Multi-Paradigm Reasoning for Access to Heterogeneous GIS

Accessing and querying geographical data in a uniform way has become easier in recent years. Emerging standards like WFS turn the web into a geospatial web services enabled place. Mediation architectures like VirGIS overcome syntactical and semantical heterogeneity between several distributed sources. On mobile devices, however, this kind of solution is not suitable, due to limitations, mostly regarding bandwidth, computation power, and available storage space. The aim of this paper is to present a solution for providing powerful reasoning mechanisms accessible from mobile applications and involving data from several heterogeneous sources. By adapting contents to time and location, mobile web information systems can not only increase the value and suitability of the service itself, but can substantially reduce the amount of data delivered to users. Because many problems pertain to infrastructures and transportation in general and to way finding in particular, one cornerstone of the architecture is higher level reasoning on graph networks with the Multi-Paradigm Location Language MPLL. A mediation architecture is used as a “graph provider” in order to transfer the load of computation to the best suited component – graph construction and transformation for example being heavy on resources. Reasoning in general can be conducted either near the “source” or near the end user, depending on the specific use case. The concepts underlying the proposal described in this paper are illustrated by a typical and concrete scenario for web applications

DBpedia Mashups

If you see Wikipedia as a main place where the knowledge of mankind is concentrated, then DBpedia – which is extracted from Wikipedia – is the best place to find machine representation of that knowledge. DBpedia constitutes a major part of the semantic data on the web. Its sheer size and wide coverage enables you to use it in many kind of mashups: it contains biographical, geographical, bibliographical data; as well as discographies, movie meta-data, technical specifications, and links to social media profiles and much more. Just like Wikipedia, DBpedia is a truly cross-language effort, e.g., it provides descriptions and other information in various languages. In this chapter we introduce its structure, contents, its connections to outside resources. We describe how the structured information in DBpedia is gathered, what you can expect from it and what are its characteristics and limitations. We analyze how other mashups exploit DBpedia and present best practices of its usage. In particular, we describe how Sztakipedia – an intelligent writing aid based on DBpedia – can help Wikipedia contributors to improve the quality and integrity of articles. DBpedia offers a myriad of ways to accessing the information it contains, ranging from SPARQL to bulk download. We compare the pros and cons of these methods. We conclude that DBpedia is an un-avoidable resource for pplications dealing with commonly known entities like notable persons, places; and for others looking for a rich hub connecting other semantic resources

Linked Data Supported Information Retrieval

Um Inhalte im World Wide Web ausfindig zu machen, sind Suchmaschienen nicht mehr wegzudenken. Semantic Web und Linked Data Technologien ermöglichen ein detaillierteres und eindeutiges Strukturieren der Inhalte und erlauben vollkommen neue Herangehensweisen an die Lösung von Information Retrieval Problemen. Diese Arbeit befasst sich mit den Möglichkeiten, wie Information Retrieval Anwendungen von der Einbeziehung von Linked Data profitieren können. Neue Methoden der computer-gestützten semantischen Textanalyse, semantischen Suche, Informationspriorisierung und -visualisierung werden vorgestellt und umfassend evaluiert. Dabei werden Linked Data Ressourcen und ihre Beziehungen in die Verfahren integriert, um eine Steigerung der Effektivität der Verfahren bzw. ihrer Benutzerfreundlichkeit zu erzielen. Zunächst wird eine Einführung in die Grundlagen des Information Retrieval und Linked Data gegeben. Anschließend werden neue manuelle und automatisierte Verfahren zum semantischen Annotieren von Dokumenten durch deren Verknüpfung mit Linked Data Ressourcen vorgestellt (Entity Linking). Eine umfassende Evaluation der Verfahren wird durchgeführt und das zu Grunde liegende Evaluationssystem umfangreich verbessert. Aufbauend auf den Annotationsverfahren werden zwei neue Retrievalmodelle zur semantischen Suche vorgestellt und evaluiert. Die Verfahren basieren auf dem generalisierten Vektorraummodell und beziehen die semantische Ähnlichkeit anhand von taxonomie-basierten Beziehungen der Linked Data Ressourcen in Dokumenten und Suchanfragen in die Berechnung der Suchergebnisrangfolge ein. Mit dem Ziel die Berechnung von semantischer Ähnlichkeit weiter zu verfeinern, wird ein Verfahren zur Priorisierung von Linked Data Ressourcen vorgestellt und evaluiert. Darauf aufbauend werden Visualisierungstechniken aufgezeigt mit dem Ziel, die Explorierbarkeit und Navigierbarkeit innerhalb eines semantisch annotierten Dokumentenkorpus zu verbessern. Hierfür werden zwei Anwendungen präsentiert. Zum einen eine Linked Data basierte explorative Erweiterung als Ergänzung zu einer traditionellen schlüsselwort-basierten Suchmaschine, zum anderen ein Linked Data basiertes Empfehlungssystem

Semantics-supported cooperative learning for enhanced awareness

Awareness is required for supporting all forms of cooperation. In Computer Supported Collaborative Learning (CSCL), awareness can be used for enhancing collaborative opportunities across physical distances and in computer-mediated environments. Shared Knowledge Awareness (SKA) intends to increase the perception about the shared knowledge, students have in a collaborative learning scenario and also concerns the understanding that this group has about it. However, it is very difficult to produce accurate awareness indicators based on informal message exchange among the participants. Therefore, we propose a semantic system for cooperation that makes use of formal methods for knowledge representation based on semantic web technologies. From these semantics-enhanced repository and messages, it could be easier to compute more accurate awareness

Natural language programming of industrial robots

In this paper, we introduce a method to use written natural language instructions to program assembly tasks for industrial robots. In our application, we used a state-of-the-art semantic and syntactic parser together with semantically rich world and skill descriptions to create highlevel symbolic task sequences. From these sequences, we generated executable code for both virtual and physical robot systems. Our focus lays on the applicability of these methods in an industrial setting with real-time constraints

Connected Information Management

Society is currently inundated with more information than ever, making efficient management a necessity. Alas, most of current information management suffers from several levels of disconnectedness: Applications partition data into segregated islands, small notes don’t fit into traditional application categories, navigating the data is different for each kind of data; data is either available at a certain computer or only online, but rarely both. Connected information management (CoIM) is an approach to information management that avoids these ways of disconnectedness. The core idea of CoIM is to keep all information in a central repository, with generic means for organization such as tagging. The heterogeneity of data is taken into account by offering specialized editors. The central repository eliminates the islands of application-specific data and is formally grounded by a CoIM model. The foundation for structured data is an RDF repository. The RDF editing meta-model (REMM) enables form-based editing of this data, similar to database applications such as MS access. Further kinds of data are supported by extending RDF, as follows. Wiki text is stored as RDF and can both contain structured text and be combined with structured data. Files are also supported by the CoIM model and are kept externally. Notes can be quickly captured and annotated with meta-data. Generic means for organization and navigation apply to all kinds of data. Ubiquitous availability of data is ensured via two CoIM implementations, the web application HYENA/Web and the desktop application HYENA/Eclipse. All data can be synchronized between these applications. The applications were used to validate the CoIM ideas

DBpedia Mashups

If you see Wikipedia as a main place where the knowledge of mankind is concentrated, then DBpedia – which is extracted from Wikipedia – is the best place to find machine representation of that knowledge. DBpedia constitutes a major part of the semantic data on the web. Its sheer size and wide coverage enables you to use it in many kind of mashups: it contains biographical, geographical, bibliographical data; as well as discographies, movie meta-data, technical specifications, and links to social media profiles and much more. Just like Wikipedia, DBpedia is a truly cross-language effort, e.g., it provides descriptions and other information in various languages. In this chapter we introduce its structure, contents, its connections to outside resources. We describe how the structured information in DBpedia is gathered, what you can expect from it and what are its characteristics and limitations. We analyze how other mashups exploit DBpedia and present best practices of its usage. In particular, we describe how Sztakipedia – an intelligent writing aid based on DBpedia – can help Wikipedia contributors to improve the quality and integrity of articles. DBpedia offers a myriad of ways to accessing the information it contains, ranging from SPARQL to bulk download. We compare the pros and cons of these methods. We conclude that DBpedia is an un-avoidable resource for pplications dealing with commonly known entities like notable persons, places; and for others looking for a rich hub connecting other semantic resources