Approches organisationnelles pour la conception de systèmes multi-agents dédiés à la gestion des connaissances; Application aux projets d'ingénierie et d'innovation Composition du jury

Approches organisationnelles pour la conception de systèmes multi-agents dédiés à la gestion des connaissances; Application aux projets d’ingénierie et d’innovatio

Results of multi-agent system and ontology to manage ideas and represent knowledge in a challenge of creativity

This article is about an intelligent system to support ideas management as a result of a multi-agent system used in a distributed system with heterogeneous information as ideas and knowledge, after the results about an ontology to describe the meaning of these ideas. The intelligent system assists participants of the creativity workshop to manage their ideas and consequently proposing an ontology dedicated to ideas. During the creative workshop many creative activities and collaborative creative methods are used by roles immersed in this creativity workshop event where they share knowledge. The collaboration of these roles is physically distant, their interactions might be synchrony or asynchrony, and the information of the ideas are heterogeneous, so we can say that the process is distributed. Those ideas are writing in natural language by participants which have a role and the ideas are heterogeneous since some of them are described by schema, text or scenario of use. This paper presents first, our MAS and second our Ontology design

A Semantic Approach to Manage Ideas in an Innovation Process

International audienceFor almost one decade, the organization " 48 hours generating ideas " (48H) makes every year a creativity workshop (CWS) to generate thousands of idea cards (IdC) by mean of creative methods with the purpose of solving industrial problems; inside of this organization, creativity depends on solver participants, creative experts, organizators, technical expert and industrial managers like Acar states in [1] " Organizational creativity is influenced by many thing. Some are social other are brought to the organization by individuals who comprise it. To certain degree, the organizational creativity depends on the individuals inside it " ; the huge quantity of these IdC's creates a problem " how to manage, to select and to compare these ideas that contain several and different fields " as Khemiri wrote in [2] " Text representations in a multimedia corpus with heterogeneous ideas " ; the 48H has several complexities such as people from different continents and cultures, but also, different industries and educative centers are participating during this event; We want to implement a semantic approach to manage ideas for the same industrial problem. This project started in september 2016, the proposal has five stages: to understand creative methods, to analyze a creativity workshop, to identify knowledge by mean of Organizational Model, to propose a Model to compare and evaluate ideas, and to develop an annotation system. We have observed that more than 1200 idea cards were generated during the last 48H creativity workshop. Each idea card describes an idea which is our main source of data. An example of an IdC: the 48H's idea card called Multifunction flying tractor (figure central), it is well structured according to nine components: Title of the idea (Text informative), Team, Description, Schema (draw),The topic (industrial problem), Priority clients, Scenario of use, Advantages, Risks, Competences; We will take in account the fields Title and Description. The proposal contain three principal steps: a) to design a model to highlights knowledge, b) to propose a semantic approach to annotate idea cards, and c) to implement an Intelligent system. The design of the model which highlights knowledge from the organizational creativity workshop 48H uses the meta-model KROM (Knowledge Reuse Organizational Meta-Model) by Girondon in [3] which facilitates the understanding of the collaboration among roles and highlight knowledge by collaborative activities; This organizational model has three domains: description of organizational structure (presented on the poster), description of expertise management, and description of knowledge management. The description of organizational structure shows missions, goals to identify the 48H and procedures inside the organization

Treat Different Negatives Differently: Enriching Loss Functions with Domain and Range Constraints for Link Prediction

Knowledge graph embedding models (KGEMs) are used for various tasks related to knowledge graphs (KGs), including link prediction. They are trained with loss functions that are computed considering a batch of scored triples and their corresponding labels. Traditional approaches consider the label of a triple to be either true or false. However, recent works suggest that all negative triples should not be valued equally. In line with this recent assumption, we posit that negative triples that are semantically valid w.r.t. domain and range constraints might be high-quality negative triples. As such, loss functions should treat them differently from semantically invalid negative ones. To this aim, we propose semantic-driven versions for the three main loss functions for link prediction. In an extensive and controlled experimental setting, we show that the proposed loss functions systematically provide satisfying results on three public benchmark KGs underpinned with different schemas, which demonstrates both the generality and superiority of our proposed approach. In fact, the proposed loss functions do (1) lead to better MRR and Hits@10 values, (2) drive KGEMs towards better semantic awareness as measured by the Sem@K metric. This highlights that semantic information globally improves KGEMs, and thus should be incorporated into loss functions. Domains and ranges of relations being largely available in schema-defined KGs, this makes our approach both beneficial and widely usable in practice

Multiple viewpoint modelling framework enabling integrated product-process design

Nowadays, companies have to cope with numerous constraints at organisational and technical levels in order to improve their competitiveness edges such as productivity, efficiency, and flexibility. Integrated product-process design becomes more and more complex to manage because of increasingly customized products related to various stakeholders and concerns geographically distributed. It is still represents a huge challenge, especially in the early phases of product development process. In such a context, the management of information within integrated product-process design highlights needs in a consistent engineering model that enables product lifecycle management (PLM) integration. The paper presents a novel multiple viewpoint framework called multiple viewpoint assembly oriented, considering product design and assembly process domains in the broader context of concurrent engineering and PLM. The proposed framework describes the consistency, the propagation of information change, and mechanisms of views generation among the product lifecycle stages in order to support assembly oriented design philosophy. A new modelling language called System Modeling Language is used to describe the proposed model from a systems engineering point of view. The implementation of the model in a Web-service called PEGASUS as an application for PLM systems is describe

Results of multi-agent system and ontology to manage ideas and represent knowledge in a challenge of creativity

International audienceThis article is about an intelligent system to support ideas management as a result of a multi-agent system used in a distributed system with heterogeneous information as ideas and knowledge, after the results about an ontology to describe the meaning of these ideas. The intelligent system assists participants of the creativity workshop to manage their ideas and consequently proposing an ontology dedicated to ideas. During the creative workshop many creative activities and collaborative creative methods are used by roles immersed in this creativity workshop event where they share knowledge. The collaboration of these roles is physically distant, their interactions might be synchrony or asynchrony, and the information of the ideas are heterogeneous, so we can say that the process is distributed. Those ideas are writing in natural language by participants which have a role and the ideas are heterogeneous since some of them are described by schema, text or scenario of use. This paper presents first, our MAS and second our Ontology design

Un système multi-agents pour la gestion des connaissances hétérogènes et distribuées

La gestion des connaissances permet d'identifier et de capitaliser les savoirs faires de l'entreprise afin de les organiser et de les diffuser. Cette thèse propose un système de gestion des connaissances hétérogènes et distribuées, appelé OCEAN. Basé sur les ontologies et sur un système multi-agents, OCEAN a pour but de résoudre le problème de la capitalisation et de réutilisation des connaissances provenant de plusieurs sources différentes, afin d aider les acteurs métiers dans le processus de développement de produits mécaniques. Le système OCEAN repose sur un cycle de vie de quatre étapes Ce cycle de vie possède les phases : d identification, d extraction, de validation et se termine par la réutilisation des connaissances. Chaque phase constitue l objectif d une organisation d agents.L identification dans le système OCEAN consiste à définir les connaissances par un expert métier sous la forme d une ontologie. Les ontologies sont utilisées dans notre système pour représenter les connaissances définis d une façon structurée et formelle afin d être compréhensible par les machines. L extraction des connaissances dans OCEAN est réalisée par les agents de manière automatique à l aide des ontologies créées par les experts métiers. Les agents interagissent avec les différentes applications métiers via des services web. Le résultat de cette phase est stocké dans une mémoire organisationnelle. La validation des connaissances consiste à permettre aux acteurs métiers de valider les connaissances de la mémoire organisationnelle dans un wiki sémantique. Ce wiki permet de présenter les connaissances de la mémoire organisationnelle aux acteurs pour les réutiliser, les évaluer et les faire évoluer. La réutilisation des connaissances dans OCEAN est inspiré de travaux antérieurs intégrés au sein d OCEAN. Les quatre phases du cycle de vie des connaissances traitées dans cette thèse nous ont permis de réaliser un système apte à gérer les connaissances hétérogènes et distribuées dans une entreprise étendue.Among the goals of Knowledge Management we can cite the identification and capitalization of the know-how of companies in order to organize and disseminate them. This thesis proposes a heterogeneous and distributed knowledge management system, called OCEAN. Based on ontologies and multi-agents system, OCEAN aims to solve the problem of capitalization and reuse of multi-sources knowledge in order to assist business actors in the development process of mechanical products. The OCEAN system is based on a knowledge life cycle composed by four steps. This knowledge life cycle begins with the identification then extraction, validation and finishes with knowledge reuse. Each step is the goal of an organization of agents.The identification in OCEAN system consists in the definition of knowledge by a business expert with an ontology. Ontologies are used in our system to represent the knowledge, defined by the business expert, in a structured and formal way in order to be understandable by machines. Agents according to the ontology defined by business experts realize knowledge extraction in OCEAN automatically. Agents interact with professional softwares via web services. The result of this extraction is stored in an organizational memory (OM). Validation of knowledge in OCEAN relies on business actors that validate the knowledge of the OM in a semantic wiki. This wiki allows also the presentation of this knowledge to business actors in order to reuse, evaluate or evolve it. Previous works, integrated within OCEAN, inspires the knowledge reuse step. The four steps lifecycle discussed in this thesis has enabled us to achieve a system that can manage heterogeneous and distributed knowledge in an extended enterprise.BELFORT-UTBM-SEVENANS (900942101) / SudocSudocFranceF

Schema First! Learn Versatile Knowledge Graph Embeddings by Capturing Semantics with MASCHInE

Knowledge graph embedding models (KGEMs) have gained considerable traction in recent years. These models learn a vector representation of knowledge graph entities and relations, a.k.a. knowledge graph embeddings (KGEs). Learning versatile KGEs is desirable as it makes them useful for a broad range of tasks. However, KGEMs are usually trained for a specific task, which makes their embeddings task-dependent. In parallel, the widespread assumption that KGEMs actually create a semantic representation of the underlying entities and relations (e.g., project similar entities closer than dissimilar ones) has been challenged. In this work, we design heuristics for generating protographs -- small, modified versions of a KG that leverage schema-based information. The learnt protograph-based embeddings are meant to encapsulate the semantics of a KG, and can be leveraged in learning KGEs that, in turn, also better capture semantics. Extensive experiments on various evaluation benchmarks demonstrate the soundness of this approach, which we call Modular and Agnostic SCHema-based Integration of protograph Embeddings (MASCHInE). In particular, MASCHInE helps produce more versatile KGEs that yield substantially better performance for entity clustering and node classification tasks. For link prediction, using MASCHInE has little impact on rank-based performance but increases the number of semantically valid predictions

Decision Support System for Online Recruitment

International audienceIn the past, potential candidates for a job offer were in physical locations that could be reached through the major media that were available at the time, often strongly rooted in their local geographic space. Today, digital media replaced those traditional channels, offering advertisers a broader geographic reach. However digital channels are more and more numerous, making it difficult to target candidates on the web. Existing decision support system on e-recruitment in the literature does not identify the desired profile from a job offer (C1), the relevance of a resume (C2) or the changing environment of recruitment (C3). Thereby, the objective of our research is to optimize the e-recruitment process by designing a decision support system capable of targeting potential candidates at a lower cost and that addresses the challenges (C1), (C2) and (C3)