Sylvie Calabretto

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.

ARCAMA-3D --- a context-aware augmented reality mobile platform for environmental discovery

In this paper, we present ARCAMA-3D, a platform for 3D map-based visualization on mobile devices powered by augmented reality. The platform offers context-aware interactions related to the concept of ubiquitous computing. The general purpose of the project is to enable users to navigate in an area with their mobile devices and interactively discover their surroundings. The system integrates real-time sensing technologies (GPS and other embedded sensors) and exploits users context and preferences in order to provide her with the necessary information. In return, the user consults the information (text, photo, audio or video files, etc.) that is published on the 3D model with the help of augmented reality. The innovative aspect of our approach lies in a light-weight 3D visualization system which is superimposed on the real scene. This approach facilitates the discovery of surroundings without preventing the visualization of real entities. We also alleviate the cognitive load of the user by avoiding the presentation of excessive information.

The Digital Library and Computational Philology: The BAMBI Project

The work presented in this paper has been developed within a European project called BAMBI. It enhances the accessibility of ancient manuscripts and presents new ways of working with them. More precisely, the BAMBI project aims to produce a software tool allowing historians, and more particularly codicologists and philologists, to read manuscripts, write annotations, and navigate between the words of the transcription and the matching piece of image in the digitized picture of the manuscript.

Multilingual extraction of semantic indexes

This article deals with multilingual document indexing. We propose an indexing method based on several stages. First of all the most important terms of the document are extracted using general characteristics of languages and statistical methods. Thus, term extraction stages can be applied to any document whatever the document language is. Secondly, our indexing method uses a multilingual ontology in order to find the most relevant concepts representing the document content. Our method can be applied to a multilingual corpus containing document written in different languages. This indexing procedure is part of a Multilingual Document System untitled SyDoM, which manages XML documents.

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.

Sequence Classification for Credit-Card Fraud Detection

Abstract Due to the growing volume of electronic payments, the monetary strain of credit-card fraud is turning into a substantial challenge for financial institutions and service providers, thus forcing them to continuously improve their fraud detection systems. However, modern data-driven and learning-based methods, despite their popularity in other domains, only slowly find their way into business applications. In this paper, we phrase the fraud detection problem as a sequence classification task and employ Long Short-Term Memory (LSTM) networks to incorporate transaction sequences. We also integrate state-of-the-art feature aggregation strategies and report our results by means of traditional retrieval metrics. A comparison to a baseline random forest (RF) classifier showed that the LSTM improves detection accuracy on offline transactions where the card-holder is physically present at a merchant. Both the sequential and non-sequential learning approaches benefit strongly from manual feature aggregation strategies. A subsequent analysis of true positives revealed that both approaches tend to detect different frauds, which suggests a combination of the two. We conclude our study with a discussion on both practical and scientific challenges that remain unsolved.

Feature vector construction combining structure and content for document classification

This paper describes a representation for XML documents in order to classify them. Document classification is based on document representation techniques. More relevant the representation phase is, more relevant the classification will be. We propose a representation model that exploits both the logical structure and the content of document. Structure is represented by the tags of XML document. Our approach is based on vector space model: a document is represented by a vector of weighted features. Each feature is a couple of (tag: term). We have modified the tf*idf formula to calculate features weight according to terms structural level in the document. SVM has been used as learning algorithm. Experimentation on Reuters collection shows that our proposition improves classification performance compared to the standard classification model based on term vector.

Ranking Entities in the Age of Two Webs, an Application to Semantic Snippets

The advances of the Linked Open Data LOD initiative are giving rise to a more structured Web of data. Indeed, a few datasets act as hubs e.g., DBpedia connecting many other datasets. They also made possible new Web services for entity detection inside plain text e.g.,i¾?DBpedia Spotlight, thus allowing for new applications that can benefit from a combination of the Web of documents and the Web of data. To ease the emergence of these new applications, we propose a query-biased algorithm LDRANK for the ranking of web of data resources with associated textual data. Our algorithm combines link analysis with dimensionality reduction. We use crowdsourcing for building a publicly available and reusable dataset for the evaluation of query-biased ranking of Web of data resources detected in Web pages. We show that, on this dataset, LDRANK outperforms the state of the art. Finally, we use this algorithm for the construction of semantic snippets of which we evaluate the usefulness with a crowdsourcing-based approach.

An architecture for surroundings discovery by linking 3D models and LOD cloud

The Linked Open Data (LOD) cloud contains vast spatio-temporal information that can be exploited by the location-based mobile applications and presented using Augmented Reality (AR). While AR shows to be well suited for searching and browsing location-based information, most approaches focus on domain specific scenarios and there is no generic data model for the information search and discovery that could be re-used in various applications. The geo-referenced data, however, is already available on the LOD cloud and can be exploited to provide location-based information to the user. With this approach, in this paper, we present a generic architecture for the surroundings discovery mobile applications. The 3D models that we publish on the LOD cloud represent the real world objects that belong to different temporal intervals. These geometric models allow the user to mentally construct the referential relationship between virtual and real-world objects and the mobile applciation developer to create experiences based on different concepts on the cloud. This way, the LOD cloud, which is a growing structured source of semantic data, becomes the main source of information for the architecture, and facilitates the knowledge discovery. We also extend our mobile location-based application, ARCAMA-3D (Augmented Reality for Context Aware Mobile Applications with 3D) that we have developed previously, by using this architecture.