DOCSLIB.ORG

A Model-Driven Development Approach for Case Bases Engineering

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
A Model-Driven Development Approach for Case Bases Engineering A Model-Driven Development Approach for Case Bases Engineering Nikita O. Dorodnykh and Alexander Yu. Yurin Matrosov Institute for System Dynamics and Control Theory, Siberian Branch of the Russian Academy of Sciences (ISDCT SB RAS) Irkutsk, Russia [email protected], [email protected] Abstract—The paper discusses application of a model- domain knowledge into knowledge base structures, and driven development approach for engineering knowledge their specification in specialized editors for further execu- bases of case-based reasoning decision support systems. The tion. Model transformations [4] are implemented during conceptual models presented in XML-like formats are used as the initial data. The problem statement, main stages the import, for example, by using Transformation Model of the proposed approach and a tool are presented. An Representation Language (TMRL) [5]. illustrative example describes an educational task. This paper discusses an example of a such compromise Keywords—model-driven development, case-based rea- solution, in particular, we propose an approach and its soning, intelligent system, knowledge base, conceptual implementation on the basis of a Personal Knowledge model Base Designer (PKBD) platform [6]. Our proposals I. INTRODUCTION provide prototyping knowledge bases for case-based decision support systems. At that, concept maps and The knowledge bases engineering for intelligent sys- formalization from [7] are used as initial data. tems remains a time-consuming. This process requires the involvement highly qualified specialists in domain II. STATE-OF-ART areas (e.g., domain experts, analysts, programmers) and Let’s consider main concepts and definitions in the also the use of specialized software. In this case, the field of case-based reasoning and model-driven develop- development and use of software focused on non- ment (MDE/MDD) as a background for the research. programming users and implementing the principles of A. Case-Based Reasoning visual programming and automatic code generation are relevant. There are examples of such software: ExSys, Case-Based Reasoning (CBR) [8], [9] is a method- Visual Expert System Designer, and others. In most ology for decision making that reuses and adapts (if cases, these systems are focused on a certain formalism necessary) of the previously obtained solutions of similar of knowledge representation, in particular, logical rules. problems. This approach is based on the “by analogy” Software for conceptual modeling is another class of sys- principle of decision making. The main concept is a case. tems that uses visual programming for knowledge bases The case is a structured representation of accumulated engineering. Such systems provide the ability to create experience in the form of data and knowledge, providing models in the form of concept maps, mind maps, fuzzy its subsequent automated processing using specialized maps, entity-relationship diagrams, tree-like semantic software. structures and schemes (fault trees, event trees), IDEF The main case features are the following: A case represents special context-related knowledge or UML models. Most of these systems are universal • modeling software and don’t take into account features of that allows to use these knowledge at the application intelligent system engineering, i.e. formalisms, languages level. Cases can be represented by various forms: cov- and program platforms, which are used in this area. In • this connection, they provide a visual representation of ering different time periods; linking solutions with domain-specific knowledge structures, but don’t support problem descriptions; results with situations, etc. Case captures only the experience that can teach (to an adequate knowledge codification (formalization) for • knowledge representation languages. be useful), fixed cases can potentially help domain The use of methods and approaches that imple- expert (decision-maker) to achieve the goal, facil- ment the principles of a Model-Driven Engineering ap- itate its formulation in the future or warn him/hir proach (MDE) (also known a Model-Driven Develop- about possible failures or unforeseen problems. ment, MDD) [1], [2] is a compromise in this case. Such A case structures units of experience. At the same compromise solutions [3] provide import (analysis and time, the used structure is a problem-specific. In general, transformation) of conceptual models, which describing a case structure includes two main parts: 179 An identifying (characterizing) part describes the as a specialized domain-specific declarative language • experience by a way that provides to estimate the for describing transformations of conceptual models are possibility of its reuse in a particular situation. discussed in [5]. Application of MDD principles for en- A learning part describes a lesson (learning knowl- gineering knowledge bases of rule-based expert systems • edge, a solution) as a part of an experience unit, are described in [3], the formalization of MDD for CBR for example, a solution of a problem or its part, a is used from [7]. decision proof (conclusion), an alternative or failed decisions. III. PROTOTYPING CASE BASES USING CBR applied to solving problems in various subject TRANSFORMATION OF CONCEPTUAL MODELS domains, including planning [10], diagnostics [11] and A. Formalization others. There are some examples of CBR tools: CBR- Express, CasePoint, CasePower, Esteem, Expert Advi- Most of decision-making tasks can be described by a sor, ReMind, CBR/text, ReCall, RATE-CBR, S3-Case, set of characteristics, and can be formalized as follows: INRECA, and CASUEL. T ask M = p1, ..., pn , pi P rop, {M } ∈ (1) P rop = p , i = 1,N, B. Model-Driven Engineering i=1 i T ask S Model Driven Engineering (MDE) or Model-Driven where M is a task model; pi is task properties Development (MDD) is a software design approach (significant characteristics); P rop is a set of properties. that uses the information models as the major artifacts, The task model from a CBR point of view is defined which, in turn, can be used for obtaining other models as follows: and generating programming codes [2]. This approach M T askCBR : P roblemCBR DecisionCBR, (2) enables programmers and non-programmers (depending → on the implementation) to create software on the basis where M T askCBR is a task model in terms of of conceptual models. CBR; P roblemCBR is a task (problem) description; The main MDD concepts are the following: DecisionCBR is a problem decision, while: A model is an abstract description of a system (a • CBR process) by a formal language. As a rule, models are P roblem = c∗,C ,C = c1, ..., ck , c∗ / C, (3) h i { } ∈ visualized with the aid of certain graphic notations and serialized (represented) in XML. where c∗ is a new case; C is a case base. A metamodel is a model of a formal language used CBR • Decision = d1, ..., dr , { } (4) to create models (a model of models). di = (ci, si), ci C, si [0, 1], A four-layer metamodeling architecture is the con- ∈ ∈ • cept that defines the different layers of abstraction where DecisionCBR is a problem decision in the form (M0-M3), where the objects of reality are repre- of a set of retrieved cases with similarities si. sented at a lowest level (M0), then a level of models Let’s formalize a case description: (M1), a level of metamodels (M2) and a level of a P roblem Decision meta-metamodel (M3). ci = P ropi , P ropi , (5) A model transformation is the automatic generation n o • P roblem of a target model from a source model with the where P ropi is a identifying (characterizing) part Decision accordance of a set of transformation rules. In of a case; P ropi is a learning part of a case. In this case, each transformation rule describes the addition, each of these parts contains task properties: correspondence between the elements of a source P roblem P ropi = p1, ..., pm , and a target metamodels. Decision { } P ropi = pm+1, ..., pn , There are examples of a successful use of MDE P roblem { Decision } (6) P ropi P ropi = P rop, for development of database applications (e.g., ECO, P roblem ∪ Decision P ropi P ropi = ∅ for Enterprise Core Objects), agent-oriented monitoring ∩ applications [12], decision support systems [13], [14], The composition of the parts has a problem-specific embedded systems (software components) for the Inter- character. net [15], rule-based expert systems [16]. There are many methods that can be used for case retrieval [17] nearest neighbour, decision trees, etc. The C. Background most popular method is the nearest neighbour, based on Proposals of the current research are based on the an assessment of similarity with the aid of different met- previous works. In particular, development of case-based rics, for example, Euclidean, City-Block-Metric, etc. The expert systems and knowledge bases for petrochemistry proposed approach uses the nearest neighbour method are presented in [11]. Model transformations, as well and Zhuravlev metric [18] with normalisation: 180 N si(c∗, ci) = wj hi(pj∗, pij )/N, j=1 X 1, if p∗ pij < ξ for quantitative | j − | (0, else hi(pj∗, pij ) = 1, pj∗ = pij for qualitative 0, p∗ = pij ( j 6 (7) where wi is an information weight, and ξ is a constraint on the difference between the property values. At the Figure 1. The main stages of case bases and expert systems engineering same time, the normalisation (standardisation) is follows: and their relationships with MDE artifacts. pik = pik min xik / max pik min pik . (8) − k k − k MDE/MDD). The MDA assumes a clear definition of B. Methodology three levels (viewpoints) of the software representation: Generally, the methodology for expert systems en- A computation-independent level that describes the • gineering consists of the following main stages [19]: basic concepts and relationships of the subject do- analysis and identification, conceptualization (structur- main expressed in the form of conceptual models. ing), formalization (representation), implementation and Models of this level can be form automatically on testing.
Load more

Recommended publications
  • Towards the Implementation of an Intelligent Software Agent for the Elderly Amir Hossein Faghih Dinevari
    Towards the Implementation of an Intelligent Software Agent for the Elderly by Amir Hossein Faghih Dinevari A thesis submitted in partial fulfillment of the requirements for the degree of Master of Science Department of Computing Science University of Alberta c Amir Hossein Faghih Dinevari, 2017 Abstract With the growing population of the elderly and the decline of population growth rate, developed countries are facing problems in taking care of their elderly. One of the issues that is becoming more severe is the issue of compan- ionship for the aged people, particularly those who chose to live independently. In order to assist the elderly, we suggest the idea of a software conversational intelligent agent as a companion and assistant. In this work, we look into the different components that are necessary for creating a personal conversational agent. We have a preliminary implementa- tion of each component. Among them, we have a personalized knowledge base which is populated by the extracted information from the conversations be- tween the user and the agent. We believe that having a personalized knowledge base helps the agent in having better, more fluent and contextual conversa- tions. We created a prototype system and conducted a preliminary evaluation to assess by users conversations of an agent with and without a personalized knowledge base. ii Table of Contents 1 Introduction 1 1.1 Motivation . 1 1.1.1 Population Trends . 1 1.1.2 Living Options for the Elderly . 2 1.1.3 Companionship . 3 1.1.4 Current Technologies . 4 1.2 Proposed System . 5 1.2.1 Personal Assistant Functionalities .
    [Show full text]
  • Personal Knowledge Base Construction from Text-Based Lifelogs
    Session 2C: Knowledge and Entities SIGIR ’19, July 21–25, 2019, Paris, France Personal Knowledge Base Construction from Text-based Lifelogs An-Zi Yen1, Hen-Hsen Huang23, Hsin-Hsi Chen13 1Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan 2Department of Computer Science, National Chengchi University, Taipei, Taiwan 3MOST Joint Research Center for AI Technology and All Vista Healthcare, Taiwan [email protected], [email protected], [email protected] ABSTRACT Previous work on lifelogging focuses on life event extraction from 1. INTRODUCTION image, audio, and video data via wearable sensors. In contrast to Life event extraction from social media data provides wearing an extra camera to record daily life, people are used to complementary information for individuals. For example, in the log their life on social media platforms. In this paper, we aim to tweet (T1), there is a life event of the user who reads the book extract life events from textual data shared on Twitter and entitled “Miracles of the Namiya General Store” and enjoys it. The construct personal knowledge bases of individuals. The issues to enriched repository personal information is useful for memory be tackled include (1) not all text descriptions are related to life recall and supports living assistance. events, (2) life events in a text description can be expressed explicitly or implicitly, (3) the predicates in the implicit events are (T1) 東野圭吾的《解憂雜貨店》真好看 (Higashino Keigo's often absent, and (4) the mapping from natural language “Miracles of the Namiya General Store” is really nice.) predicates to knowledge base relations may be ambiguous.
    [Show full text]
  • Document Structure Aware Relational Graph Convolutional Networks for Ontology Population
    Document Structure aware Relational Graph Convolutional Networks for Ontology Population Abhay M Shalghar1?, Ayush Kumar1?, Balaji Ganesan2, Aswin Kannan2, and Shobha G1 1 RV College of Engineering, Bengaluru, India fabhayms.cs18,ayushkumar.cs18,[email protected] 2 IBM Research, Bengaluru, India fbganesa1,[email protected] Abstract. Ontologies comprising of concepts, their attributes, and re- lationships, form the quintessential backbone of many knowledge based AI systems. These systems manifest in the form of question-answering or dialogue in number of business analytics and master data manage- ment applications. While there have been efforts towards populating do- main specific ontologies, we examine the role of document structure in learning ontological relationships between concepts in any document cor- pus. Inspired by ideas from hypernym discovery and explainability, our method performs about 15 points more accurate than a stand-alone R- GCN model for this task. Keywords: Graph Neural Networks · Information Retrieval · Ontology Population 1 Introduction Ontology induction (creating an ontology) and ontology population (populating ontology with instances of concepts and relations) are important tasks in knowl- edge based AI systems. While the focus in recent years has shifted towards au- tomatic knowledge base population and individual tasks like entity recognition, entity classification, relation extraction among other things, there are infrequent advances in ontology related tasks. Ontologies are usually created manually and tend to be domain specific i.e. arXiv:2104.12950v1 [cs.AI] 27 Apr 2021 meant for a particular industry. For example, there are large standard ontologies like Snomed for healthcare, and FIBO for finance. However there are also require- ments that are common across different industries like data protection, privacy and AI fairness.
    [Show full text]
  • Knowledge Management in the Learning Society
    THE PRODUCTION, MEDIATION AND USE OF PROFESSIONAL KNOWLEDGE AMONG TEACHERS AND DOCTORS: A COMPARATIVE ANALYSIS by David H. Hargreaves School of Education, University of Cambridge, United Kingdom The most pressing need confronting the study of professions is for an adequate method of conceptualizing knowledge itself. Eliot Freidson, 1994 Introduction The are two grounds for undertaking a comparative analysis of the knowledge-base and associated processes of the medical and teaching professions. First, by examining the similarities and differences between the two professions, it should be possible to advance a generic model of a professional knowl- edge-base. Secondly, the contrast offers some indications of what each profession might learn from the other. In this chapter, doctors and teachers are compared in relation to their highly contrasting knowledge- bases. Whereas the knowledge-base of doctors is rooted in the biomedical sciences, teachers have no obvious equivalent, and the attempt to find one in the social sciences has so far largely failed. Yet both professions share a central core to their knowledge-base, namely the need to generate systems for clas- sifying the diagnoses of their clients’ problems and possible solutions to them. Contrasts are drawn between the styles of training for doctors and teachers, with doctors having retained some strengths from an apprenticeship model and teachers now developing more sophisticated systems for mentoring train- ees. In both professions, practice is often less grounded in evidence about effectiveness than is com- monly believed, but practitioner-led research and evidence-based medicine have put doctors far in advance of teachers. Adaptations of some developments in medicine could be used to improve the sys- temic capacities for producing and disseminating the knowledge which is needed for a better profes- sional knowledge-base for teachers.
    [Show full text]
  • Personal Knowledge Elaboration and Sharing: Presentation Is Knowledge Too
    Personal Knowledge Elaboration and Sharing: Presentation is Knowledge too Pierre-Antoine Champin, Yannick Pri´e,Bertrand Richard LIRIS UMR 5205 CNRS Universit´eClaude Bernard Lyon 1 F-69622, Villeurbanne, France fi[email protected] Abstract. In this paper, we propose to consider the construction, elab- oration and sharing of \personal knowledge", as it is developed during an activity and at the same time sustains that activity. We distinguish three poles in personal knowledge: specific, generic and presentation knowl- edge. The elaboration process consists in the building of knowledge of each kind, but also in the circulation of information across them. The sharing of personal knowledge can concern all of only some of the three poles. Although the third pole (presentation) is the less formal, we never- theless claim that it deserves to be considered equally to the two others in the process. We then illustrate our approach in different kinds of activity involving the creation of personal knowledge, its use and its sharing with others. We finally discuss the consequences and the potential benefits of our approaches in the more general domain of knowledge engineering. 1 Introduction As the digital world gets more and more connected, information gets both more fragmented and sharable, and computerized devices act more and more as mem- ories and supports to various activities. In that context, we are interested in the means of construction, use and sharing of so-called \personal knowledge": more or less structured data that results from the user's activity and at the same time sustains it. Any system that can be used to define, organize, visualize and share personal knowledge fits in that category.
    [Show full text]
  • Automated Extraction of Personal Knowledge from Smartphone Push Notifications
    TECHNICAL REPORT 1 Automated Extraction of Personal Knowledge from Smartphone Push Notifications Yuanchun Li, Ziyue Yang, Yao Guo, Xiangqun Chen, Yuvraj Agarwal, Jason I. Hong Abstract—Personalized services are in need of a rich and powerful personal knowledge base, i.e. a knowledge base containing information about the user. This paper proposes an approach to extracting personal knowledge from smartphone push notifications, which are used by mobile systems and apps to inform users of a rich range of information. Our solution is based on the insight that most notifications are formatted using templates, while knowledge entities can be usually found within the parameters to the templates. As defining all the notification templates and their semantic rules are impractical due to the huge number of notification templates used by potentially millions of apps, we propose an automated approach for personal knowledge extraction from push notifications. We first discover notification templates through pattern mining, then use machine learning to understand the template semantics. Based on the templates and their semantics, we are able to translate notification text into knowledge facts automatically. Users’ privacy is preserved as we only need to upload the templates to the server for model training, which do not contain any personal information. According to our experiments with about 120 million push notifications from 100,000 smartphone users, our system is able to extract personal knowledge accurately and efficiently. Index Terms—Personal data; knowledge base; knowledge extraction; push notifications; privacy F 1 INTRODUCTION USH notifications are widely used on mobile devices hotel reservation information from emails with markup [8].
    [Show full text]
  • A Knowledge Base for Personal Information Management
    A Knowledge Base for Personal Information Management David Montoya Thomas Pellissier Tanon Serge Abiteboul Square Sense LTCI, Télécom ParisTech Inria Paris [email protected] [email protected] & DI ENS, CNRS, PSL Research University [email protected] Pierre Senellart Fabian M. Suchanek DI ENS, CNRS, PSL Research University LTCI, Télécom ParisTech & Inria Paris [email protected] & LTCI, Télécom ParisTech [email protected] ABSTRACT the knowledge base, and integrated with other data. For example, Internet users have personal data spread over several devices and we have to find out that the same person appears with different across several web systems. In this paper, we introduce a novel email addresses in address books from different sources. Standard open-source framework for integrating the data of a user from KB alignment algorithms do not perform well in our scenario, as we different sources into a single knowledge base. Our framework show in our experiments. Furthermore, integration spans data of integrates data of different kinds into a coherent whole, starting different modalities: to create a coherent user experience, we need with email messages, calendar, contacts, and location history. We to align calendar events (temporal information) with the user’s show how event periods in the user’s location data can be detected location history (spatiotemporal) and place names (spatial). and how they can be aligned with events from the calendar. This We provide a fully functional and open-source personal knowl- allows users to query their personal information within and across edge management system. A first contribution of our work is the different dimensions, and to perform analytics over their emails, management of location data.
    [Show full text]
  • Referência Bibliográfica
    UNIVERSIDADE DE BRASÍLIA FACULDADE DE CIÊNCIA DA INFORMAÇÃO PROGRAMA DE PÓS-GRADUAÇÃO EM CIÊNCIA DA INFORMAÇÃO MESTRADO EM CIÊNCIA DA INFORMAÇÃO MARA CRISTINA SALLES CORREIA LEVANTAMENTO DAS NECESSIDADES E REQUISITOS BIBLIOGRÁFICOS DOS PESQUISADORES DA FACULDADE DE CIÊNCIA DA INFORMAÇÃO, COM VISTAS À ADOÇÃO DE UM APLICATIVO PARA A AUTOMAÇÃO DE REFERÊNCIAS BRASÍLIA 2010 MARA CRISTINA SALLES CORREIA LEVANTAMENTO DAS NECESSIDADES E REQUISITOS BIBLIOGRÁFICOS DOS PESQUISADORES DA FACULDADE DE CIÊNCIA DA INFORMAÇÃO, COM VISTAS À ADOÇÃO DE UM APLICATIVO PARA A AUTOMAÇÃO DE REFERÊNCIAS Dissertação apresentada à banca examinadora como requisito parcial à obtenção do título de Mestre em Ciência da Informação pelo Programa de Pós-Graduação em Ciência da Informação da Universidade de Brasília. Orientador: Drº Tarcisio Zandonade. BRASÍLIA 2010 Salles-Correia, Mara Cristina. Levantamento das necessidades e requisitos bibliográficos dos pesquisadores da Faculdade de Ciência da Informação, com vistas à adoção de um aplicativo para a automação de referências / Mara Cristina Salles Correia. – 2010. 250 f. : Il. color. ; 30 cm. Dissertação (Mestrado) – Universidade de Brasília, Faculdade de Ciência da Informação, 2010. Orientador: Prof. Dr. Tarcisio Zandonade. 1. Comunicação da Informação Científica. 2. Normalização. 3. Programa Gerenciador de Referências Bibliográficas (PGRB). 4. Referência bibliográfica. 5. Referenciação bibliográfica. 6. Zotero. I. Título. Ficha catalográfica elaborada por Tarcisio Zandonade. CRB-1: 24. Dedico esta pesquisa: A Deus, que em sua infinita misericórdia me deu livramento, permitindo que eu vivesse para concluir esta pesquisa e a convicção que Ele me dá de que GRANDES coisas estão por vir. Ao meu amado esposo, Ermano Júnior, por ser um canal de comunicação que Deus usou para me ensinar o valor real do amor e do perdão.
    [Show full text]
  • An Ontology-Based Knowledge Management System for the Metal Industry
    An Ontology-based Knowledge Management System for the Metal Industry Sheng-Tun Li Huang-Chih Hsieh I-Wei Sun Department of Information Department of Information Metal Industries Research & Management, National Management, National Development Center Kaohsiung First University of Kaohsiung First University of 1001 Kaonan Highway, Nantz Science Technology Science Technology District, Kaohsiung, 811, Taiwan, 2 Juoyue Rd. Nantz District, 2 Juoyue Rd. Nantz District, R.O.C. Kaohsiung 811, Taiwan, R.O.C. Kaohsiung 811, Taiwan, R.O.C. +886-7-3513121 ext 2360 +886-7-6011000 ext 4111 +886-7-6011000 ext 4111 [email protected] [email protected] [email protected] failure of a procedure. These measurements and computations ABSTRACT such as measuring material size, computing machine stress, and Determining definite and consistent measurement and estimating manufacture frequency are usually formalized as computation of materials and operations is of great importance for mathematic models. In reality, each engineer has his (her) own the operation procedures in metal industry. The effectiveness and favorite model in use, and models could be revised or adjusted to efficiency of such decisions are greatly dependent on the domain be more practical and precise according to different requests of understanding and empirical experience of engineers. The engineers whereas adopting appropriate revise or adjustment is complexity and diversity of domain knowledge and terminology is based on the tacit knowledge and empirical experience of the major hurdle for a successful operation procedure. engineers or the related knowledge documents they have on hand. Fortunately, such hurdles can be overcome by the emerging However, with the diversity and complexity of conceptual ontology technology.
    [Show full text]
  • Proceedings of the First Workshop on Semantic Wikis – from Wiki to Semantics
    Proceedings of the First Workshop on Semantic Wikis – From Wiki To Semantics edited by Max V¨olkel May 15, 2006 Proceedings of the First Workshop on Semantic Wikis - From Wiki to Semantics [SemWiki2006] - at the ESWC 2006 Preface Dear Reader, The community of Semantic Wiki researchers has probably first met at the dinner table of the Semantic Desktop Workshop, ISWC 2005 in Galway, Ireland. It was that very night, were the idea of the ”First Workshop on Semantic Wikis” and a mailing list were born. Since then, much has happened. The Topic of Semantic Wikis has evolved from a an obscure side-topic to one of interest for a broad community. Our mailing list1 has grown from twenty to over hundred subscribers. As the diversity of papers at this workshop shows, the field of Semantic Wiki research is quite diverse. We see papers on semantic wiki engines, a multitude of ways to combine wiki and semantic web ideas, and application of semantic wikis to bioscience, mathematics, e-learning, and multimedia. Semantic Wikis are currently explored from two sides: Wikis augmented with Seman- tic Web technology and Semantic Web applications being wiki-fied. In essence, wikis are portals with an editing component. Semantic Wikis can close the ”annotation bot- tleneck” of the Semantic Web – currently, we have many techniques and tools, but few data to apply them. We will change that. We wish to thank all authors that spend their nights contributing to this topic and thereby made the workshop possible. The high number of good submissions made the work for the programm committee members even more difficult – thank you all for your work.
    [Show full text]
  • A Personal Knowledge Base Integrating User Data and Activity Timeline David Montoya
    A personal knowledge base integrating user data and activity timeline David Montoya To cite this version: David Montoya. A personal knowledge base integrating user data and activity timeline. Data Struc- tures and Algorithms [cs.DS]. Université Paris-Saclay, 2017. English. NNT : 2017SACLN009. tel- 01715270 HAL Id: tel-01715270 https://tel.archives-ouvertes.fr/tel-01715270 Submitted on 28 Feb 2018 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. NNT : 2017SACLN009 Thèse de doctorat de l’Université Paris-Saclay préparée à l’École normale supérieure Paris-Saclay Ecole doctorale n◦580 Sciences et technologies de l’information et de la communication Spécialité de doctorat : Informatique par M. David Montoya Une base de connaissance personnelle intégrant les données d’un utilisateur et une chronologie de ses activités Thèse présentée et soutenue à Cachan, le 6 mars 2017. Composition du Jury : M. Serge Abiteboul Directeur de recherche (Directeur de thèse) Inria Paris M. Nicolas Anciaux Chargé de recherche (Examinateur) Inria Saclay Mme. Salima Benbernou Professeur (Président) Université Paris Descartes Mme. Angela Bonifati Professeur (Rapporteur) Université de Lyon M. Patrick Comont Directeur innovation et PI (Invité) Engie M. Pierre Senellart Professeur (Examinateur) École normale supérieure Mme.
    [Show full text]
  • Personal Knowledge Management: the Role of Web 2.0 Tools for Managing Knowledge at Individual and Organisational Levels
    Personal Knowledge Management: The role of Web 2.0 tools for managing knowledge at individual and organisational levels Liana Razmerita* Department of International Language Studies and Computational Linguistics Copenhagen Business School Frederiksberg, Denmark Kathrin Kirchner Department of Business Information Systems Friedrich-Schiller-University Jena, Germany Frantisek Sudzina Centre of Applied Information and Communication Technology Copenhagen Business School Frederiksberg, Denmark About the authors *Liana Razmerita, the corresponding author, is Assistant Professor at the Copenhagen Business School. She holds a doctorate in computer science from Université Paul Sabatier in Toulouse. Her doctoral research was done in the context of an Information Society Technology (IST), European Commission-funded project, at the Centre of Advanced Learning Technologies, INSEAD. Previous publications include more than 30 refereed journal articles, book chapters and conference papers in domains such as knowledge management, user modelling, e-learning and e-government. Email: [email protected] Kathrin Kirchner holds a post-doctoral position in the Department of Business Information Systems, Friedrich-Schiller-University, and works in the fields of data analysis, decision support and knowledge management. She completed her doctorate in 2006 at Friedrich- Schiller-University on spatial decision support systems for rehabilitating gas pipeline networks. Until 2001 she worked as a lecturer in computer science. Frantisek Sudzina finished his studies in economics and business management at the University of Economics Bratislava in 2000, in mathematics in 2004, and in computer science in 2006. He completed his doctorate in 2003 on the topic of management information systems and business competitiveness. Since 2007 he has been an assistant professor at Copenhagen Business School.
    [Show full text]