Taxonomy Induction using Hypernym Subsequences

We propose a novel, semi-supervised approach towards domain taxonomy induction from an input vocabulary of seed terms. Unlike all previous approaches, which typically extract direct hypernym edges for terms, our approach utilizes a novel probabilistic framework to extract hypernym subsequences. Taxonomy induction from extracted subsequences is cast as an instance of the minimumcost flow problem on a carefully designed directed graph. Through experiments, we demonstrate that our approach outperforms stateof- the-art taxonomy induction approaches across four languages. Importantly, we also show that our approach is robust to the presence of noise in the input vocabulary. To the best of our knowledge, no previous approaches have been empirically proven to manifest noise-robustness in the input vocabulary

Découverte interactive et complète de chroniques: application à la co-construction de connaissances à partir de traces

This thesis deals with the engineering of knowledge dynamics and it focuses on the interactive discovery of knowledge from activity traces. The applicative context targeted by this work is the management of the dynamic aspect of knowledge in Knowledge Management Systems (KMS). Two theoretical contributions are presented in this thesis. Firstly, we propose an iterative and interactive process for the co-construction of dynamic knowledge that requires a dialogue and a cooperation of the machine and humans. Secondly, we present an algorithm for the complete discovery of temporal patterns in sequences of events. This algorithm implements the machine proactive behaviour in this process.Interaction traces are information that users leave when they interact with their environment. This information about users' activities is collected, sometimes intentionally, by the designer of the environment. Interaction traces are represented in an expressive format designed especially for the engineering of interaction traces: the format of modelled traces. Such interaction traces are managed separately in a Trace-Based System (TBS), which can store modelled traces and provides primitive functions to access them. We argue that such interaction traces are potential containers of contextual knowledge about how users behave in their activities mediated by the traced environment. For this reason, interaction traces can be used for building systems that provide contextual assistance to users. We propose an iterative and interactive process for the co-construction of knowledge from traces. In this process, the machine analyses the traces and suggests some behaviour patterns to the human involved in the process. The human validates these patterns if he finds them relevant. If it is not the case, the human elaborates new requests and the machine suggests new candidate patterns, and so on. The idea behind this process was to build a bottom-up knowledge construction approach that takes into account the dynamic and contextual aspects of knowledge. The proactive participation of the machine to this co-construction process implies/requires the development of an algorithm that can extract temporal pattern from interaction traces, that is complete, and that can provide patterns to the human in real time, so that the knowledge co-construction process takes the form of a dialogue between the human and the machine.Chronicles are patterns that can occur in interaction traces and that contain temporal constraints with numerical bounds. The frequent chronicle mining approach we present in this thesis has been designed to implement the machine's behaviour in this process. This algorithm is the first algorithm for chronicle extraction from a sequence of events that is complete. It allows real time interactivity with its users by returning the partial result set of frequent chronicles, at any time. The algorithm supports temporal and structural user constraints pushing, which allows the human to make the chronicle exploration procedure converge more quickly towards the most interesting chronicles. The algorithm can be configured in a waythat makes it return the same non-complete chronicle result set as other existing algorithms in the literature. It can also be configured so as to return the complete frequent chronicle set, or to return the complete set of frequent hybrid episodes. Hybrid episodes are summarized forms of chronicles, with a simpler pattern structure that is easier to understand by humans. When compared to existing chronicle mining algorithms with the same conditions, our algorithm shows equivalent time performances. The main inconvenient of the chronicle discovery problem is that the size of the exploration space depends exponentially on the chronicle length. As a result, it is possible to discover only small chronicles in one shot, which implies the need for an iterative and incremental discovery approach.The platform Scheme Emerger has been developed for the purpose of this PhD project. It implements our complete chronicle discovery algorithm and it provides a graphical user interface for it. We use the platform Scheme Emerger to illustrate our knowledge co-construction process with interaction traces of the platform CollaborativeECM, which is the collaborative platform that has been developed within the project PROCOGEC.Cette thèse se situe dans le cadre de l'ingénierie de la dynamique des connaissances et s'intéresse plus particulièrement à la découverte interactive de connaissances dans les traces d'interactions. La gestion de la dynamique des connaissances liée à la mise en place d'un environnement de gestion de connaissances constitue le cadre applicatif principal du travail. Les contributions théoriques concernent d'une part la proposition d'un processus de co-construction de connaissances exploitant les capacités d'apprentissage automatique de la machine et les capacités d'interprétation de l'utilisateur et d'autre part une contribution algorithmique permettant d'exploiter de manière interactive un processus de découverte dans des séquences temporelles d'événements. Les traces d'interactions sont des informations que les utilisateurs d'un système informatique laissent lors de leurs activités. Ces informations sont collectées volontairement ou non par le concepteur du système. Lors de la collecte, elles sont représentées dans un format expressif dédié à l'ingénierie des traces, le format des traces modélisées, et sont accessibles par l'intermédiaire d'un système de gestion des traces (SBT) qui gère leur stockage. Nous argumentons que ces traces d'interactions sont des conteneurs de connaissances riches en informations contextuelles et qu'il est possible de les utiliser pour inférer des connaissances pertinentes sur l'activité tracée et exploitables par des systèmes d'assistance à l'utilisateur. Nous proposons un processus de co-construction de connaissances à partir de traces, qui est itératif et interactif. L'humain et la machine jouent tour à tour un rôle dans la construction des connaissances : la machine propose des motifs de comportement de l'utilisateur à partir des traces et l'humain valide ces motifs s'il les reconnaît et les juge intéressants. Dans le cas contraire, il formule de nouvelles requêtes à la machine qui lui propose alors de nouveaux motifs, et ainsi de suite. L'idée est d'implémenter un processus de construction de connaissances ascendant qui prenne en compte les aspects dynamique et contextuel de la connaissance. Pour que la machine puisse jouer un tel rôle pro-actif dans la construction, il faut concevoir un algorithme d'extraction de motifs temporels à partir de traces qui soit complet et qui permette de fournir des motifs en temps réel à l'humain, de sorte que le processus prenne la forme d'un dialogue avec la machine. Une chronique est une structure de motif spécifiant des contraintes temporelles numériques. L'algorithme d'extraction de chroniques fréquentes que nous présentons dans cette thèse pour implémenter ce processus est le premier algorithme d'extraction complète de chroniques à partir de séquences d'événements. Il permet l'interactivité en temps réel avec son utilisateur en affichant les résultats partiels de l'extraction à tout moment. L'algorithme supporte l'intégration de plusieurs types de contraintes temporelles et structurelles permettant à l'utilisateur de faire converger la découverte plus rapidement vers les chroniques d'intérêt. L'algorithme se comporte comme un framework dans la mesure où il peut être configuré pour agir comme les algorithmes d'extraction de chroniques non complets existants, pour découvrir l'ensemble véritablement complet des chroniques fréquentes, ou encore l'ensemble complet des épisodes hybrides fréquents, une certaine forme résumée et simplifiée des chroniques. Lorsqu'il est comparé aux algorithmes existants dans les mêmes conditions, notre algorithme montre des performances tout à fait comparables. L'inconvénient du problème de découverte de chroniques est que l'espace d'exploration s'agrandit exponentiellement avec la longueur des chroniques, si bien qu'il n'est possible de découvrir que des chroniques de faibles longueurs, introduisant la nécessité de réaliser la découverte de manière incrémentale. La plate-forme Scheme Emerger, développée dans le cadre de cette thèse, implémente cet algorithme et une interface graphique de pilotage. Scheme Emerger illustre le processus de co-construction de connaissances proposé sur des traces d'activités collaboratives collectées dans la plate-forme CollaborativeECM, développée dans le cadre du projet PROCOGEC

.

Cette thèse se situe dans le cadre de l'ingénierie de la dynamique des connaissances et s'intéresse plus particulièrement à la découverte interactive de connaissances dans les traces d'interactions. La gestion de la dynamique des connaissances liée à la mise en place d'un environnement de gestion de connaissances constitue le cadre applicatif principal du travail. Les contributions théoriques concernent d'une part la proposition d'un processus de co-construction de connaissances exploitant les capacités d'apprentissage automatique de la machine et les capacités d'interprétation de l'utilisateur et d'autre part une contribution algorithmique permettant d'exploiter de manière interactive un processus de découverte dans des séquences temporelles d'événements. Les traces d'interactions sont des informations que les utilisateurs d'un système informatique laissent lors de leurs activités. Ces informations sont collectées volontairement ou non par le concepteur du système. Lors de la collecte, elles sont représentées dans un format expressif dédié à l'ingénierie des traces, le format des traces modélisées, et sont accessibles par l'intermédiaire d'un système de gestion des traces (SBT) qui gère leur stockage. Nous argumentons que ces traces d'interactions sont des conteneurs de connaissances riches en informations contextuelles et qu'il est possible de les utiliser pour inférer des connaissances pertinentes sur l'activité tracée et exploitables par des systèmes d'assistance à l'utilisateur. Nous proposons un processus de co-construction de connaissances à partir de traces, qui est itératif et interactif. L'humain et la machine jouent tour à tour un rôle dans la construction des connaissances : la machine propose des motifs de comportement de l'utilisateur à partir des traces et l'humain valide ces motifs s'il les reconnaît et les juge intéressants. Dans le cas contraire, il formule de nouvelles requêtes à la machine qui lui propose alors de nouveaux motifs, et ainsi de suite. L'idée est d'implémenter un processus de construction de connaissances ascendant qui prenne en compte les aspects dynamique et contextuel de la connaissance. Pour que la machine puisse jouer un tel rôle pro-actif dans la construction, il faut concevoir un algorithme d'extraction de motifs temporels à partir de traces qui soit complet et qui permette de fournir des motifs en temps réel à l'humain, de sorte que le processus prenne la forme d'un dialogue avec la machine. Une chronique est une structure de motif spécifiant des contraintes temporelles numériques. L'algorithme d'extraction de chroniques fréquentes que nous présentons dans cette thèse pour implémenter ce processus est le premier algorithme d'extraction complète de chroniques à partir de séquences d'événements. Il permet l'interactivité en temps réel avec son utilisateur en affichant les résultats partiels de l'extraction à tout moment. L'algorithme supporte l'intégration de plusieurs types de contraintes temporelles et structurelles permettant à l'utilisateur de faire converger la découverte plus rapidement vers les chroniques d'intérêt. L'algorithme se comporte comme un framework dans la mesure où il peut être configuré pour agir comme les algorithmes d'extraction de chroniques non complets existants, pour découvrir l'ensemble véritablement complet des chroniques fréquentes, ou encore l'ensemble complet des épisodes hybrides fréquents, une certaine forme résumée et simplifiée des chroniques. Lorsqu'il est comparé aux algorithmes existants dans les mêmes conditions, notre algorithme montre des performances tout à fait comparables. L'inconvénient du problème de découverte de chroniques est que l'espace d'exploration s'agrandit exponentiellement avec la longueur des chroniques, si bien qu'il n'est possible de découvrir que des chroniques de faibles longueurs, introduisant la nécessité de réaliser la découverte de manière incrémentale [etc...]This thesis deals with the engineering of knowledge dynamics and it focuses on the interactive discovery of knowledge from activity traces. The applicative context targeted by this work is the management of the dynamic aspect of knowledge in Knowledge Management Systems (KMS). Two theoretical contributions are presented in this thesis. Firstly, we propose an iterative and interactive process for the co-construction of dynamic knowledge that requires a dialogue and a cooperation of the machine and humans. Secondly, we present an algorithm for the complete discovery of temporal patterns in sequences of events. This algorithm implements the machine proactive behaviour in this process. Interaction traces are information that users leave when they interact with their environment. This information about users' activities is collected, sometimes intentionally, by the designer of the environment. Interaction traces are represented in an expressive format designed especially for the engineering of interaction traces: the format of modelled traces. Such interaction traces are managed separately in a Trace-Based System (TBS), which can store modelled traces and provides primitive functions to access them. We argue that such interaction traces are potential containers of contextual knowledge about how users behave in their activities mediated by the traced environment. For this reason, interaction traces can be used for building systems that provide contextual assistance to users. We propose an iterative and interactive process for the co-construction of knowledge from traces. In this process, the machine analyses the traces and suggests some behaviour patterns to the human involved in the process. The human validates these patterns if he finds them relevant. If it is not the case, the human elaborates new requests and the machine suggests new candidate patterns, and so on. The idea behind this process was to build a bottom-up knowledge construction approach that takes into account the dynamic and contextual aspects of knowledge. The proactive participation of the machine to this co-construction process implies/requires the development of an algorithm that can extract temporal pattern from interaction traces, that is complete, and that can provide patterns to the human in real time, so that the knowledge co-construction process takes the form of a dialogue between the human and the machine. Chronicles are patterns that can occur in interaction traces and that contain temporal constraints with numerical bounds. The frequent chronicle mining approach we present in this thesis has been designed to implement the machine's behaviour in this process. This algorithm is the first algorithm for chronicle extraction from a sequence of events that is complete. It allows real time interactivity with its users by returning the partial result set of frequent chronicles, at any time. The algorithm supports temporal and structural user constraints pushing, which allows the human to make the chronicle exploration procedure converge more quickly towards the most interesting chronicles. The algorithm can be configured in a way that makes it return the same non-complete chronicle result set as other existing algorithms in the literature. It can also be configured so as to return the complete frequent chronicle set, or to return the complete set of frequent hybrid episodes. Hybrid episodes are summarized forms of chronicles, with a simpler pattern structure that is easier to understand by humans. When compared to existing chronicle mining algorithms with the same conditions, our algorithm shows equivalent time performances. The main inconvenient of the chronicle discovery problem is that the size of the exploration space depends exponentially on the chronicle length. As a result, it is possible to discover only small chronicles in one shot, which implies the need for an iterative and incremental discovery approach [etc...

Using patterns in object's memories to make plan-driven help systems more flexible

International audienceThis paper presents a method that makes use of temporal patterns, called memory chronicles, that can be found in ob jects’ memories to enhance any pervasive environment with the ability of task recognition. When a user interacts with this environment to achieve some goal ac- cording to a plan, this ability increases the real-time environment aware- ness of which user task in the plan is currently on-going. That up-to- date and contextual knowledge is very useful to bring pertinent help to the user and to make his activity easier. We define the underlying concepts under the approach and we illustrate it in the context of the smart kitchen, which aims at helping the cook in realizing recipes

A complete chronicle discovery approach: application to activity analysis

International audienceDiscovering temporal patterns hidden in a sequence of events has applications in numerous areas like network failure analysis, customer behaviour analysis, web navigation pattern discovery, etc. In this article, we present an approach to the discovery of chronicles hidden in the interaction traces of a human activity with the intention of characterizing some interesting tasks. Chronicles are a special type of temporal patterns, where temporal orders of events are quantified with numerical bounds. The algorithm we present is the first existing chronicle discovery algorithm that is complete. It is a chronicle discovery framework that can be configured to behave exactly as non-complete algorithms existing in litterature with no reduction of performance, but it can also be extended to other useful chronicle discovery problems like hybrid episode discovery. We show that the complete chronicle discovery problem has a very high complexity but we argue and illustrate that this high complexity is acceptable when the knowledge discovery process in which our algorithm takes part is real time and interactive. The platform Scheme Emerger, also presented in this paper, has been developed in order to implement the algorithm and to support graphically the real time and interactive chronicle discovery process

Using patterns in object's memories to make plan-driven help systems more flexible

International audienceThis paper presents a method that makes use of temporal patterns, called memory chronicles, that can be found in ob jects’ memories to enhance any pervasive environment with the ability of task recognition. When a user interacts with this environment to achieve some goal ac- cording to a plan, this ability increases the real-time environment aware- ness of which user task in the plan is currently on-going. That up-to- date and contextual knowledge is very useful to bring pertinent help to the user and to make his activity easier. We define the underlying concepts under the approach and we illustrate it in the context of the smart kitchen, which aims at helping the cook in realizing recipes

Visualizing Interaction Traces to improve Reflexivity in Synchronous Collaborative e-Learning Activities

International audienceEMediatheque is a collaborative, synchronous e-learning platform developed by eLycée S.A.S, providing various collaborative tools such as shared whiteboards, instant messaging, shared web browsing, synchronous multimedia viewing, etc., on top of a multi-party video-conferencing system and a highly configurable UI. This platform allows students and teachers to meet and communicate in virtual classrooms, and perform collaborative pedagogical activities involving the use of various sorts of resources (audio, video, Web, etc.) and document co-construction.The originality of eMediatheque is that it was designed to support the tracing of meaningful user interactions with the platform (actions perceivable by the user), allowing to build a rich and contextual usage history that we refer to as the primary trace. It also comprises a real-time trace visualization system that transforms and abstracts the primary trace (with respect to the ongoing activity) into a more understandable form to the user, in order to provide a reflexive perception of its own activity. These visualized traces support interactive parameterization (filtering, merging, etc.), may be shared and confronted, and provide shortcuts for generic operations (undo/redo, replay, etc).In showing its own trace to the user, we expect to provide a reflexive dimension to the activity in order to improve the pedagogical process. In this paper, we discuss practical issues of the use of traces in such a context: how to build a trace-enabled collaborative platform; how to set up interactive trace visualization. We also discuss the potential benefits regarding reflexivity and awareness, and the main difficulties in building up a visualization that makes sense for the student or the teacher in the context of his or her pedagogical activity.The discussion is illustrated by a concrete application: a simple activity where two students co-translate a cartoon and can use their traces to be aware of their past actions

How Document Pre-processing affects Keyphrase Extraction Performance

International audienceThe SemEval-2010 benchmark dataset has brought renewed attention to the task of automatic keyphrase extraction. This dataset is made up of scientific articles that were automatically converted from PDF format to plain text and thus require careful preprocessing so that irrevelant spans of text do not negatively affect keyphrase extraction performance. In previous work, a wide range of document preprocessing techniques were described but their impact on the overall performance of keyphrase extraction models is still unexplored. Here, we re-assess the performance of several keyphrase extraction models and measure their robustness against increasingly sophisticated levels of document preprocessing

Visualisation interactive de traces et réflexivité : application à l'EIAH collaboratif synchrone eMédiathèque

National audienceThis paper presents an interactive visualization tool for interaction traces, in the context of a synchronous collaborative e-learning activity. This tool has been developed by an e-learning company, eLycée, in collaboration with a research team working on e-learning, interaction traces, and experience reuse. This work adopts the grounding hypothesis that metacognitive processes and reflexive activities can facilitate learning. The paper presents the specific issues of such usage trace construction and visualization, the design of the hosting collaborative synchronous platform, and the associated tools and underlying models properties. This tool has so far not been experimented with students, but the technical tests with various researchers have been very promising and encountered a wide acceptance. The paper also points out the generic aspects of the tracing mechanisms, and the possibility for the student, teacher and designer to configure, update and extend it dynamically.Cet article présente un outil de visualisation interactive de traces d'interactions dans le cadre d'une activité d'apprentissage collaboratif synchrone. Cet outil a été développé en collaboration entre l'entreprise eLycée S.A.S., et une équipe de recherche travaillant sur l'ingénierie de l'expérience tracée et les EIAH. L'hypothèse de facilitation de la tâche d'apprentissage par les processus métacognitifs liés à une activité réflexive est à la base de la contribution. L'article est l'occasion de situer précisément les enjeux du travail engagé, de décrire l'environnement et les outils développés, et de présenter les propriétés des modèles sous-jacents. Bien que cet outil de visualisation de traces n'ait pas encore fait l'objet d'expérimentations, les tests techniques auprès d'un public varié ont rencontré une forte adhésion. L'article pointe les aspects génériques des mécanismes de traçage développés, en particulier les possibilités de faire évoluer dynamiquement l'environnement par l'utilisateur mais aussi par les concepteurs d'activités et les enseignants