Jean-Marie Pinon

A semantic approach to life cycle assessment applied on energy environmental impact data management

Environmental impact assessment of goods and services is nowadays a major challenge for both economic and ethical reasons. Life Cycle Assessment (LCA) provides a well-accepted methodology for modelling environmental impacts of human activities. One stage of the LCA methodology is Life Cycle Inventory, which consists of decomposing economic activities as elementary processes linked together through interdependency relations. This stage is needed in order to evaluate the environmental impacts of processes. A global analysis of economic activities requires considering a huge amount of elementary processes and interdependency links, making the model difficult to understand. In this paper, we propose a semantic approach for the modelling of lice cycle inventory databases. The method has the advantage of offering a more comprehensible model. We explain our model and illustrate it with life cycle inventory data for the U. S. electricity production.

Semantic structuring of documents

This paper presents a formal model for an explicit description of the semantic structure which implicitly exists with documents. This model relies on content meaning description of document elements. Meaning representation is distributed in the overall architecture model: it binds a semantic structure, a logical structure of documents and a domain model. The semantic structure contains two levels of description: meaning representation of information units. The description logic formalism is used to represent semantics of document elements and document rhetorical organisation. This paper shows how semantic structuring of documents can be efficiently defined using SGML syntax. Using this documents structuring norm, one can define two levels of description: generic semantic structure (vs. Document Type Definition) and specific semantic structure (vs. instantiated document) in order to define an abstract interface to the information stored in documents. The medical patient record has been presented as a relevant example for handling semantic structured documents.

An efficient collaborative information retrieval system by incorporating the user profile

As the volume of information augments, the importance of the Information Retrieval (IR) increases. Collaborative Information Retrieval (CIR) is one of the popular social-based IR approaches. A CIR system registers the previous user interactions to response to the subsequent user queries more efficiently. But the goals and the characteristics of two users may be different; so when they send the same query to a CIR system, they may be interested in two different lists of documents. In this paper we deal with the personalization problem in the CIR systems by constructing a profile for each user. We propose three new approaches to calculate the user profile similarity that we will employ in our personalized CIR algorithm.

Conceptual framework for document semantic modelling: an application to document and knowledge management in the legal domain

The main contribution of this paper is to lay down a conceptual framework for document semantics modeling. This framework provides a generic graphical knowledge representation model based on Sowas conceptual structures. Modeling primitives are introduced to represent factual and ontological knowledge that can be expressed in electronic documents. Binding features are proposed so as to keep knowledge representation and knowledge formulation linked together.This framework may be applied to various domains and may accept, for this purpose, many different ontological extensions. Thus an extension is provided so as to properly handle the particular kind of knowledge encountered in the legal domain.

Geo-based automatic image annotation

A huge number of user-tagged images are daily uploaded to the web. Recently, a growing number of those images are also geotagged. These provide new opportunities for solutions to automatically tag images so that efficient image management and retrieval can be achieved. In this paper an automatic image annotation approach is proposed. It is based on a statistical model that combines two different kinds of information: high level information represented by user tags of images captured in the same location as a new unlabeled image (input image); and low level information represented by the visual similarity between the input image and the collection of geographically similar images. To maximize the number of images that are visually similar to the input image, an iterative visual matching approach is proposed and evaluated. The results show that a significant recall improvement can be achieved with an increasing number of iterations. The quality of the recommended tags has also been evaluated and an overall good performance has been observed.

AI-STRATA: A User-Centered Model for Content-Based Description and Retrieval of Audiovisual Sequences

We first insist on the need for conceptual and knowledge-based audiovisual (AV) models in AV and multimedia information retrieval systems.We then propose several criteria for characterizing audio-visual representation approaches, and present a new approach for modeling and structuring AV documents with Annotations Interconnected Strata (AI-STRATA). This consists in analyzing AV documents through analysis dimensions allowing the detection of objects of interest of any type (structural, conceptual,... ). Annotations are structured by annotation elements (AE) representing both objects of interest and relationships. A knowledge base is used in order to monitor the annotation process. We show how to use annotations to link different strata on the base of explicit or implicit contexts and how AI-Strata can be used to build contextual views of a stratum, using both annotation and knowledge levels. We finally show how the model can efficiently support different description tasks such as indexing, searching and browsing audiovisual material.

Improving SURF Image Matching Using Supervised Learning

Key points-based image matching algorithms have proven very successful in recent years. However, their execution time makes them unsuitable for online applications. Indeed, identifying similar key points requires comparing a large number of high dimensional descriptor vectors. Previous work has shown that matching could be still accurately performed when only considering a few highly significant key points. In this paper, we investigate reducing the number of generated SURF features to speed up image matching while maintaining the matching recall at a high level. We propose a machine learning approach that uses a binary classifier to identify key points that are useful for the matching process. Furthermore, we compare the proposed approach to another method for key point pruning based on saliency maps. The two approaches are evaluated using ground truth datasets. The evaluation shows that the proposed classification-based approach outperforms the adversary in terms of the trade-off between the matching recall and the percentage of reduced key points. Additionally, the evaluation demonstrates the ability of the proposed approach of effectively reducing the matching runtime.

Tag Relatedness Using Laplacian Score Feature Selection and Adapted Jensen-Shannon Divergence

Folksonomies - networks of users, resources, and tags allow users to easily retrieve, organize and browse web contents. However, their advantages are still limited according to the noisiness of user provided tags. To overcome this problem, we propose an approach for identifying related tags in folksonomies. The approach uses tag co-occurrence statistics and Laplacian score feature selection to create probability distribution for each tag. Consequently, related tags are determined according to the distance between their distributions. In this regards, we propose a distance metric based on Jensen-Shannon Divergence. The new metric named AJSD deals with the noise in the measurements due to statistical fluctuations in tag co-occurrences. We experimentally evaluated our approach using WordNet and compared it to a common tag relatedness approach based on the cosine similarity. The results show the effectiveness of our approach and its advantage over the adversary method.

Modeling, encoding and querying multi-structured documents

The issue of multi-structured documents became prominent with the emergence of the digital Humanities field of practices. Many distinct structures may be defined simultaneously on the same original content for matching different documentary tasks. For example, a document may have both a structure for the logical organization of content (logical structure), and a structure expressing a set of content formatting rules (physical structure). In this paper, we present MSDM, a generic model for multi-structured documents, in which several important features are established. We also address the problem of efficiently encoding multi-structured documents by introducing MultiX, a new XML formalism based on the MSDM model. Finally, we propose a library of Xquery functions for querying MultiX documents. We will illustrate all the contributions with a use case based on a fragment of an old manuscript.

Porphyry 2001: Semantics for Scholarly Publications Retrieval

We describe the design and algorithms of Porphyry 2001, a scholarly publication retrieval system. This system is intended to meet library user studies which advocate human interpretation and social interactions. The metaphors we used are annotations and publication (making public). We first discuss about different philosophical approaches to semantics and choose the more suited to scholarly work: the one considering a transitory, hypothetical and polemical knowledge construction. Then we propose an overview of Porphyry 2001: an hypertext system based on a dynamic structure of user annotations. The visualization and evolution of the structure (a dynamic directed acyclic graph) is made more efficient by the use of an ad hoc browsing algorithm.