Catálogo de publicaciones - libros

The Semantic Web seems to be evolving into a property-linked web of RDF data, conceptually divorced from (but physically housed in) the hyperlinked web of HTML documents. We discuss the Unified Web model that integrates the two webs and formalizes the structure and the semantics of interconnections between them. We also discuss the Hybrid Query Language which combines the Data and Information Retrieval techniques to provide a convenient and uniform way to retrieve data and documents from the Unified Web. We present the retrieval system SITAR and some preliminary results.

The vision of the defines a user’s personal information environment as a source and end-point of the Semantic Web: Knowledge workers comprehensively express their information and data with respect to their own conceptualizations. Semantic Web languages and protocols are used to formalize these conceptualizations and for coordinating local and global information access. From the way this vision is being pursued in the NEPOMUK project, we identified several requirements and research questions with respect to knowledge representation. In addition to the general question of the expressivity needed in such a scenario, two main challenges come into focus: i) How can we cope with the heterogeneity of knowledge models and ontologies, esp. multiple knowledge modules with potentially different interpretations? ii) How can we support the tailoring of ontologies towards different needs in various exploiting applications?

In this paper, we present NRL, an approach to these two question that is based on named graphs for the modularization aspect and a view concept for the tailoring of ontologies. This view concept turned out to be of additional value, as it also provides a mechanism to impose different semantics on the same syntactical structure.

We think that the elements of our approach are not only adequate for the semantic desktop scenario, but are also of importance as building blocks for the general Semantic Web.

The vision of semantic business processes is to enable the integration and inter-operability of business processes across organizational boundaries. Since different organizations model their processes differently, the discovery and retrieval of similar semantic business processes is necessary in order to foster inter-organizational collaborations. This paper presents our approach of using iSPARQL – our imprecise query engine based on iSPARQL – to query the OWL MIT Process Handbook – a large collection of over 5000 semantic business processes. We particularly show how easy it is to use iSPARQL to perform the presented process retrieval task. Furthermore, since choosing the best performing similarity strategy is a non-trivial, data-, and context-dependent task, we evaluate the performance of three simple and two human-engineered similarity strategies. In addition, we conduct machine learning experiments to learn similarity measures showing that complementary information contained in the different notions of similarity strategies provide a very high retrieval accuracy. Our preliminary results indicate that iSPARQL is indeed useful for extending the reach of queries and that it, therefore, is an enabler for inter- and intra-organizational collaborations.

While tags in collaborative tagging systems serve primarily an indexing purpose, facilitating search and navigation of resources, the use of the same tags by more than one individual can yield a collective classification schema. We present an approach for making explicit the semantics behind the tag space in social tagging systems, so that this collaborative organization can emerge in the form of groups of concepts and partial ontologies. This is achieved by using a combination of shallow pre-processing strategies and statistical techniques together with knowledge provided by ontologies available on the semantic web. Preliminary results on the del.icio.us and Flickr tag sets show that the approach is very promising: it generates clusters with highly related tags corresponding to concepts in ontologies and meaningful relationships among subsets of these tags can be identified.

In this paper we introduce the IdentityRank algorithm, developed as part of the EU-funded project NEWS to address the problem of named entity disambiguation in the context of semantic annotation of news items. The algorithm provides a ranking of the candidate instances within an ontology which can be associated to a certain entity. In order to do so, it uses as context the metadata available in a certain news item. The algorithm has been evaluated with promising results.

Many ontology mapping systems nowadays exist. In order to evaluate their strengths and weaknesses, benchmark datasets (ontology collections) have been created, several of which have been used in the most recent edition of the Ontology Alignment Evaluation Initiative (OAEI). While most OAEI tracks rely on straightforward comparison of the results achieved by the mapping systems with some kind of reference mapping created a priori, the ’conference’ track (based on the collection of heterogeneous ’conference organisation’ ontologies) instead encompassed multiway manual as well as automated analysis of mapping results themselves, with ‘correct’ and ‘incorrect’ cases determined a posteriori. The manual analysis consisted in simple labelling of discovered mappings plus discussion of selected cases (‘casuistics’) within a face-to-face consensus building workshop. The automated analysis relied on two different tools: the DRAGO system for testing the consistency of aligned ontologies and the system for discovering frequent associations in mapping meta-data including the phenomenon of graph-based mapping patterns. The results potentially provide specific feedback to the developers and users of mining tools, and generally indicate that automated mapping can rarely be successful without considering the larger context and possibly deeper semantics of the entities involved.

State-of-the-art research on automated learning of ontologies from text currently focuses on inexpressive ontologies. The acquisition of complex axioms involving logical connectives, role restrictions, and other expressive features of the Web Ontology Language OWL remains largely unexplored. In this paper, we present a method and implementation for enriching inexpressive OWL ontologies with expressive axioms which is based on a deep syntactic analysis of natural language definitions. We argue that it can serve as a core for a semi-automatic ontology engineering process supported by a methodology that integrates methods for both ontology learning and evaluation. The feasibility of our approach is demonstrated by generating complex class descriptions from Wikipedia definitions and from a fishery glossary provided by the Food and Agriculture Organization of the United Nations.

With the dawn of Ajax the capabilities of tracking user behavior multiplied. The same holds for the capabilities of adapting the user interface in a Web browser. To provide meaningful adaptation, the events, context and elements of an Ajaxified Portal must be given meaning. We show the use of ontologies as a model for user-related context and portal-related content. Content-related concepts are used to annotate Ajax widgets to associate them with meaning. As a user navigates a portal and fires events related to the widgets, a semantically rich user model is built, enabling suitable adaptation. Both the user model and the adaptation are based on ontologies and logic rules. Since user tracking and portal adaptation in the era of Ajax, now takes place on the client-side we present a resource-saving approach to executing adaptation rules in the browser. The approach is applied in an e-Government case study.

The current state of the art in supporting e-learning objectives is primarily based on providing a learner with learning content by using metadata standards. Due to this approach, several issues have to be taken into account – e. g. limited re-usability across different standards and learning contexts and high development costs. To overcome these issues, this paper describes an innovative semantic web service-oriented framework aimed at changing this data- and metadata-based paradigm to a highly dynamic service-oriented approach. Instead of providing a learner with static data, our approach is based on fulfilling learning objectives based on a dynamic supply of services. Therefore, we introduce a semantic layer architecture to abstract from existing learning data as well as process metadata standards by using Semantic Web Service (SWS) technology. Furthermore, our approach is based on abstract and reusable learning process models describing a learning process semantically as a composition of learning goals. Based on the formal semantic descriptions of learning goals as well as web services, services appropriate to achieve a specific learning goal can be selected, composed and invoked dynamically. This supports a high level of re-usability since a dynamic adaptation to different learning contexts and requirements of individual learners is achieved while utilizing standard-compliant learning applications. To illustrate the application of our approach, we describe a prototypical implementation utilizing the introduced approach based on the SWS framework WSMO.

In this paper we propose an algorithm for personalized learning based on a user’s query and a repository of lecture subparts —i.e., learning objects— both are described in a subset of OWL-DL. It works in two steps. First, it retrieves lecture subparts that cover as much as possible the user’s query. The solution is based on the concept covering problem for which we present a modified algorithm. Second, an appropriate sequence of lecture subparts is generated. Indeed, the different lecture subparts are only reachable when a given prerequisite is fulfilled, i.e., the learner must have a minimal background knowledge to be able to assimilate the requested learning object. Therefore, our algorithm takes into account the user’s knowledge to generate a personalized lecture composition and suggests a flow of learning objects to the user.