Mercedes Garijo

One of the most promising fields for ambient intelligence is the implementation of intelligent emergency plans. Because the use of drills and living labs cannot reproduce social behaviors, such as panic attacks, that strongly affect these plans, the use of agent-based social simulation provides an approach to evaluate these plans more thoroughly. (1) The hypothesis presented in this paper is that there has been little interest in describing the key modules that these simulators must include, such as formally represented knowledge and a realistic simulated sensor model, and especially in providing researchers with tools to reuse, extend and interconnect modules from different works. This lack of interest hinders researchers from achieving a holistic framework for evaluating emergency plans and forces them to reconsider and to implement the same components from scratch over and over. In addition to supporting this hypothesis by considering over 150 simulators, this paper: (2) defines the main modules identified and proposes the use of semantic web technologies as a cornerstone for the aforementioned holistic framework; (3) provides a basic methodology to achieve the framework; (4) identifies the main challenges; and (5) presents an open and free software tool to hint at the potential of such a holistic view of emergency plan evaluation in indoor environments.

One of the challenges facing the current web is the efficient use of all the available information. The Web 2.0 phenomenon has favored the creation of contents by average users, and thus the amount of information that can be found for diverse topics has grown exponentially in the last years. Initiatives such as linked data are helping to build the Semantic Web, in which a set of standards are proposed for the exchange of data among heterogeneous systems. However, these standards are sometimes not used, and there are still plenty of websites that require naive techniques to discover their contents and services. This paper proposes an integrated framework for content and service discovery and extraction. The framework is divided into several layers where the discovery of contents and services is made in a representational stateless transfer system such as the web. It employs several web mining techniques as well as feature-oriented modeling for the discovery of cross-cutting features in web resources. The framework is used in a scenario of electronic newspapers. An intelligent agent crawls the web for related news, and uses services and visits links automatically according to its goal. This scenario illustrates how the discovery is made at different levels and how the use of semantics helps implement an agent that performs high-level tasks.

Information extraction out of web pages, commonly known as screen scraping, is usually performed through wrapper induction, a technique that is based on the internal structure of HTML documents. As such, the main limitation of these kinds of techniques is that a generated wrapper is only useful for the web page it was designed for. To overcome this, in this paper it is proposed a system that generates first-order logic rules that can be used to extract data from web pages. These rules are based on visual features such as font size, elements positioning or types of contents. Thus, they do not depend on a document's internal structure, and are able to work on different sites. The system has been validated on a set of different web pages, showing very high precision and good recall, which validates the robustness and the generalization capabilities of the approach.

In this paper we propose a conceptual semantic based tourism platform that enables users to consume mash up applications by using a multi agent system. We explore different mechanisms for discovery, publishing, composition and querying of services. In particular, we present a methodology for collecting, organizing and searching data distributed in different resources-RDF data sets- and - Application Programming Interface (API) -. As a part of our model, we propose a semantic architecture that defines three main components: i) A mechanism for automatic data collection. ii) A mechanism for automatic service composition. ii) A semantic recommendation module based on user context and social profile.