Knowledge formalization in experience feedback processes : an ontology-based approach

Because of the current trend of integration and interoperability of industrial systems, their size and complexity continue to grow making it more difficult to analyze, to understand and to solve the problems that happen in their organizations. Continuous improvement methodologies are powerful tools in order to understand and to solve problems, to control the effects of changes and finally to capitalize knowledge about changes and improvements. These tools involve suitably represent knowledge relating to the concerned system. Consequently, knowledge management (KM) is an increasingly important source of competitive advantage for organizations. Particularly, the capitalization and sharing of knowledge resulting from experience feedback are elements which play an essential role in the continuous improvement of industrial activities. In this paper, the contribution deals with semantic interoperability and relates to the structuring and the formalization of an experience feedback (EF) process aiming at transforming information or understanding gained by experience into explicit knowledge. The reuse of such knowledge has proved to have significant impact on achieving themissions of companies. However, the means of describing the knowledge objects of an experience generally remain informal. Based on an experience feedback process model and conceptual graphs, this paper takes domain ontology as a framework for the clarification of explicit knowledge and know-how, the aim of which is to get lessons learned descriptions that are significant, correct and applicable

Concept graphs: Applications to biomedical text categorization and concept extraction

As science advances, the underlying literature grows rapidly providing valuable knowledge mines for researchers and practitioners. The text content that makes up these knowledge collections is often unstructured and, thus, extracting relevant or novel information could be nontrivial and costly. In addition, human knowledge and expertise are being transformed into structured digital information in the form of vocabulary databases and ontologies. These knowledge bases hold substantial hierarchical and semantic relationships of common domain concepts. Consequently, automating learning tasks could be reinforced with those knowledge bases through constructing human-like representations of knowledge. This allows developing algorithms that simulate the human reasoning tasks of content perception, concept identification, and classification. This study explores the representation of text documents using concept graphs that are constructed with the help of a domain ontology. In particular, the target data sets are collections of biomedical text documents, and the domain ontology is a collection of predefined biomedical concepts and relationships among them. The proposed representation preserves those relationships and allows using the structural features of graphs in text mining and learning algorithms. Those features emphasize the significance of the underlying relationship information that exists in the text content behind the interrelated topics and concepts of a text document. The experiments presented in this study include text categorization and concept extraction applied on biomedical data sets. The experimental results demonstrate how the relationships extracted from text and captured in graph structures can be used to improve the performance of the aforementioned applications. The discussed techniques can be used in creating and maintaining digital libraries through enhancing indexing, retrieval, and management of documents as well as in a broad range of domain-specific applications such as drug discovery, hypothesis generation, and the analysis of molecular structures in chemoinformatics

An Automatic Intelligent System for Document Processing and Fruition

With the increasing amount of documents available on-line, the need for intelligent digital libraries, that allow to automatize the document processing tasks and to suitably organize and make available the documents so as to provide personalized and focused access, becomes more and more pressing. This paper proposes an integrated system that merges intelligent modules covering all the phases involved in a document lifecycle, from acquisition, to processing, to information extraction, to personalized fruition for final users. The role and possible cooperation of Machine Learning and Data Mining techniques in the system is highlighted and discussed, along with their importance to provide effective support to both the building and the fruition of the Digital Library and the underlying knowledge base

Continuous Improvement Through Knowledge-Guided Analysis in Experience Feedback

Continuous improvement in industrial processes is increasingly a key element of competitiveness for industrial systems. The management of experience feedback in this framework is designed to build, analyze and facilitate the knowledge sharing among problem solving practitioners of an organization in order to improve processes and products achievement. During Problem Solving Processes, the intellectual investment of experts is often considerable and the opportunities for expert knowledge exploitation are numerous: decision making, problem solving under uncertainty, and expert configuration. In this paper, our contribution relates to the structuring of a cognitive experience feedback framework, which allows a flexible exploitation of expert knowledge during Problem Solving Processes and a reuse such collected experience. To that purpose, the proposed approach uses the general principles of root cause analysis for identifying the root causes of problems or events, the conceptual graphs formalism for the semantic conceptualization of the domain vocabulary and the Transferable Belief Model for the fusion of information from different sources. The underlying formal reasoning mechanisms (logic-based semantics) in conceptual graphs enable intelligent information retrieval for the effective exploitation of lessons learned from past projects. An example will illustrate the application of the proposed approach of experience feedback processes formalization in the transport industry sector

Improving data management through automatic information extraction model in ontology for road asset management

lRoads are a critical component of transportation infrastructure, and their effective maintenance is paramount in ensuring their continued functionality and safety. This research proposes a novel information management approach based on state-of-the-art deep learning models and ontologies. The approach can automatically extract, integrate, complete, and search for project knowledge buried in unstructured text documents. The approach on the one hand facilitates implementation of modern management approaches, i.e., advanced working packaging to delivery success road management projects, on the other hand improves information management practices in the construction industry

Semantic Similarity of Spatial Scenes

The formalization of similarity in spatial information systems can unleash their functionality and contribute technology not only useful, but also desirable by broad groups of users. As a paradigm for information retrieval, similarity supersedes tedious querying techniques and unveils novel ways for user-system interaction by naturally supporting modalities such as speech and sketching. As a tool within the scope of a broader objective, it can facilitate such diverse tasks as data integration, landmark determination, and prediction making. This potential motivated the development of several similarity models within the geospatial and computer science communities. Despite the merit of these studies, their cognitive plausibility can be limited due to neglect of well-established psychological principles about properties and behaviors of similarity. Moreover, such approaches are typically guided by experience, intuition, and observation, thereby often relying on more narrow perspectives or restrictive assumptions that produce inflexible and incompatible measures. This thesis consolidates such fragmentary efforts and integrates them along with novel formalisms into a scalable, comprehensive, and cognitively-sensitive framework for similarity queries in spatial information systems. Three conceptually different similarity queries at the levels of attributes, objects, and scenes are distinguished. An analysis of the relationship between similarity and change provides a unifying basis for the approach and a theoretical foundation for measures satisfying important similarity properties such as asymmetry and context dependence. The classification of attributes into categories with common structural and cognitive characteristics drives the implementation of a small core of generic functions, able to perform any type of attribute value assessment. Appropriate techniques combine such atomic assessments to compute similarities at the object level and to handle more complex inquiries with multiple constraints. These techniques, along with a solid graph-theoretical methodology adapted to the particularities of the geospatial domain, provide the foundation for reasoning about scene similarity queries. Provisions are made so that all methods comply with major psychological findings about people’s perceptions of similarity. An experimental evaluation supplies the main result of this thesis, which separates psychological findings with a major impact on the results from those that can be safely incorporated into the framework through computationally simpler alternatives

Un environnement de spécification et de découverte pour la réutilisation des composants logiciels dans le développement des logiciels distribués

Notre travail vise à élaborer une solution efficace pour la découverte et la réutilisation des composants logiciels dans les environnements de développement existants et couramment utilisés. Nous proposons une ontologie pour décrire et découvrir des composants logiciels élémentaires. La description couvre à la fois les propriétés fonctionnelles et les propriétés non fonctionnelles des composants logiciels exprimées comme des paramètres de QoS. Notre processus de recherche est basé sur la fonction qui calcule la distance sémantique entre la signature d'un composant et la signature d'une requête donnée, réalisant ainsi une comparaison judicieuse. Nous employons également la notion de " subsumption " pour comparer l'entrée-sortie de la requête et des composants. Après sélection des composants adéquats, les propriétés non fonctionnelles sont employées comme un facteur distinctif pour raffiner le résultat de publication des composants résultats. Nous proposons une approche de découverte des composants composite si aucun composant élémentaire n'est trouvé, cette approche basée sur l'ontologie commune. Pour intégrer le composant résultat dans le projet en cours de développement, nous avons développé l'ontologie d'intégration et les deux services " input/output convertor " et " output Matching ".Our work aims to develop an effective solution for the discovery and the reuse of software components in existing and commonly used development environments. We propose an ontology for describing and discovering atomic software components. The description covers both the functional and non functional properties which are expressed as QoS parameters. Our search process is based on the function that calculates the semantic distance between the component interface signature and the signature of a given query, thus achieving an appropriate comparison. We also use the notion of "subsumption" to compare the input/output of the query and the components input/output. After selecting the appropriate components, the non-functional properties are used to refine the search result. We propose an approach for discovering composite components if any atomic component is found, this approach based on the shared ontology. To integrate the component results in the project under development, we developed the ontology integration and two services " input/output convertor " and " output Matching "

A Survey of Volunteered Open Geo-Knowledge Bases in the Semantic Web

Over the past decade, rapid advances in web technologies, coupled with innovative models of spatial data collection and consumption, have generated a robust growth in geo-referenced information, resulting in spatial information overload. Increasing 'geographic intelligence' in traditional text-based information retrieval has become a prominent approach to respond to this issue and to fulfill users' spatial information needs. Numerous efforts in the Semantic Geospatial Web, Volunteered Geographic Information (VGI), and the Linking Open Data initiative have converged in a constellation of open knowledge bases, freely available online. In this article, we survey these open knowledge bases, focusing on their geospatial dimension. Particular attention is devoted to the crucial issue of the quality of geo-knowledge bases, as well as of crowdsourced data. A new knowledge base, the OpenStreetMap Semantic Network, is outlined as our contribution to this area. Research directions in information integration and Geographic Information Retrieval (GIR) are then reviewed, with a critical discussion of their current limitations and future prospects

Conceptual Representations for Computational Concept Creation

Computational creativity seeks to understand computational mechanisms that can be characterized as creative. The creation of new concepts is a central challenge for any creative system. In this article, we outline different approaches to computational concept creation and then review conceptual representations relevant to concept creation, and therefore to computational creativity. The conceptual representations are organized in accordance with two important perspectives on the distinctions between them. One distinction is between symbolic, spatial and connectionist representations. The other is between descriptive and procedural representations. Additionally, conceptual representations used in particular creative domains, such as language, music, image and emotion, are reviewed separately. For every representation reviewed, we cover the inference it affords, the computational means of building it, and its application in concept creation.Peer reviewe