A knowledge engineering framework for intelligent retrieval of legal case studies

International audienceJuris-Data is one of the largest case-study base in France. The case studies are indexed by legal classification elaborated by the Juris-Data Group. Knowledge engineering was used to design an intelligent interface for information retrieval based on this classification. The aim of the system is to help users find the case-study which is the most relevant to their own. The approach is potentially very useful, but for standardising it for other legal document bases, it is necessary to extract a legal classification of the primary documents. Thus, a methodology for the construction of these classifications was designed together with a framework for index construction. The project led to the implementation of a Legal Case Studie, based on the accumulated experimentation and the methodologies designed. It consists of a set of computerised tools which support the life-cycle of the legal document from their processing by legal experts to their consultation by clients

Intelligent support for inquiry learning from images: a learning scenario and tool

Inquiry learning involves the learner acquiring new concepts and skills by means of carrying out an investigation. Some previous studies have looked into how these learning activities can be carried out on source materials, such as web documents, and the provision of appropriate scaffolding to guide the learning process. Here we consider how intelligent support can be provided to guide the learner in analysing source materials and building knowledge from their interpretation. An important feature of our case study is that the source materials are images, potentially having greater variation in how they are interpreted and therefore increasing the need for intelligent support. Intelligent support provided by our system can identify patterns and inconsistencies in the learner’s interpretation and knowledge building, and offer recommendations based on comparison with a reference model. The recommendations derived from a reference model aim to guide the learner in reviewing and revising their interpretation of the images and the implications of these for their inferences, rather than directly providing a “right” or “wrong” evaluation of their answer

Improving Knowledge Retrieval in Digital Libraries Applying Intelligent Techniques

Nowadays an enormous quantity of heterogeneous and distributed information is stored in the digital University. Exploring online collections to find knowledge relevant to a user’s interests is a challenging work. The artificial intelligence and Semantic Web provide a common framework that allows knowledge to be shared and reused in an efficient way. In this work we propose a comprehensive approach for discovering E-learning objects in large digital collections based on analysis of recorded semantic metadata in those objects and the application of expert system technologies. We have used Case Based-Reasoning methodology to develop a prototype for supporting efficient retrieval knowledge from online repositories. We suggest a conceptual architecture for a semantic search engine. OntoUS is a collaborative effort that proposes a new form of interaction between users and digital libraries, where the latter are adapted to users and their surroundings

Generating indicative-informative summaries with SumUM

We present and evaluate SumUM, a text summarization system that takes a raw technical text as input and produces an indicative informative summary. The indicative part of the summary identifies the topics of the document, and the informative part elaborates on some of these topics according to the reader's interest. SumUM motivates the topics, describes entities, and defines concepts. It is a first step for exploring the issue of dynamic summarization. This is accomplished through a process of shallow syntactic and semantic analysis, concept identification, and text regeneration. Our method was developed through the study of a corpus of abstracts written by professional abstractors. Relying on human judgment, we have evaluated indicativeness, informativeness, and text acceptability of the automatic summaries. The results thus far indicate good performance when compared with other summarization technologies

Determination of the influence of specific building regulations in smart buildings

The automation of domestic services began to be implemented in buildings since the late nineteenth century, and today we are used to terms like ‘intelligent buildings’, ‘digital home’ or ‘domotic buildings’. These concepts tell us about constructions which integrate new technologies in order to improve comfort, optimize energy consumption or enhance the security of users. In conjunction, building regulations have been updated to suit the needs of society and to regulate these new facilities in such structures. However, we are not always sure about how far, from the quantitative or qualitative point of view, legislation should regulate certain aspects of the building activity. Consequently, content analysis is adopted in this research to determine the influence of building regulations in the implementation of new technologies in the construction process. This study includes the analysis of different European regulations, the collection and documentation of such guidelines that have been established and a study of the impact that all of these have had in the way we start thinking an architectural project. The achievements of the research could be explained in terms of the regulatory requirements that must be taken into account in order to achieve a successful implementation of a home automation system, and the key finding has been the confirmation of how the design of smart buildings may be promoted through specific regulatory requirements while other factors, such as the global economic situation, do not seem to affect directly the rate of penetration of home automation in construction

Working out a common task: design and evaluation of user-intelligent system collaboration

This paper describes the design and user evaluation of an intelligent user interface intended to mediate between users and an Adaptive Information Extraction (AIE) system. The design goal was to support a synergistic and cooperative work. Laboratory tests showed the approach was efficient and effective; focus groups were run to assess its ease of use. Logs, user satisfaction questionnaires, and interviews were exploited to investigate the interaction experience. We found that user’ attitude is mainly hierarchical with the user wishing to control and check the system’s initiatives. However when confidence in the system capabilities rises, a more cooperative interaction is adopted