Automatic case acquisition from texts for process-oriented case-based reasoning

This paper introduces a method for the automatic acquisition of a rich case representation from free text for process-oriented case-based reasoning. Case engineering is among the most complicated and costly tasks in implementing a case-based reasoning system. This is especially so for process-oriented case-based reasoning, where more expressive case representations are generally used and, in our opinion, actually required for satisfactory case adaptation. In this context, the ability to acquire cases automatically from procedural texts is a major step forward in order to reason on processes. We therefore detail a methodology that makes case acquisition from processes described as free text possible, with special attention given to assembly instruction texts. This methodology extends the techniques we used to extract actions from cooking recipes. We argue that techniques taken from natural language processing are required for this task, and that they give satisfactory results. An evaluation based on our implemented prototype extracting workflows from recipe texts is provided.Comment: Sous presse, publication pr\'evue en 201

Construction of a taxonomy for requirements engineering commercial-off-the-shelf components

This article presents a procedure for constructing a taxonomy of COTS products in the field of Requirements Engineering (RE). The taxonomy and the obtained information reach transcendental benefits to the selection of systems and tools that aid to RE-related actors to simplify and facilitate their work. This taxonomy is performed by means of a goal-oriented methodology inspired in GBRAM (Goal-Based Requirements Analysis Method), called GBTCM (Goal-Based Taxonomy Construction Method), that provides a guide to analyze sources of information and modeling requirements and domains, as well as gathering and organizing the knowledge in any segment of the COTS market. GBTCM claims to promote the use of standards and the reuse of requirements in order to support different processes of selection and integration of components.Peer ReviewedPostprint (published version

Extracting, investigating and representing geographical concepts in Herodotus: the case of the Black Sea

In a short break from his preparations for the invasion of Scythia, Darius stops off where the Bosporus was bridged and sails to the Dark Rocks, apparently retracing the steps of the Argonauts.1 ‘There’, Herodotus reports, ‘he sat on the headland and viewed the Pontus, a wonderful sight’ (έζόμενος δέ έπί ρίω έθηεĩτο τόν Πόντον έόντα άξιοθέητον 4. 85. 1).2 In this paper, we aim to bring that wonderful sight to life using the latest digital technology, and to set out some of the ways in which the world that Herodotus describes can now be represented. At the same time, however, we will be concerned to show the potential of digital technologies for opening up new lines of enquiry, in particular the investigation of the ‘deep’ topological structures that underpin the Histories. After all, the Persian king is not the only figure to take an interest in the Pontus as a geographical concept: the historian too shows an interest in the Black Sea by extensively mapping the region and its place in the world, both before and after this episode (4. 37-45; 4. 99-101). The way that Herodotus articulates this space himself, which frames, and to a certain extent pre-empts, Darius’ invasion of Scythia, will be the concern of this paper

Towards the ontology-based approach for factual information matching

Factual information is information based on facts or relating to facts. The reliability of automatically extracted facts is the main problem of processing factual information. The fact retrieval system remains one of the most effective tools for identifying the information for decision-making. In this work, we explore how can natural language processing methods and problem domain ontology help to check contradictions and mismatches in facts automatically

Multimodal Visual Concept Learning with Weakly Supervised Techniques

Despite the availability of a huge amount of video data accompanied by descriptive texts, it is not always easy to exploit the information contained in natural language in order to automatically recognize video concepts. Towards this goal, in this paper we use textual cues as means of supervision, introducing two weakly supervised techniques that extend the Multiple Instance Learning (MIL) framework: the Fuzzy Sets Multiple Instance Learning (FSMIL) and the Probabilistic Labels Multiple Instance Learning (PLMIL). The former encodes the spatio-temporal imprecision of the linguistic descriptions with Fuzzy Sets, while the latter models different interpretations of each description's semantics with Probabilistic Labels, both formulated through a convex optimization algorithm. In addition, we provide a novel technique to extract weak labels in the presence of complex semantics, that consists of semantic similarity computations. We evaluate our methods on two distinct problems, namely face and action recognition, in the challenging and realistic setting of movies accompanied by their screenplays, contained in the COGNIMUSE database. We show that, on both tasks, our method considerably outperforms a state-of-the-art weakly supervised approach, as well as other baselines.Comment: CVPR 201