Full and Partial Knowledge Sharing on Intra-Organizational Broadcast Media

Knowledge sharing, along with its potential predictors, has been a popular research topic. This research extends prior research by examining potential predictors of knowledge sharing together within a more comprehensive model with two additional contexts: the type of recipient of the knowledge is the recipients of intraorganizational broadcast media, and the type of knowledge sharing behavior (full knowledge sharing and partial knowledge sharing). The results of this study suggest that what predicts knowledge sharing behaviors depends on the type of knowledge sharing behavior when considering why people share their knowledge through intra-organizational broadcast media. We explore theoretical implications and future research avenues

Roles '07 – Proceedings of the 2nd Workshop on Roles and Relationships in Object Oriented Programming, Multiagent Systems, and Ontologies : workshop co-located with ECOOP 2007 Berlin, July 30 and 31, 2007

Roles are a truly ubiquitous notion: like classes, objects, and relationships, they pervade the vocabulary of all disciplines that deal with the nature of things and how these things relate to each other. In fact, it seems that roles are so fundamental a notion that they must be granted the status of an ontological primitive. The definition of roles depends on the definition of relationships. With the advent of Object Technology, however, relationships have moved out of the focus of attention, giving way to the more restricted concept of attributes or, more technically, references to other ob- jects. A reference is tied to the object holding it and as such is asymmetric – at most the target of the reference can be associated with a role. This is counter to the intuition that every role should have at least one counter-role, namely the one it interacts with. It seems that the natural role of roles in object-oriented designs can only be restored by installing relationships (collaborations, teams, etc.) as first-class programming concepts. By contrast, the relational nature of roles is already acknowl- edged in the area of Multiagent Systems, since roles are related to the interaction among agents and to communication protocols. However, in this area there is no convergence on a single definition of roles yet, and different points of view, such as agent software en- gineering, specification languages, agent communication, or agent programming languages, make different use of roles. Like its pre- decessor “Roles, an interdisciplinary perspective” (Roles’05) held at the AAAI 2005 Fall Symposium (see the website of the Symposium http://www.aaai.org/Press/Reports/Symposia/Fall/fs-05-08.php), this workshop aimed at gathering researchers from different dis- ciplines to foster interchange of knowledge and ideas concerning roles and relationships, and in particular to converge on ontolog- ically founded proposals which can be applied to programming and agent languages

Exploring attributes, sequences, and time in Recommender Systems: From classical to Point-of-Interest recommendation

Tesis Doctoral inédita leída en la Universidad Autónoma de Madrid, Escuela Politécnica Superior, Departamento de Ingenieria Informática. Fecha de lectura: 08-07-2021Since the emergence of the Internet and the spread of digital communications throughout the world, the amount of data stored on the Web has been growing exponentially. In this new digital era, a large number of companies have emerged with the purpose of ltering the information available on the web and provide users with interesting items. The algorithms and models used to recommend these items are called Recommender Systems. These systems are applied to a large number of domains, from music, books, or movies to dating or Point-of-Interest (POI), which is an increasingly popular domain where users receive recommendations of di erent places when they arrive to a city. In this thesis, we focus on exploiting the use of contextual information, especially temporal and sequential data, and apply it in novel ways in both traditional and Point-of-Interest recommendation. We believe that this type of information can be used not only for creating new recommendation models but also for developing new metrics for analyzing the quality of these recommendations. In one of our rst contributions we propose di erent metrics, some of them derived from previously existing frameworks, using this contextual information. Besides, we also propose an intuitive algorithm that is able to provide recommendations to a target user by exploiting the last common interactions with other similar users of the system. At the same time, we conduct a comprehensive review of the algorithms that have been proposed in the area of POI recommendation between 2011 and 2019, identifying the common characteristics and methodologies used. Once this classi cation of the algorithms proposed to date is completed, we design a mechanism to recommend complete routes (not only independent POIs) to users, making use of reranking techniques. In addition, due to the great di culty of making recommendations in the POI domain, we propose the use of data aggregation techniques to use information from di erent cities to generate POI recommendations in a given target city. In the experimental work we present our approaches on di erent datasets belonging to both classical and POI recommendation. The results obtained in these experiments con rm the usefulness of our recommendation proposals, in terms of ranking accuracy and other dimensions like novelty, diversity, and coverage, and the appropriateness of our metrics for analyzing temporal information and biases in the recommendations producedDesde la aparici on de Internet y la difusi on de las redes de comunicaciones en todo el mundo, la cantidad de datos almacenados en la red ha crecido exponencialmente. En esta nueva era digital, han surgido un gran n umero de empresas con el objetivo de ltrar la informaci on disponible en la red y ofrecer a los usuarios art culos interesantes. Los algoritmos y modelos utilizados para recomendar estos art culos reciben el nombre de Sistemas de Recomendaci on. Estos sistemas se aplican a un gran n umero de dominios, desde m usica, libros o pel culas hasta las citas o los Puntos de Inter es (POIs, en ingl es), un dominio cada vez m as popular en el que los usuarios reciben recomendaciones de diferentes lugares cuando llegan a una ciudad. En esta tesis, nos centramos en explotar el uso de la informaci on contextual, especialmente los datos temporales y secuenciales, y aplicarla de forma novedosa tanto en la recomendaci on cl asica como en la recomendaci on de POIs. Creemos que este tipo de informaci on puede utilizarse no s olo para crear nuevos modelos de recomendaci on, sino tambi en para desarrollar nuevas m etricas para analizar la calidad de estas recomendaciones. En una de nuestras primeras contribuciones proponemos diferentes m etricas, algunas derivadas de formulaciones previamente existentes, utilizando esta informaci on contextual. Adem as, proponemos un algoritmo intuitivo que es capaz de proporcionar recomendaciones a un usuario objetivo explotando las ultimas interacciones comunes con otros usuarios similares del sistema. Al mismo tiempo, realizamos una revisi on exhaustiva de los algoritmos que se han propuesto en el a mbito de la recomendaci o n de POIs entre 2011 y 2019, identi cando las caracter sticas comunes y las metodolog as utilizadas. Una vez realizada esta clasi caci on de los algoritmos propuestos hasta la fecha, dise~namos un mecanismo para recomendar rutas completas (no s olo POIs independientes) a los usuarios, haciendo uso de t ecnicas de reranking. Adem as, debido a la gran di cultad de realizar recomendaciones en el ambito de los POIs, proponemos el uso de t ecnicas de agregaci on de datos para utilizar la informaci on de diferentes ciudades y generar recomendaciones de POIs en una determinada ciudad objetivo. En el trabajo experimental presentamos nuestros m etodos en diferentes conjuntos de datos tanto de recomendaci on cl asica como de POIs. Los resultados obtenidos en estos experimentos con rman la utilidad de nuestras propuestas de recomendaci on en t erminos de precisi on de ranking y de otras dimensiones como la novedad, la diversidad y la cobertura, y c omo de apropiadas son nuestras m etricas para analizar la informaci on temporal y los sesgos en las recomendaciones producida

From iterated revision to iterated contraction: extending the Harper Identity

The study of iterated belief change has principally focused on revision, with the other main operator of AGM belief change theory, namely contraction, receiving comparatively little attention. In this paper we show how principles of iterated revision can be carried over to iterated contraction by generalising a principle known as the ‘Harper Identity’. The Harper Identity provides a recipe for defining the belief set resulting from contraction by a sentence A in terms of (i) the initial belief set and (ii) the belief set resulting from revision by ¬A. Here, we look at ways to similarly define the conditional belief set resulting from contraction by A. After noting that the most straightforward proposal of this kind leads to triviality, we characterise a promising family of alternative suggestions that avoid such a result. One member of that family, which involves the operation of rational closure, is noted to be particularly theoretically fruitful and normatively appealing

Exploiting general-purpose background knowledge for automated schema matching

The schema matching task is an integral part of the data integration process. It is usually the first step in integrating data. Schema matching is typically very complex and time-consuming. It is, therefore, to the largest part, carried out by humans. One reason for the low amount of automation is the fact that schemas are often defined with deep background knowledge that is not itself present within the schemas. Overcoming the problem of missing background knowledge is a core challenge in automating the data integration process. In this dissertation, the task of matching semantic models, so-called ontologies, with the help of external background knowledge is investigated in-depth in Part I. Throughout this thesis, the focus lies on large, general-purpose resources since domain-specific resources are rarely available for most domains. Besides new knowledge resources, this thesis also explores new strategies to exploit such resources. A technical base for the development and comparison of matching systems is presented in Part II. The framework introduced here allows for simple and modularized matcher development (with background knowledge sources) and for extensive evaluations of matching systems. One of the largest structured sources for general-purpose background knowledge are knowledge graphs which have grown significantly in size in recent years. However, exploiting such graphs is not trivial. In Part III, knowledge graph em- beddings are explored, analyzed, and compared. Multiple improvements to existing approaches are presented. In Part IV, numerous concrete matching systems which exploit general-purpose background knowledge are presented. Furthermore, exploitation strategies and resources are analyzed and compared. This dissertation closes with a perspective on real-world applications

Tracking the Temporal-Evolution of Supernova Bubbles in Numerical Simulations

The study of low-dimensional, noisy manifolds embedded in a higher dimensional space has been extremely useful in many applications, from the chemical analysis of multi-phase flows to simulations of galactic mergers. Building a probabilistic model of the manifolds has helped in describing their essential properties and how they vary in space. However, when the manifold is evolving through time, a joint spatio-temporal modelling is needed, in order to fully comprehend its nature. We propose a first-order Markovian process that propagates the spatial probabilistic model of a manifold at fixed time, to its adjacent temporal stages. The proposed methodology is demonstrated using a particle simulation of an interacting dwarf galaxy to describe the evolution of a cavity generated by a Supernov

Actes des 29es Journées Francophones d'Ingénierie des Connaissances, IC 2018

International audienc

Answerers' Motivations and Strategies for Providing Information and Social Support in Social Q&A: An Investigation of Health Question Answering

Social Q&A allows people to ask and answer questions for each other and to solve problems in everyday life collaboratively. The purpose of the current study is to understand the motivations and strategies of answerers in social Q&A. Thus, three research questions were investigated: 1)Why do answerers participate and contribute in social Q&A? 2)What strategies do they use to provide effective answers in social Q&A? 3)What are the relationships between motivations and strategies? The domain of health is chosen because health is one of the most popular topics that people search information and support online. A model of answering behaviors has been proposed with a composition of 10 motivations and 32 strategies related to five steps of answering behaviors - question selection, question interpretation, information seeking, answer creation and answer evaluation. Two research methods - a survey and content analysis - were used. A survey questionnaire was distributed to top answerers and recent answerers in the health category of Yahoo! Answers. Answers of the survey participants were additionally collected in order to analyze the types of health messages and the sources of the answers. Altruism was found to be the most influential motivation, followed closely by Enjoyment and Efficacy. Answerers select questions based on their confidence or interest in the topic of the question. When interpreting questions, answerers believe that they understand the question most of the time. When seeking information for answers, most of the sources of answers are from the answerers' own information and experiences. When creating answers, accuracy and completeness are the most frequently used criteria for evaluating information sources. When evaluating answers, answerers review responses to their answers from questioners, other answerers, and other members in Yahoo! Answers. Additionally, motivations and strategies of all participants, top answerers, and health experts and the relationship between motivations and strategies are reported. Findings from the current study have practical implications for promoting the use of social Q&A as well as other similar Q&A services. The other important research implication is its contributions to the body of knowledge on information providing behaviors