Adaptation Knowledge from the Case Base

Case adaptation continues to be one of the more difficult aspects of case-based reasoning to automate. This paper looks at several techniques for utilising the implicit knowledge contained in a case base for case adaptation in case-based reasoning systems. The most significant of the techniques proposed are a moderately successful data mining technique and a highly successful artificial neural network technique. Their effectiveness was evaluated on a footwear design problem

Гибридная интеллектуальная СППР для управления судном

В статье предложена структура и принцип работы интеллектуальной системы поддержки принятия решений по управлению судном в особых условиях плавания «БРИЗ», в основе которой лежат сценарно- прецедентные модели принятия решений. ИСППР представляет собой гибридную интеллектуальную систему, имеющую подсистемы рассуждений на основе прецедентов, на основе правил и на основе моделей.У статті запропоновано структуру і принцип роботи інтелектуальної системи підтримки прийняття рішень щодо керування судном в особливих умовах плавання «БРИЗ», в основі якої лежать сценарно- прецедентні моделі прийняття рішень. ІСППР є гібридною інтелектуальною системою, яка має під- системи міркувань на основі прецедентів, на основі правил і на основі моделей

Knowledge Organization and Terminology: application to Cork

This PhD thesis aims to prove the relevance of texts within the conceptual strand of terminological work. Our methodology serves to demonstrate how linguists can infer knowledge information from texts and subsequently systematise it, either through semiformal or formal representations. We mainly focus on the terminological analysis of specialised corpora resorting to semi-automatic tools for text analysis to systematise lexical-semantic relationships observed in specialised discourse context and subsequent modelling of the underlying conceptual system. The ultimate goal of this methodology is to propose a typology that can help lexicographers to write definitions. Based on the double dimension of Terminology, we hypothesise that text and logic modelling do not go hand in hand since the latter does not directly relate to the former. We highlight that knowledge and language are crucial for knowledge systematisation, albeit keeping in mind that they pertain to different levels of analysis, for they are not isomorphic. To meet our goals, we resorted to specialised texts produced within the industry of cork. These texts provide us with a test bed made of knowledge-rich data which enable us to demonstrate our deductive mechanisms employing the Aristotelian formula: X=Y+DC through the linguistic and conceptual analysis of the semi-automatically extracted textual data. To explore the corpus, we resorted to text mining strategies where regular expressions play a central role. The final goal of this study is to create a terminological resource for the cork industry, where two types of resources interlink, namely the CorkCorpus and the OntoCork. TermCork is a project that stems from the organisation of knowledge in the specialised field of cork. For that purpose, a terminological knowledge database is being developed to feed an e-dictionary. This e-dictionary is designed as a multilingual and multimodal product, where several resources, namely linguistic and conceptual ones are paired. OntoCork is a micro domain-ontology where the concepts are enriched with natural language definitions and complemented with images, either annotated with metainformation or enriched with hyperlinks to additional information, such as a lexicographic resource. This type of e-dictionary embodies what we consider a useful terminological tool in the current digital information society: accounting for its main features, along with an electronic format that can be integrated into the Semantic Web due to its interoperability data format. This aspect emphasises its contribution to reduce ambiguity as much as possible and to increase effective communication between experts of the domain, future experts, and language professionals.Cette thèse vise à prouver la pertinence des textes dans le volet conceptuel du travail terminologique. Notre méthodologie sert à démontrer comment les linguistes peuvent déduire des informations de connaissance à partir de textes et les systématiser par la suite, soit à travers des représentations semi-formelles ou formelles. Nous nous concentrons principalement sur l'analyse terminologique de corpus spécialisé faisant appel à des outils semi-automatiques d'analyse de texte pour systématiser les relations lexico-sémantiques observées dans un contexte de discours spécialisé et la modélisation ultérieure du système conceptuel sous-jacent. L’objectif de cette méthodologie est de proposer une typologie qui peut aider les lexicographes à rédiger des définitions. Sur la base de la double dimension de la terminologie, nous émettons l'hypothèse que la modélisation textuelle et logique ne va pas de pair puisque cette dernière n'est pas directement liée à la première. Nous soulignons que la connaissance et le langage sont essentiels pour la systématisation des connaissances, tout en gardant à l'esprit qu'ils appartiennent à différents niveaux d'analyse, car ils ne sont pas isomorphes. Pour atteindre nos objectifs, nous avons eu recours à des textes spécialisés produits dans l'industrie du liège. Ces textes nous fournissent un banc d'essai constitué de données riches en connaissances qui nous permettent de démontrer nos mécanismes déductifs utilisant la formule aristotélicienne : X = Y + DC à travers l'analyse linguistique et conceptuelle des données textuelles extraites semi-automatiquement. Pour l'exploitation du corpus, nous avons recours à des stratégies de text mining où les expressions régulières jouent un rôle central. Le but de cette étude est de créer une ressource terminologique pour l'industrie du liège, où deux types de ressources sont liés, à savoir le CorkCorpus et l'OntoCork. TermCork est un projet qui découle de l'organisation des connaissances dans le domaine spécialisé du liège. À cette fin, une base de données de connaissances terminologiques est en cours de développement pour alimenter un dictionnaire électronique. Cet edictionnaire est conçu comme un produit multilingue et multimodal, où plusieurs ressources, à savoir linguistiques et conceptuelles, sont jumelées. OntoCork est une micro-ontologie de domaine où les concepts sont enrichis de définitions de langage naturel et complétés par des images, annotées avec des méta-informations ou enrichies d'hyperliens vers des informations supplémentaires. Ce type de dictionnaire électronique désigne ce que nous considérons comme un outil terminologique utile dans la société de l'information numérique actuelle : la prise en compte de ses principales caractéristiques, ainsi qu'un format électronique qui peut être intégré dans le Web sémantique en raison de son format de données d'interopérabilité. Cet aspect met l'accent sur sa contribution à réduire autant que possible l'ambiguïté et à accroître l'efficacité de la communication entre les experts du domaine, les futurs experts et les professionnels de la langue

E-commerce website personalisation based on ontological profiling

Electronic commerce has become an important part of our consumer lives, and we increasingly choose to do more and more of our shopping online. Along with the growth of online sales, the number of e-commerce retailers has also increased. This has inevitably put additional demands on existing companies as well as new market entrants to ensure that their growth (if not just survival) as well as competitiveness are sustainable and evolving. Web personalisation has been adopted as a means to support business sustainability and competitiveness. It is now increasingly common and has been recognised by e-commerce businesses and consumers as a feature and functionality, expected to be offered as ‘standard’. Recent World Wide Web technology advances have greatly improved the way ecommerce websites are designed and deployed. However, the analysis of academic literature and professional practices shows that these advances are not used to their full potential. This research gap is an opportunity for this community to consider how techniques such as ontologies could be used to enhance personalisation of e-commerce websites. This thesis presents a novel approach to e-commerce website personalisation (PERSONTO), and in particular, personalisation of content presentation. Personalisation is achieved by means of an ontology-based e-shopper profiling. For this purpose, a reusable, extendible and Semantic Web compatible customer profiling ontology OntoProfi is designed and implemented. A ‘proof-of-concept’ prototype of PERSONTO confirmed the feasibility of the proposed approach. The analysis of achievements of the research objectives and outcomes showed that the approach is flexible, extendible and reusable, and that it was achieved by using systematic methods in the system design and implementation of the prototype. The evaluation of the acceptance of the proposed approach suggests there is a high level of acceptance of the approach by the prospective end users and e-commerce developers

HIDE: User centred Domotic evolution toward Ambient Intelligence

Pervasive Computing and Ambient Intelligence (AmI) visions are still far from being achieved, especially with regard to Domotics and home applications. According to the vision of Ambient Intelligence (AmI), the most advanced technologies are those that disappear: at maturity, computer technology should become invisible. All the objects surrounding us must possess sufficient computing capacity to interact with users, the surroundings and each other. The entire physical environment in which users are immersed should thus be a hidden computer system equipped with the appropriate software in order to exhibit intelligent behavior. Even though many implementations have started to appear in several contexts, few applications have been made available for the home environment and the general public. This is mainly due to the segmentation of standards and proprietary solutions, which are currently confusing the market with a sparse offer of uninteroperable devices and systems. Although modern houses are equipped with smart technological appliances, still very few of these appliances can be seamlessly connected to each other. The objective of this research work is to take steps in these directions by proposing, on the one hand, a software system designed to make today’s heterogeneous, mostly incompatible domotic systems fully interoperable and, on the other hand, a feasible software application able to learn the behavior and habits of home inhabitants in order to actively contribute to anticipating user needs, and preventing emergency situations for his health. By applying machine learning techniques, the system offers a complete, ready-to-use practical application that learns through interaction with the user in order to improve life quality in a technological living environment, such as a house, a smart city and so on. The proposed solution, besides making life more comfortable for users without particular needs, represents an opportunity to provide greater autonomy and safety to disabled and elderly occupants, especially the critically ill ones. The prototype has been developed and is currently running at the Pisa CNR laboratory, where a home environment has been faithfully recreated

Contribution to the elaboration of a decision support system based on modular ontologies for ecological labelling

With the rising concern of sustainability and environmental performance, eco-labeled products and services are becoming more and more popular. In addition to the financial costs, the long and complex process of eco-labeling sometimes demotivates manufacturers and service providers to be certificated. In this research work, we propose a decision support process and implement a decision support platform aiming at further improvement and acceleration of the eco-labeling process in order to democratize a broader application and certification of eco-labels. The decision support platform is based on a comprehensive knowledge base composed of various domain ontologies that are constructed according to official eco-label criteria documentation. Traditional knowledge base in relational data model is low interoperable, lack of inference support and difficult to be reused. In our research, the knowledge base composed of interconnected ontologies modules covers various products and services, and allows reasoning and semantic querying. A domain-centric modularization scheme about EU Eco-label laundry detergent product criteria is introduced as an application case. This modularization scheme separates the entity knowledge and rule knowledge so that the ontology modules can be reused easily in other domains. We explore a reasoning methodology based on inference with SWRL (Semantic Web Rule Language) rules which allows decision making with explanation. Through standard RDF (Resource Description Framework) and OWL (Web Ontology Language) ontology query interface, the assets of the decision support platform will stimulate domain knowledge sharing and can be applied into other application. In order to foster the reuse of ontology modules, we also proposed a usercentric approach for federate contextual ontologies (mapping and integration). This approach will create an ontology federation by a contextual configuration that avoid the “OWL:imports” disadvantages. Instead of putting mapping or new semantics in ontology modules, our approach will conserve the extra contextual information separately without impacting original ontologies or without importing all ontologies’ concepts. By introducing this contextualization, it becomes easier to support more expressive semantics in term of ontology integration itself, then it will also facilitate application agents to access and reuse ontologies. To realize this approach, we elaborate a new plug-in for the Protégé ontology editor