Contextual Understanding in Neural Dialog Systems: the Integration of External Knowledge Graphs for Generating Coherent and Knowledge-rich Conversations

The integration of external knowledge graphs has emerged as a powerful approach to enrich conversational AI systems with coherent and knowledge-rich conversations. This paper provides an overview of the integration process and highlights its benefits. Knowledge graphs serve as structured representations of information, capturing the relationships between entities through nodes and edges. They offer an organized and efficient means of representing factual knowledge. External knowledge graphs, such as DBpedia, Wikidata, Freebase, and Google's Knowledge Graph, are pre-existing repositories that encompass a wide range of information across various domains. These knowledge graphs are compiled by aggregating data from diverse sources, including online encyclopedias, databases, and structured repositories. To integrate an external knowledge graph into a conversational AI system, a connection needs to be established between the system and the knowledge graph. This can be achieved through APIs or by importing a copy of the knowledge graph into the AI system's internal storage. Once integrated, the conversational AI system can query the knowledge graph to retrieve relevant information when a user poses a question or makes a statement. When analyzing user inputs, the conversational AI system identifies entities or concepts that require additional knowledge. It then formulates queries to retrieve relevant information from the integrated knowledge graph. These queries may involve searching for specific entities, retrieving related entities, or accessing properties and attributes associated with the entities. The obtained information is used to generate coherent and knowledge-rich responses. By integrating external knowledge graphs, conversational AI systems can augment their internal knowledge base and provide more accurate and up-to-date responses. The retrieved information allows the system to extract relevant facts, provide detailed explanations, or offer additional context. This integration empowers AI systems to deliver comprehensive and insightful responses that enhance user experience. As external knowledge graphs are regularly updated with new information and improvements, conversational AI systems should ensure their integrated knowledge graphs remain current. This can be achieved through periodic updates, either by synchronizing the system's internal representation with the external knowledge graph or by querying the external knowledge graph in real-time

Annotation analysis for testing drug safety signals using unstructured clinical notes

BackgroundThe electronic surveillance for adverse drug events is largely based upon the analysis of coded data from reporting systems. Yet, the vast majority of electronic health data lies embedded within the free text of clinical notes and is not gathered into centralized repositories. With the increasing access to large volumes of electronic medical data-in particular the clinical notes-it may be possible to computationally encode and to test drug safety signals in an active manner.ResultsWe describe the application of simple annotation tools on clinical text and the mining of the resulting annotations to compute the risk of getting a myocardial infarction for patients with rheumatoid arthritis that take Vioxx. Our analysis clearly reveals elevated risks for myocardial infarction in rheumatoid arthritis patients taking Vioxx (odds ratio 2.06) before 2005.ConclusionsOur results show that it is possible to apply annotation analysis methods for testing hypotheses about drug safety using electronic medical records

Pervasive Personal Information Spaces

Each user’s electronic information-interaction uniquely matches their information behaviour, activities and work context. In the ubiquitous computing environment, this information-interaction and the underlying personal information is distributed across multiple personal devices. This thesis investigates the idea of Pervasive Personal Information Spaces for improving ubiquitous personal information-interaction. Pervasive Personal Information Spaces integrate information distributed across multiple personal devices to support anytime-anywhere access to an individual’s information. This information is then visualised through context-based, flexible views that are personalised through user activities, diverse annotations and spontaneous information associations. The Spaces model embodies the characteristics of Pervasive Personal Information Spaces, which emphasise integration of the user’s information space, automation and communication, and flexible views. The model forms the basis for InfoMesh, an example implementation developed for desktops, laptops and PDAs. The design of the system was supported by a tool developed during the research called activity snaps that captures realistic user activity information for aiding the design and evaluation of interactive systems. User evaluation of InfoMesh elicited a positive response from participants for the ideas underlying Pervasive Personal Information Spaces, especially for carrying out work naturally and visualising, interpreting and retrieving information according to personalised contexts, associations and annotations. The user studies supported the research hypothesis, revealing that context-based flexible views may indeed provide better contextual, ubiquitous access and visualisation of information than current-day systems

Shades of Grey: guidelines for working with the grey literature in systematic reviews for management and organizational studies

This paper suggests how the ‘grey literature’, the diverse and heterogeneous body of material that is made public outside, and not subject to, traditional academic peer-review processes, can be used to increase the relevance and impact of management and organization studies (MOS). The authors clarify the possibilities by reviewing 140 systematic reviews published in academic and practitioner outlets to answer the following three questions: (i) Why is grey literature excluded from/included in systematic reviews in MOS? (ii) What types of grey material have been included in systematic reviews since guidelines for practice were first established in this discipline? (iii) How is the grey literature treated currently to advance management and organization scholarship and knowledge? This investigation updates previous guidelines for more inclusive systematic reviews that respond to criticisms of current review practices and the needs of evidence-based management

CHORUS Deliverable 2.2: Second report - identification of multi-disciplinary key issues for gap analysis toward EU multimedia search engines roadmap

After addressing the state-of-the-art during the first year of Chorus and establishing the existing landscape in multimedia search engines, we have identified and analyzed gaps within European research effort during our second year. In this period we focused on three directions, notably technological issues, user-centred issues and use-cases and socio- economic and legal aspects. These were assessed by two central studies: firstly, a concerted vision of functional breakdown of generic multimedia search engine, and secondly, a representative use-cases descriptions with the related discussion on requirement for technological challenges. Both studies have been carried out in cooperation and consultation with the community at large through EC concertation meetings (multimedia search engines cluster), several meetings with our Think-Tank, presentations in international conferences, and surveys addressed to EU projects coordinators as well as National initiatives coordinators. Based on the obtained feedback we identified two types of gaps, namely core technological gaps that involve research challenges, and “enablers”, which are not necessarily technical research challenges, but have impact on innovation progress. New socio-economic trends are presented as well as emerging legal challenges

The Use of Social Media in Enterprises for Communication, Collaboration, and Knowledge Management

Der Erfolg von Social Media im Internet hat dazu geführt, dass diese Technologie zunehmend auch in Unternehmen eingesetzt, oder über deren Implementierung nachgedacht wird. Durch die erwartete Verbesserung der Kommunikation und Interaktion zwischen Mitarbeitern auf der einen Seite und des Wissensmanagements auf der anderen Seite er-hoffen sich Entscheidungsträger in Unternehmen einen erheblichen betriebswirtschaftlichen Nutzen. Obwohl es einige Beispiele erfolgreicher Enterprise-Social-Media(ESM)-Implementierungen gibt und mehr als 90% der Fortune 500 Unternehmen ESM eingeführt haben oder dies planen, verfehlen 80% der ESM-Projekte die eingangs definierten Ziele. Während die Entscheidung, die Software einzukaufen, zentral getroffen wird, hängt deren Erfolg von der aktiven Partizipation der Mitarbeiter ab – wie sich anhand der genannten Statistiken zeigt, ist beides nicht zwangsläufig korreliert. Im Gegensatz zu organischem Wachstum, wie es in Social-Media-Anwendungen im Internet in den vergangenen Jahren beobachtet werden konnte (z.B. bei Facebook), ist die Nutzungsrate von internen ESM oft zu gering, um den Fortbestand der Community zu sichern. Es zeigt sich dabei verstärkt, dass passive Roll-Out-Strategien, die darauf vertrauen, dass es ein vergleichbares organisches Wachstum auch bei ESM gibt, zum Scheitern verurteilt sind. Viel-mehr müssen Analysen im Vorhinein das für einen spezifischen Anwendungsbereich geeignete Tool identifizieren, und Strategien entwickelt werden, wie Mitarbeiter für die Interaktion über die neuen Anwendungen gewonnen werden können. Da Ausgaben für Informationstechnologien bei einem geringen Nutzungsgrad nicht zu-rechtfertigen sind, trägt die vorliegende Dissertation in acht Essays dazu bei, verschiedene Facetten der ESM-Nutzung näher beleuchten und so zu einem besseren Verständnis des Themas und damit einhergehend einer effektiveren und effizienteren Implementierung von ESM beitragen. Sowohl die Analyse von Einflussfaktoren auf verschiedene Nutzungstypen von ESM, die Optimierung von Enterprise-Suchalgorithmen als auch die Neuinterpretation von Online-Produkt-Ratings können dabei helfen, die Veränderungen der internen und externen Kommunikation, Kollaboration und des Wissensmanagements, die sich durch den Einsatz von ESM ergeben, besser zu erklären und bedarfs-gerechter einzusetzen. Die theoretischen und praktischen Implikationen, welche sich konkret aus den einzelnen Essays ergeben, werden in den entsprechenden Abschnitten der jeweiligen Papiere erläutert

Information Filtering in Electronic Networks of Practice: An fMRI Investigation of Expectation (Dis)confirmation

Online forums sponsored by electronic networks of practice (ENPs) have become an important platform for technology-mediated knowledge exchange, yet relatively little is known about how ENP participants filter and evaluate the information they encounter on these forums. This study integrates perspectives from expectation confirmation theory, prospect theory, and neuroscience research to explore how ENP forum filtering judgments are influenced when expectations formed on the basis of contextual cues are confirmed or disconfirmed by the examination of solution quality. We summarize six different models of expectation confirmation explored in previous IS literature and report the results of a neuroimaging experiment using functional MRI (fMRI) that paired both positive and negative contextual cues with high- and low-quality solutions on a mock ENP forum interface. Results show that evaluation judgments are strongest in conditions where initial contextual cue judgments are confirmed by examination of solution quality except when the perceived expectation-experience gap is large, providing evidence for an assimilation-contrast model of expectation confirmation. We also found neural activation differences for expectation confirmation vs. disconfirmation and, consistent with prospect theory, differences in filtering behaviors with respect to unexpected gains vs. unexpected losses

Adaptive User Interfaces for Intelligent E-Learning: Issues and Trends

Adaptive User Interfaces have a long history rooted in the emergence of such eminent technologies as Artificial Intelligence, Soft Computing, Graphical User Interface, JAVA, Internet, and Mobile Services. More specifically, the advent and advancement of the Web and Mobile Learning Services has brought forward adaptivity as an immensely important issue for both efficacy and acceptability of such services. The success of such a learning process depends on the intelligent context-oriented presentation of the domain knowledge and its adaptivity in terms of complexity and granularity consistent to the learner’s cognitive level/progress. Researchers have always deemed adaptive user interfaces as a promising solution in this regard. However, the richness in the human behavior, technological opportunities, and contextual nature of information offers daunting challenges. These require creativity, cross-domain synergy, cross-cultural and cross-demographic understanding, and an adequate representation of mission and conception of the task. This paper provides a review of state-of-the-art in adaptive user interface research in Intelligent Multimedia Educational Systems and related areas with an emphasis on core issues and future directions