Building a territorial working group to reduce gender gap in the field of artificial intelligence

The gender gap (both at vocational and professional sides) in Artificial Intelligence (AI), and scientific and technological fields in general, is one of the most critical challenges that the current digital society must solve. This paper describes the proposal of the gender commission donesIAcat to create a gender working group formed by Catalan AI scientists and professionals who work in a network for bridging this gap. The main objectives for letting girls know that they can study and work in the AI field are presented in this paper. A general methodological framework is proposed, following the internal organization of the Catalan group donesIAcat. Several key actions are explained and classified into six blocks. A relevant contribution of the paper is the definition of the guidelines required to build a territorial network-based structure capable of launching several AI-related activities targeting people at different stages of their life. The activities done at donesIAcat illustrate the possible outcomes of the proposed methodology and show successful initiatives to engage girls in technology and AI. The paper shows the validity of this model for small homogeneous territories where activities can be suitable for the different cities in the region. Proximity is one of the advantages of such a model and one of the reasons for its success.Peer ReviewedPostprint (published version

Customization of an agent-based medical system

In this paper, the automatic customization of an agent-based medical system is approached by means of ontologies. The particular case of Home Care studied and developed in the EU K4Care project, is presented. The customization is achieved by means of generating individual versions of a reference ontology, called Actor Profile Ontology, which defines the behaviour of the actors in the multi-agent system. The paper, analyses the usability and advantages of this customization in order to add flexibility and adaptability to the system. It also shows how the personalized ontology is able to represent the liabilities and permissions of a particular user, providing the base for automatically generating the behaviour of the corresponding personal agent. A tool, called ATAPO, is also presented. It has been designed to assist the user in the personalization process. The way how this tool interacts with the system to permit the online modification of the behaviour of the agents is also discussed.Postprint (published version

Semantic similarity estimation from multiple ontologies

The version of record is available online at: http://dx.doi.org/10.1007/s10489-012-0355-yPeer ReviewedPostprint (author's final draft

Ontology-driven web-based semantic similarity

The version of record is available online at: http://dx.doi.org/10.1007/s10844-009-0103-xEstimation of the degree of semantic similarity/distance between concepts is a very common problem in research areas such as natural language processing, knowledge acquisition, information retrieval or data mining. In the past, many similarity measures have been proposed, exploiting explicit knowledge—such as the structure of a taxonomy—or implicit knowledge—such as information distribution. In the former case, taxonomies and/or ontologies are used to introduce additional semantics; in the latter case, frequencies of term appearances in a corpus are considered. Classical measures based on those premises suffer from some problems: in the first case, their excessive dependency of the taxonomical/ontological structure; in the second case, the lack of semantics of a pure statistical analysis of occurrences and/or the ambiguity of estimating concept statistical distribution from term appearances. Measures based on Information Content (IC) of taxonomical concepts combine both approaches. However, they heavily depend on a properly pre-tagged and disambiguated corpus according to the ontological entities in order to compute accurate concept appearance probabilities. This limits the applicability of those measures to other ontologies –like specific domain ontologies- and massive corpus –like the Web-. In this paper, several of the presented issues are analyzed. Modifications of classical similarity measures are also proposed. They are based on a contextualized and scalable version of IC computation in the Web by exploiting taxonomical knowledge. The goal is to avoid the measures’ dependency on the corpus pre-processing to achieve reliable results and minimize language ambiguity. Our proposals are able to outperform classical approaches when using the Web for estimating concept probabilities.Peer ReviewedPostprint (author's final draft

Introducing semantic variables in mixed distance measures: Impact on hierarchical clustering

Today, it is well known that taking into account the semantic information available for categorical variables sensibly improves the meaningfulness of the final results of any analysis. The paper presents a generalization of mixed Gibert's metrics, which originally handled numerical and categorical variables, to include also semantic variables. Semantic variables are defined as categorical variables related to a reference ontology (ontologies are formal structures to model semantic relationships between the concepts of a certain domain). The superconcept-based distance (SCD) is introduced to compare semantic variables taking into account the information provided by the reference ontology. A benchmark shows the good performance of SCD with respect to other proposals, taken from the literature, to compare semantic features. Mixed Gibert's metrics is generalized incorporating SCD. Finally, two real applications based on touristic data show the impact of the generalized Gibert's metrics in clustering procedures and, in consequence, the impact of taking into account the reference ontology in clustering. The main conclusion is that the reference ontology, when available, can sensibly improve the meaningfulness of the final clusters.Peer ReviewedPostprint (published version

Suunnitteluohjeiston kehittäminen

In this work we present the general classifier system Sedàs. We show how this system implements the description of the domain and how it builds similarity matrices and classification trees. The system uses a new semantics, introduced in [Torra96], to define a distance between qualitative values

Finding the most sustainable wind farm sites with a hierarchical outranking decision aiding method

The final publication is available at Springer via http://dx.doi.org/10.1007/s10479-017-2590-4This paper considers the problem of finding suitable sites for wind farms in a region of Catalonia (Spain). The evaluation criteria are structured into a hierarchy that identifies several intermediate sub-goals dealing with different points of view. Therefore, the recent ELECTRE-III-H hierarchical multi-criteria analysis method is proposed as a good solution to help decision-makers. This method establishes an order among the set of possible sites for the wind farms for each sub-goal. ELECTRE-III-H aggregates these orders into an overall order using different parameters. The procedure is based on the construction and exploitation of a pairwise outranking relation, following the principles of concordance (i.e. majority rule) and discordance (i.e. respect for the minority opinions). This paper makes two main contributions. First, it contributes to the ELECTRE-III-H method by studying its mathematical properties for the construction of outranking relations. Second, the case study is solved and its results show that we can effectively represent and manage the overall influence of the various criteria on the global result at different levels of the hierarchy. The paper compares different scenarios with strict, normal, and optimistic preference, indifference and veto thresholds. Results show that the best site differs for technical, economic, environmental, and social intermediate criteria. Therefore, the best overall solution changes depending on the preference and veto thresholds fixed at the intermediate level of the hierarchy.Peer ReviewedPostprint (author's final draft

Knowledge-driven delivery of home care services

The version of record is available online at: http://dx.doi.org/10.1007/s10844-010-0145-0Home Care (HC) assistance is emerging as an effective and efficient alternative to institutionalized care, especially for the case of senior patients that present multiple co-morbidities and require life long treatments under continuous supervision. The care of such patients requires the definition of specially tailored treatments and their delivery involves the coordination of a team of professionals from different institutions, requiring the management of many kinds of knowledge (medical, organizational, social and procedural). The K4Care project aims to assist the HC of elderly patients by proposing a standard HC model and implementing it in a knowledge-driven e-health platform aimed to support the provision of HC services.Peer ReviewedPostprint (author's final draft

2nd URV Doctoral Workshop in Computer Science and Mathematics

This proceeding book contains the contributions presented at the 2nd URV Doctoral workshop in Computer Science and Mathematics. The main aim of this workshop is to promote the dissemination of the ideas, methods and results that are developed by the students of our PhD program

1st URV Doctoral Workshop in Computer Science and Mathematics

This proceeding book contains the contributions presented at the 1st URV Doctoral workshop in Computer Science and Mathematics. The main aim of this workshop is to promote the dissemination of the ideas, methods and results that are developed by the students of our PhD program