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INFODOQ ist eine Webbasierte Informationsplattform für den Einsatz in ambulant betreuten Wohn- Pflegegruppen. Das System wurde aufgrund des Wunsches nach einer transparenten Informations-, Abstimmungs- und Kommunikationsplattform verschiedener Demenz-Wohngemeinschaften zur Optimierung des Betreuungs-[zum vollständigen Text gelangen Sie über die oben angegebene URL], 18. Deutscher Kongress für Versorgungsforschung (DKVF)

Item does not contain fulltext In this paper, a multi-agent simulation model is presented to investigate the dynamics of 'mutual-support networks': online social networks consisting of both humans and artificial agents. Via such networks, human users who are coping with stress can share their problems, via text messages, with human peers as well as 'artificial friends'. Even though not everybody feels comfortable sharing personal problems with artificial agents, a bot is always available to help human users, and does not face any negative consequence of providing help to stressed peers. Using the simulation model, the dynamics of social networks consisting of an arbitrary combination of humans and agents have been explored under various circumstances. This exploration resulted in several insights that are useful for shaping our vision on artificial friends: (1) humans can provide less emotional support than artificial agents because they have limited emotional resources, (2) the type of support that is provided has a large impact on the human’s stress level, and (3) the more open users are to receiving automated support, the more effective the support is in reducing their stress level. The model was internally validated by means of a mathematical verification. SocInfo 2018: Social Informatics: 10th International Conference on Social Informatics (St. Petersburg, Russia, September 25-28, 2018)

Forms are our gates to the web. They enable us to access the deep content of web sites. Automatic form understanding unlocks this content for applications ranging from crawlers to meta-search engines and is essential for improving usability and accessibility of the web. Form understanding has received surprisingly little attention other than as component in specific applications such as crawlers. No comprehensive approach to form understanding exists and previous works disagree even in the definition of the problem. In this paper, we present OPAL, the first comprehensive approach to form understanding. We identify form labeling and form interpretation as the two main tasks involved in form understanding. On both problems OPAL pushes the state of the art: For form labeling, it combines signals from the text, structure, and visual rendering of a web page, yielding robust characterisations of common design patterns. In extensive experiments on the ICQ and TEL-8 benchmarks and a set of 200 modern web forms OPAL outperforms previous approaches by a significant margin. For form interpretation, we introduce a template language to describe frequent form patterns. These two parts of OPAL combined yield form understanding with near perfect accuracy (> 98%).

We present RDFox—a main-memory, scalable, centralised RDF store that supports materialisation-based parallel datalog reasoning and SPARQL query answering. RDFox uses novel and highlyefficient parallel reasoning algorithms for the computation and incremental update of datalog materialisations with efficient handling of owl: sameAs. In this system description paper, we present an overview of the system architecture and highlight the main ideas behind our indexing data structures and our novel reasoning algorithms. In addition, we evaluate RDFox on a high-end SPARC T5-8 server with 128 physical cores and 4TB of RAM. Our results show that RDFox can effectively exploit such a machine, achieving speedups of up to 87 times, storage of up to 9.2 billion triples, memory usage as low as 36.9 bytes per triple, importation rates of up to 1 million triples per second, and reasoning rates of up to 6.1 million triples per second.

The Microsoft SenseCam is a small lightweight wearable camera used to passively capture photos and other sensor readings from a user's day-to-day activities. It can capture up to 3,000 images per day, equating to almost 1 million images per year. It is used to aid memory by creating a personal multimedia lifelog, or visual recording of the wearer's life. However the sheer volume of image data captured within a visual lifelog creates a number of challenges, particularly for locating relevant content. Within this work, we explore the applicability of semantic concept detection, a method often used within video retrieval, on the novel domain of visual lifelogs. A concept detector models the correspondence between low-level visual features and high-level semantic concepts (such as indoors, outdoors, people, buildings, etc.) using supervised machine learning. By doing so it determines the probability of a concept's presence. We apply detection of 27 everyday semantic concepts on a lifelog collection composed of 257,518 SenseCam images from 5 users. The results were then evaluated on a subset of 95,907 images, to determine the precision for detection of each semantic concept and to draw some interesting inferences on the lifestyles of those 5 users. We additionally present future applications of concept detection within the domain of lifelogging. © 2008 Springer Berlin Heidelberg.

Wrapper induction faces a dilemma: To reach web scale, it requires automatically generated examples, but to produce accurate results, these examples must have the quality of human annotations. We resolve this conflict with AMBER, a system for fully automated data extraction from result pages. In contrast to previous approaches, AMBER employs domain specific gazetteers to discern basic domain attributes on a page, and leverages repeated occurrences of similar attributes to group related attributes into records rather than relying on the noisy structure of the DOM.With this approach AMBER is able to identify records and their attributes with almost perfect accuracy (>98%) on a large sample of websites. To make such an approach feasible at scale, AMBER automatically learns domain gazetteers from a small seed set. In this demonstration, we show how AMBER uses the repeated structure of records on deep web result pages to learn such gazetteers. This is only possible with a highly accurate extraction system. Depending on its parametrization, this learning process runs either fully automatically or with human interaction. We show how AMBER bootstraps a gazetteer for UK locations in 4 iterations: From a small seed sample we achieve 94.4% accuracy in recognizing UK locations in the 4th iteration. Copyright is held by the International World Wide Web Conference Committee (IW3C2).

Search engines are the sinews of the web. These sinews have become strained, however: Where the web's function once was a mix of library and yellow pages, it has become the central marketplace for information of almost any kind. We search more and more for objects with specific characteristics, a car with a certain milage, an affordable apartment close to a good school, or the latest accessory for our phones. Search engines all too often fail to provide reasonable answers, making us sift through dozens of websites with thousands of offers-never to be sure a better offer isn't just around the corner. What search engines are missing is understanding of the objects and their attributes published on websites. Automatically identifying and extracting these objects is akin to alchemy: transforming unstructured web information into highly structured data with near perfect accuracy. With DIADEM we present a formula for this transformation, but at a price: DIADEM identifies and extracts data from a website with high accuracy. The price is that for this task we need to provide DIADEM with extensive knowledge about the ontology and phenomenology of the domain, i.e., about entities (and relations) and about the representation of these entities in the textual, structural, and visual language of a website of this domain. In this demonstration, we demonstrate with a first prototype of DIADEM that, in contrast to alchemists, DIADEM has developed a viable formula. Copyright is held by the International World Wide Web Conference Committee (IW3C2).

Web forms are the interfaces of the deep web. Though modern web browsers provide facilities to assist in form filling, this assistance is limited to prior form fillings or keyword matching. Automatic form understanding enables a broad range of applications, including crawlers, meta-search engines, and usability and accessibility support for enhanced web browsing. In this demonstration, we use a novel form understanding approach, OPAL, to assist in form filling even for complex, previously unknown forms. OPAL associates form labels to fields by analyzing structural properties in the HTML encoding and visual features of the page rendering. OPAL interprets this labeling and classifies the fields according to a given domain ontology. The combination of these two properties, allows OPAL to deal effectively with many forms outside of the grasp of existing form filling techniques. In the UK real estate domain, OPAL achieves > 99% accuracy in form understanding. Copyright is held by the International World Wide Web Conference Committee (IW3C2).

The main problem encountered when starting verification of goals for some formal system, is the ambiguity of those goals when they are specified in natural language. To verify goals given in natural language, a translation of those goals to the formalism of the verification tool is required. The main concern is to assure equivalence of the final translation and the original. A structured method is required to assure equivalence in every case. This article proposes a goal formalisation method in five steps, in which the domain expert is involved in such a way that the correctness of the result can be assured. The contribution of this article is a conceptual goal model, a formal expression language for this model, and a structured method which transforms any input goal to a fully formalised goal in the required target formalism. The proposed formalisation method guarantees essential properties like correctness, traceability, reduced variability and reusability.

The OWL Web Ontology Language is designed for use by applications that need to process the content of information instead of just presenting information to humans. OWL facilitates greater machine interpretability of Web content than that supported by XML, RDF, and RDF Schema (RDF-S) by providing additional vocabulary along with a formal semantics. OWL has three increasingly-expressive sublanguages: OWL Lite, OWL DL, and OWL Full. This document is written for readers who want a first impression of the capabilities of OWL. It provides an introduction to OWL by informally describing the features of each of the sublanguages of OWL. Some knowledge of RDF Schema is useful for understanding this document, but not essential. After this document, interested readers may turn to the OWL Guide for more detailed descriptions and extensive examples on the features of OWL. The normative formal definition of OWL can be found in the OWL Semantics and Abstract Syntax.

Mutations in the genes ovo or otu can cause abnormal proliferation of XX germ cells, which leads to so-called ovarian tumors, or they can lead to the elimination of XX germ cells, such that adult females possess empty ovaries. Males carrying ovo or otu mutations are unaffected. To find out when this sexual dimorphism affects germ cells, we analyzed the requirement of embryos and larvae for zygotic ovo and otu products. We found that ovo is required for the survival of XX germ cells during larval stages, while XX germ cells lacking otu survive until metamorphosis. Furthermore, we found no sex-transformed mutant larval germ cells and no evidence for an early sex-specific vital process acting in germ cells of the embryo, contrary to what had been suggested earlier.

We have analyzed a substantial number of language documentation artifacts, including language standards, language specifications, language reference manuals, as well as internal documents of standardization bodies. We have reverse-engineered their intended internal structure, and compared the results. The Language Document Format (LDF), was developed to specifically support the documentation domain. We have also integrated LDF into an engineering discipline for language documents including tool support, for example, for rendering language documents, extracting grammars and samples, and migrating existing documents into LDF. The definition of LDF, tool support for LDF, and LDF applications are freely available through SourceForge.

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We are pleased to present the proceedings of the Third International Conference on Software Language Engineering (SLE 2010). The conference will be held in Eindhoven, the Netherlands during October 12-13, 2010 and will be co-located with The Ninth International Conference on Generative Programming and Component Engineering (GPCE'10), and The Workshop on Feature-Oriented Software Development (FOSD). An important goal of SLE is to integrate the different sub-communities of the software-language-engineering community to foster cross-fertilization and strengthen research overall. The Doctoral Symposium at SLE 2010 contributes towards these goals by providing a forum for both early and late-stage PhD students to present their research and get detailed feedback and advice from other researchers. The SLE conference series is devoted to a wide range of topics related to artificial languages in software engineering. SLE is an international research forum that brings together researchers and practitioners from both industry and academia to expand the frontiers of software language engineering. SLE's foremost mission is to encourage and organize communication between communities that have traditionally looked at software languages from different, more specialized, and yet complementary perspectives. SLE emphasizes the fundamental notion of languages as opposed to any realization in specific technical spaces. In this context, the term "software language" comprises all sorts of artificial languages used in software development including general-purpose programming languages, domain-specific languages, modeling and meta-modeling languages, data models, and ontologies. Software language engineering is the application of a systematic, disciplined, quantifiable approach to the development, use, and maintenance of these languages. The SLE conference is concerned with all phases of the lifecycle of software languages; these include the design, implementation, documentation, testing, deployment, evolution, recovery, and retirement of languages. Of special interest are tools, techniques, methods, and formalisms that support these activities. In particular, tools are often based on, or automatically generated from, a formal description of the language. Hence, the treatment of language descriptions as software artifacts, akin to programs, is of particular interest - while noting the special status of language descriptions, and the tailored engineering principles and methods for modularization, refactoring, refinement, composition, versioning, co-evolution, and analysis that can be applied to them. The response to the call for papers for SLE 2010 was very enthusiastic. We received 79 full submissions from 108 initial abstract submissions. From these submissions, the Program Committee (PC) selected 25 papers: 17 full papers, five short papers, and two tool demonstration papers, resulting in an acceptance rate of 32%. To ensure the quality of the accepted papers, each submitted paper was reviewed by at least three PC members. Each paper was discussed in detail during the electronic PC meeting. A summary of this discussion was prepared by members of the PC and provided to the authors along with the reviews.

We present an audiovisual creativity tool that automatically recognizes facial expressions in real time, producing sounds in combination with images. The facial expression recognition component detects and tracks a face and outputs a feature vector of motions of specific locations in the face. The feature vector is used as input to a Bayesian network which classifies facial expressions into several categories (e.g., angry, disgusted, happy, etc.). The classification results are used along with the feature vector to generate a combination of sounds and images that change in real time depending on the person's facial expressions. We explain the basic components of our tool and several possible applications in the arts (performance, installation) and medical domains.

We analyze the datatype system of OWL and OWL 2, and discuss certain nontrivial consequences of its definition, such as the extensibility of the set of supported datatypes and complexity of reasoning. We also argue that certain datatypes from the list of normative datatypes in the current OWL 2 Working Draft are inappropriate and should be replaced with different ones. Finally, we present an algorithm for datatype reasoning. Our algorithm is modular in the sense that it can handle any datatype that supports certain basic operations. We show how to implement these operations for number and string datatypes. © 2008 Springer Berlin Heidelberg.

Finding mappings between compatible ontologies is an important but difficult open problem. Instance-based methods for solving this problem have the advantage of focusing on the most active parts of the ontologies and reflect concept semantics as they are actually being used. However such methods have not at present been widely investigated in ontology mapping, compared to linguistic and structural techniques. Furthermore, previous instance-based mapping techniques were only applicable to cases where a substantial set of instances was available that was doubly annotated with both vocabularies. In this paper we approach the mapping problem as a classification problem based on the similarity between instances of concepts. This has the advantage that no doubly annotated instances are required, so that the method can be applied to any two corpora annotated with their own vocabularies. We evaluate the resulting classifiers on two real-world use cases, one with homogeneous and one with heterogeneous instances. The results illustrate the efficiency and generality of this method.

We propose a novel method for reasoning in the description logic . After a satisfiability preserving transformation from to the description logic , the obtained Tbox is converted into an ordered binary decision diagram (OBDD) which represents a canonical model for . This OBDD is turned into a disjunctive datalog program that can be used for Abox reasoning. The algorithm is worst-case optimal w.r.t. data complexity, and admits easy extensions with DL-safe rules and ground conjunctive queries. © 2008 Springer Berlin Heidelberg.

We introduce as a decidable fragment of the Semantic Web Rule Language (SWRL) that admits reasoning in polynomial time. is based on the tractable description logic , and encompasses an extended notion of the recently proposed DL rules for that logic. Thus extends with a number of features introduced by the forthcoming OWL 2, such as disjoint roles, local reflexivity, certain range restrictions, and the universal role. We present a reasoning algorithm based on a translation of to Datalog, and this translation also enables the seamless integration of DL-safe rules into . While reasoning with DL-safe rules as such is already highly intractable, we show that DL-safe rules based on the Description Logic Programming (DLP) fragment of OWL 2 can be admitted in without losing tractability. © 2008 Springer Berlin Heidelberg.

Semantic descriptions of non-textual media available on the web can be used to facilitate retrieval and presentation of media assets and documents containing them. While technologies for multimedia semantic descriptions already exist, there is as yet no formal description of a high quality multimedia ontology that is compatible with existing (semantic) web technologies. We explain the complexity of the problem using an annotation scenario. We then derive a number of requirements for specifying a formal multimedia ontology before we present the developed ontology, COMM, and evaluate it with respect to our requirements. We provide an API for generating multimedia annotations that conform to COMM.

The deployment and integration of audio processing tools can enhance the semantic annotation of multimedia content, and as a consequence, improve the effectiveness of conceptual access tools. This paper overviews the various ways in which automatic speech and audio analysis can contribute to increased granularity of automatically extracted metadata. A number of techniques will be presented, including the alignment of speech and text resources, large vocabulary speech recognition, key word spotting and speaker classification. The applicability of techniques will be discussed from a media crossing perspective. The added value of the techniques and their potential contribution to the content value chain will be illustrated by the description of two (complementary) demonstrators for browsing broadcast news archives. © Springer-Verlag Berlin Heidelberg 2006.

RDF Query Language proposals are numerous. However, the most prominent proposals are query languages that were conceived as first generation tryouts of RDF querying, with little or no RDF-specific implementation and use experience to guide design, and based on an ever changing set of syntactical and semantic specifications. In this chapter, we introduce a set of general requirements for an RDF query language. This set is compiled from discussions between RDF implementors, our own experience and user feedback that we received on our work in Sesame, as well as general principles of query language design. We go on to show how we have compiled these requirements into designing the SeRQL query language, and conclude that SeRQL can be considered a real second generation RDF querying and transformation language.

This article presents a technique to incrementally maintain materializations of ontological entailments. Materialization consists in precomputing and storing a set of implicit entailments, such that frequent and/or crucial queries to the ontology can be solved more efficiently. The central problem that arises with materialization is its maintenance when axioms change, viz. the process of propagating changes in explicit axioms to the stored implicit entailments. When considering rule-enabled ontology languages that are operationalized in logic databases, we can distinguish two types of changes. Changes to the ontology will typically manifest themselves in changes to the rules of the logic program, whereas changes to facts will typically lead to changes in the extensions of logical predicates. The incremental maintenance of the latter type of changes has been studied extensively in the deductive database context and we apply the technique proposed in [30] for our purpose. The former type of changes has, however, not been tackled before. In this article we elaborate on our previous papers [32, 33], which extend the approach of [30] to deal with changes in the logic program. Our approach is not limited to a particular ontology language but can be generally applied to arbitrary ontology languages that can be translated to Datalog programs, i.e. such as O-Telos, F-Logic [16] RDF(S), or Description Logic Programs [34]. © Springer-Verlag Berlin Heidelberg 2004.

This paper discusses the incremental maintenance of materialized ontologies in a rule-enabled Semantic Web. Materialization allows to speed up query processing by explicating the implicit entailments which are sanctioned by the semantics of an ontology. The complexity of reasoning with the ontology is thereby shifted from query time to update time. We assume that materialization techniques will frequently be important to achieve a scalable Semantic Web, since read access to ontologies is predominant. Central to materialization are maintenance techniques that allow to incrementally update a materialization when changes occur. We present a novel solution that allows to cope with changes in rules and facts. To achieve this we extend a known approach for the incremental maintenance of views in deductive databases. We show how our technique can be employed for a broad range of existing Web ontology languages, such as RDF/S and subsets of OWL and present a first evaluation. © Springer-Verlag Berlin Heidelberg 2003.

Web publishing systems have to take into account a plethora of Web-enabled devices, user preferences and abilities. Technologies generating these presentations will need to be explicitly aware of the context in which the information is being presented. Semantic Web technology can be a fundamental part of the solution to this problem by explicitly modeling the knowledge needed to adapt presentations to a specific delivery context. We propose the development of a Smart Style layer which is able to process metadata that describes content and use this metadata to improve the presentation of the content to human users. In the paper, we derive the requirements of such a Smart Style layer by considering Web design from both the document engineering and graphic design perspectives. In addition, design tradeoffs made by human designers have to be taken into account for the automated process. After stating the requirements for a Smart Style layer, we discuss to what extent the currently available Web technology can be used and what its limitations are. The limitations are illustrated with examples of potential future extensions.

Knowledge portals provide views onto domain-specific information on the World Wide Web, thus helping their users find relevant, domain-specific information. The construction of intelligent access and the contribution of information to knowledge portals, however, remained an ad hoc task, requiring extensive manual editing and maintenance by the knowledge portal providers. To diminish these efforts, we use ontologies as a conceptual backbone for providing, accessing, and structuring information in a comprehensive approach for building and maintaining knowledge portals. We present one research study and one commercial case study that show how our approach, called seal (semantic portal), is used in practice.

Ontologies are becoming an important mechanism to build information systems. Nevertheless, there is still no systematic approach to support the design of such systems using tools that are common to information systems developers. In this paper, we propose an approach for deriving object frameworks from domain ontologies and then we show the application of this approach in the software process domain.

Semantic Annotation is a basic technology for intelligent content and is bene cial in a wide range of content-oriented intelligent applications, esp. in the area of the Semantic Web. In this paper we present our work in ontology-based semantic annotation, which is embedded in a scenario of a knowledge portal application. Starting with seemingly good and bad manual semantic annotation, we describe our experiences made within the KA2 initiative. The experiences gave us the starting point for developing an ergonomic and knowledge base-supported annotation tool. Furthermore, the annotation tool described are currently extended with mechanisms for semi-automatic information-extraction based annotation. Supporting the evolving nature of semantic content we additionally describe our idea of evolving ontologies supporting semantic annotation.This paper has been presented at the COLING-2000 Workshop on Semantic Annotation and Intelligent Content, Centre Universitaire, Luxembourg, 5.-6. August, 2000.