population, semantic access through a natural language interface and a failure analysis.

The integration of heterogenous data sources is a crucial step for the upcoming semantic web – if existing information is not integrated, where will the data come from that the semantic web builds on? In this paper we present the gnowsis adapter framework, an implementation of an RDF graph system that can be used to integrate structured data sources, together with a set of already implemented adapters that can be used in own applications or extended for new situations. We will give an overview of the architecture and implementation details together with a description of the common problems in this field and our solutions, leading to an outlook on the future developments we expect. Using our presented results, researchers can generate test data for experiments and practitioners can access their desktop data sources as RDF graph.

Besides the reduction of time to market, there may be observed another trend in the automotive industry: built-to-order. Built-to-order reduces the mass production of cars to a limited-lot-production. Emphasis for optimization issues moves then from the production step to earlier steps as the collaboration of suppliers and manufacturer in development and delivering. Thus knowledge has to be shared between different organizations and departments in early development processes. In this paper we describe a project in the automotive industry where ontologies have two main purposes: (i) representing and sharing knowledge to optimize business processes for the testing of cars and (ii) integration of life data into this optimization process. A test car configuration assistant (semantic guide) is built on top of an inference engine equipped with an ontology containing information about parts and configuration rules. The ontology is attached to the legacy systems of the manufacturer and thus accesses and integrates up-to-date information. This semantic guide accelerates the configuration of test cars and thus reduces time to market.

The Semantic Web is a difficult concept for typical end-users to comprehend. There is a lack of widespread understanding on how the Semantic Web could be used in day-to-day applications. While there are now practical applications that have appeared supporting back-end functions such as data integration, there is only a handful of Semantic Web applications that the average Google user would want to use on a regular basis. The Concept Object Web is a prototype application for knowledge/intelligence management that aggregates data from text documents, XML files, and databases so that end-users can visually discover and learn about knowledge object (entities) without reading documents. The application addresses limitations with current knowledge/intelligence management tools giving end-users the power of the Semantic Web without the perceived burden and complexity of the Semantic Web.

This application demonstrates how to provide personalized, syndicated views on distributed web data using Semantic Web technologies. The application comprises four steps: The

information gathering step

, in which information from distributed, heterogenous sources is extracted and enriched with machine-readable semantics, the

operation step

for timely and up-to-date extractions, the

reasoning step

in which rules reason about the created semantic descriptions and additional knowledge-bases like ontologies and user profile information, and the

user interface creation step

in which the RDF-descriptions resulting from the reasoning step are interpreted and translated into an appropriate, personalized user interface. We have developed this application for solving the following real-world problem: We provide personalized, syndicated views on the publications of a large European research project with more than twenty geographically distributed partners and embed this information with contextual information on the project, its working groups, information about the authors, related publications, etc.

It is tedious and error-prone to query search engines manually in order to accumulate a large body of factual information. Search engines retrieve and rank potentially relevant documents for human perusal, but do not extract facts, or fuse information from multiple documents. This paper introduces

DynamicView

, a Semantic Web application for researchers to query, browse and visualize distribution and evolution of research areas in computer science. Present and historical web pages of top 20 universities in USA and China are analyzed, and research areas of faculties in computer science are extracted automatically by segmentation based algorithm. Different ontologies of ACM and MST classification systems are combined by SKOS vocabularies, and the classification of research areas is learned from the ACM Digital Library. Query results including numbers of researchers and their locations are visualized in SVG map and animation. Interestingly, great differences of hot topics do exist between the two countries, and the number of researchers in certain areas changed greatly from the year 2000 to 2005.

This paper presents Oyster, a Peer-to-Peer system for exchanging ontology metadata among communities in the Semantic Web. We describe how Oyster assists researchers in re-using existing ontologies, and how Oyster exploits semantic web techniques in data representation, query formulation, query result presentation to provide an online solution to share ontologies.

Bioinformatics and genomics cover a wide range of different data formats (i.e. annotations, pathways, structures, sequences) derived from experimental and in-silico biological analysis which are stored, used, and manipulated by scientists and machines. The volume of this data is huge and usually distributed in different locations, and often frequently being updated.

FungalWeb is the first project of its kind in Canada to focus on bringing semantic web technology to genomics. It aimed to bring together available expertise in ontologies, multi-agent systems, machine learning and natural language processing to build a tailored knowledgebase and semantic systems of direct use to the scientific discovery process in the domain of fungal genomics [1].

CONFOTO is a semantic browsing and annotation service for conference photos. It combines recent Web trends with the advantages of Semantic Web platforms. The service offers several tools to upload, link, browse and annotate pictures. Simple forms can be used to create multilingual titles, tags, or descriptions, while more advanced forms allow the relation of pictures to events, persons, ratings, and copyright information. CONFOTO provides tailored and interlinked browsers for photos, people, events, and documents. Remotely maintained photo descriptions can be added to the local knowledge base, data re-use is made possible via customizable syndication functions and a query interface.