The Synonym management process in SAREL

The specification phase is one of the most important and least supported parts of the software development process. The SAREL system has been conceived as a knowledge-based tool to improve the specification phase. The purpose of SAREL (Assistance System for Writing Software Specifications in Natural Language) is to assist engineers in the creation of software specifications written in Natural Language (NL). These documents are divided into several parts. We can distinguish the Introduction and the Overall Description as parts that should be used in the Knowledge Base construction. The information contained in the Specific Requirements Section corresponds to the information represented in the Requirements Base. In order to obtain high-quality software requirements specification the writing norms that define the linguistic restrictions required and the software engineering constraints related to the quality factors have been taken into account. One of the controls performed is the lexical analysis that verifies the words belong to the application domain lexicon which consists of the Required and the Extended lexicon. In this sense a synonym management process is needed in order to get a quality software specification. The aim of this paper is to present the synonym management process performed during the Knowledge Base construction. Such process makes use of the Spanish Wordnet developed inside the Eurowordnet project. This process generates both the Required lexicon and the Extended lexicon that will be used during the Requirements Base construction.Postprint (published version

Business Domain Modelling using an Integrated Framework

This paper presents an application of a “Systematic Soft Domain Driven Design Framework” as a soft systems approach to domain-driven design of information systems development. The framework combining techniques from Soft Systems Methodology (SSM), the Unified Modelling Language (UML), and an implementation pattern known as “Naked Objects”. This framework have been used in action research projects that have involved the investigation and modelling of business processes using object-oriented domain models and the implementation of software systems based on those domain models. Within this framework, Soft Systems Methodology (SSM) is used as a guiding methodology to explore the problem situation and to develop the domain model using UML for the given business domain. The framework is proposed and evaluated in our previous works, and a real case study “Information Retrieval System for academic research” is used, in this paper, to show further practice and evaluation of the framework in different business domain. We argue that there are advantages from combining and using techniques from different methodologies in this way for business domain modelling. The framework is overviewed and justified as multimethodology using Mingers multimethodology ideas

Using NLP tools in the specification phase

The software quality control is one of the main topics in the Software Engineering area. To put the effort in the quality control during the specification phase leads us to detect possible mistakes in an early steps and, easily, to correct them before the design and implementation steps start. In this framework the goal of SAREL system, a knowledge-based system, is twofold. On one hand, to help software engineers in the creation of quality Software Requirements Specifications. On the other hand, to analyze the correspondence between two different conceptual representations associated with two different Software Requirements Specification documents. For the first goal, a set of NLP and Knowledge management tools is applied to obtain a conceptual representation that can be validated and managed by the software engineer. For the second goal we have established some correspondence measures in order to get a comparison between two conceptual representations. This information will be useful during the interaction.Postprint (published version

Semantic Concept Co-Occurrence Patterns for Image Annotation and Retrieval.

Describing visual image contents by semantic concepts is an effective and straightforward way to facilitate various high level applications. Inferring semantic concepts from low-level pictorial feature analysis is challenging due to the semantic gap problem, while manually labeling concepts is unwise because of a large number of images in both online and offline collections. In this paper, we present a novel approach to automatically generate intermediate image descriptors by exploiting concept co-occurrence patterns in the pre-labeled training set that renders it possible to depict complex scene images semantically. Our work is motivated by the fact that multiple concepts that frequently co-occur across images form patterns which could provide contextual cues for individual concept inference. We discover the co-occurrence patterns as hierarchical communities by graph modularity maximization in a network with nodes and edges representing concepts and co-occurrence relationships separately. A random walk process working on the inferred concept probabilities with the discovered co-occurrence patterns is applied to acquire the refined concept signature representation. Through experiments in automatic image annotation and semantic image retrieval on several challenging datasets, we demonstrate the effectiveness of the proposed concept co-occurrence patterns as well as the concept signature representation in comparison with state-of-the-art approaches

TagBook: A Semantic Video Representation without Supervision for Event Detection

We consider the problem of event detection in video for scenarios where only few, or even zero examples are available for training. For this challenging setting, the prevailing solutions in the literature rely on a semantic video representation obtained from thousands of pre-trained concept detectors. Different from existing work, we propose a new semantic video representation that is based on freely available social tagged videos only, without the need for training any intermediate concept detectors. We introduce a simple algorithm that propagates tags from a video's nearest neighbors, similar in spirit to the ones used for image retrieval, but redesign it for video event detection by including video source set refinement and varying the video tag assignment. We call our approach TagBook and study its construction, descriptiveness and detection performance on the TRECVID 2013 and 2014 multimedia event detection datasets and the Columbia Consumer Video dataset. Despite its simple nature, the proposed TagBook video representation is remarkably effective for few-example and zero-example event detection, even outperforming very recent state-of-the-art alternatives building on supervised representations.Comment: accepted for publication as a regular paper in the IEEE Transactions on Multimedi

Discovery-led refinement in e-discovery investigations: sensemaking, cognitive ergonomics and system design.

Given the very large numbers of documents involved in e-discovery investigations, lawyers face a considerable challenge of collaborative sensemaking. We report findings from three workplace studies which looked at different aspects of how this challenge was met. From a sociotechnical perspective, the studies aimed to understand how investigators collectively and individually worked with information to support sensemaking and decision making. Here, we focus on discovery-led refinement; specifically, how engaging with the materials of the investigations led to discoveries that supported refinement of the problems and new strategies for addressing them. These refinements were essential for tractability. We begin with observations which show how new lines of enquiry were recursively embedded. We then analyse the conceptual structure of a line of enquiry and consider how reflecting this in e-discovery support systems might support scalability and group collaboration. We then focus on the individual activity of manual document review where refinement corresponded with the inductive identification of classes of irrelevant and relevant documents within a collection. Our observations point to the effects of priming on dealing with these efficiently and to issues of cognitive ergonomics at the human–computer interface. We use these observations to introduce visualisations that might enable reviewers to deal with such refinements more efficiently