Improvements of Decision Support Systems for Public Administrations via a Mechanism of Co-creation of Value

This paper focuses on a possible improvement of knowledge-based decision support systems for human resource management within Public Administrations, using a co-creation of value's mechanism, according to the Service-Dominant Logic (SDL) paradigm. In particular, it applies ontology-driven data entry procedures to trigger the cooperation between the Public Administration itself and its employees. Advantages in such sense are evident: constraining the data entry process by means of the term definition ontology improves the quality of gathered data, thus reducing potential mismatching problems and allowing a suitable skill gap analysis among real and ideal workers competence profiles. The procedure foresees the following steps: analyzing organograms and job descriptions; modelling Knowledge, Skills and Attitudes (KSA) for job descriptions; transforming KSAs of job descriptions into a standard-based model with integrations of other characteristics; extracting information from Curricula Vitae according to the selected model; comparing profiles and roles played by the employees. The 'a priori' ontology-driven approach adequately supports the operations that involve both the Public Administration and employees, as for the data storage of job descriptions and curricula vitae. The comparison step is useful to understand if employees perform roles that are coherent with their own professional profiles. The proposed approach has been experimented on a small test case and the results show that its objective evaluation represents an improvement for a decision support system for the re-organization of Italian Public Administrations where, unfortunately often, people are engaged in activities that are not so close to their competences

Neural Networks forBuilding Semantic Models and Knowledge Graphs

1noL'abstract è presente nell'allegato / the abstract is in the attachmentopen677. INGEGNERIA INFORMATInoopenFutia, Giusepp

Big Data Analytics National Educational System Monitoring and Decision Making

This paper reviews the applications of big data in supporting monitoring and decision making in the National Educational System. It describes different types of monitoring methodologies and explores the opportunities, challenges and benefits of incorporating big data applications in order to study the National Educational System. This approach allows to analyze schools as entities, which included in a local context with specific social, economic, and cultural development features. In addition, the paper attempts to identify the prerequisites that support the implementation of data analysis in the national educational system. This review reveals that there are several opportunities for using big data (structured and unstructured information) in the educational system, in order to improve strategic multidimensional knowledge for decision making and developing educational policies; however, there are still many issues and challenges that need to be addressed so as to achieve a better use of this technology

A strategy to gradual implementation of data interoperability

Data interoperability is a major concern on e-government, both from the point of view of service offering and from the point of view of public administration efficiency. This paper purposes an incremental, pragmatic approach to data interoperability. It is argued that integration with minor required initial efforts from institutions is feasible, may provide useful solutions and is a solid ground basis for subsequent evolution. This paper presents general guidelines and model solutions to support this approach. Also, presents a demo implementation that proves feasibility of the purposed models and delivers useful solutions on a specific business e-government scenario. Although still limited in range and demonstrated on a quite specific business environment, it is expected that the analysis and the proposed strategies, solutions and models be of interest on a larger spectrum of data interoperability problems.info:eu-repo/semantics/acceptedVersio

WISM 2005 : web information systems modeling

Modern Web Information Systems (WIS) need to satisfy a large number of requirements coming from different WIS stakeholders. Modeling WIS by focusing at one design aspect at-a-time helps the implementation of these requirements. During the last years several model-driven methodologies have been proposed to support the WIS design. Strategic modeling is usually the first step in WIS design. It is a very general characterization of WIS which answers questions like: what is the purpose of the WIS?, which are the WIS users?, what functionality is provided by the WIS?, what is the content of the WIS?, what is the layout and atmosphere of the presentations provided by the WIS?, etc. It is only after answering the above questions at a high abstract level that the designer can proceed with the detailed specifications of the WIS. Data integration is one of the most important characteristics of WIS. Some examples of domains in which data integration is present are: public services and bioinformatics. WIS need to support user interfaces that make a lot of data coming from different sources available to the user in a transparent way. The Semantic Web technologies seem to facilitate the data integration problem on the Web by providing the necessary languages to describe the data semantics. Very often the Web user browses pages that he will like to view again at a later time. The present browsing history mechanisms included in Web browsers proved to be insufficient for an adequate retrieval of already seen information. A semantical organization of the previously visited pages can improve the process of retrieving previously seen data. There is an increasing demand to make WIS personalizable so that these systems better deal with the user interests. WIS design methodologies do propose adaptation techniques in order to realize WIS personalization. Despite the fact that some of these adaptation techniques are very similar (or even the same) in different methodologies, the notations to specify WIS personalization aspects are quite different. By defining a reference model for specifying WIS personalization one could improve the reuse of the personalization specifications and also enable a seamless translation between different specific personalization specifications. The above issues are some of the topics that are tackled in the workshop papers. We hope that we did raise the readers’ interest so that they will have a close look at the papers and possibly contribute to the fascinating and challenging area of WIS modeling

Ontologies in medicinal chemistry: current status and future challenges

[Abstract] Recent years have seen a dramatic increase in the amount and availability of data in the diverse areas of medicinal chemistry, making it possible to achieve significant advances in fields such as the design, synthesis and biological evaluation of compounds. However, with this data explosion, the storage, management and analysis of available data to extract relevant information has become even a more complex task that offers challenging research issues to Artificial Intelligence (AI) scientists. Ontologies have emerged in AI as a key tool to formally represent and semantically organize aspects of the real world. Beyond glossaries or thesauri, ontologies facilitate communication between experts and allow the application of computational techniques to extract useful information from available data. In medicinal chemistry, multiple ontologies have been developed during the last years which contain knowledge about chemical compounds and processes of synthesis of pharmaceutical products. This article reviews the principal standards and ontologies in medicinal chemistry, analyzes their main applications and suggests future directions.Instituto de Salud Carlos III; FIS-PI10/02180Programa Iberoamericano de Ciencia y Tecnología para el Desarrollo; 209RT0366Galicia. Consellería de Cultura, Educación e Ordenación Universitaria; CN2012/217Galicia. Consellería de Cultura, Educación e Ordenación Universitaria; CN2011/034Galicia. Consellería de Cultura, Educación e Ordenación Universitaria; CN2012/21

Transforming Points of Single Contact Data into Linked Data

Open data portals contain valuable information for citizens and business. However, searching for information can prove to be tiresome even in portals tackling domains similar information. A typical case is the information residing in the European Commission’s portals supported by Member States aiming to facilitate service provision activities for EU citizens and businesses. The current work followed the FAIR principles (Findability, Accessibility, Interoperability, and Reuse of digital assets) as well as the GO-FAIR principles and tried to transform raw data into fair data. The innovative part of this work is the mapping of information residing in various governmental portals (Points of Single Contacts) by transforming information appearing in them in RDF format (i.e., as Linked data), in order to make them easily accessible, exchangeable, interoperable and publishable as linked open data. Mapping was performed using the semantic model of a single portal, i.e., the enriched Greek e-GIF ontology and by retrieving and analyzing raw, i.e., non-FAIR data, by defining the semantic model and by making data linkable. The Data mapping process proved to require a significant manual effort and revealed that data value remains unexplored due to poor data representation. It also highlighted the need for appropriately designing and implementing horizontal actions addressing an important number of recipients in an interoperable way

Ontologies Supporting Intelligent Agent-Based Assistance

Intelligent agent-based assistants are systems that try to simplify peoples work based on computers. Recent research on intelligent assistance has presented significant results in several and different situations. Building such a system is a difficult task that requires expertise in numerous artificial intelligence and engineering disciplines. A key point in this kind of system is knowledge handling. The use of ontologies for representing domain knowledge and for supporting reasoning is becoming wide-spread in many areas, including intelligent assistance. In this paper we present how ontologies can be used to support intelligent assistance in a multi-agent system context. We show how ontologies may be spread over the multi-agent system architecture, highlighting their role controlling user interaction and service description. We present in detail an ontology-based conversational interface for personal assistants, showing how to design an ontology for semantic interpretation and how the interpretation process uses it for semantic analysis. We also present how ontologies are used to describe decentralized services based on a multi-agent architecture

Pattern-based design applied to cultural heritage knowledge graphs

Ontology Design Patterns (ODPs) have become an established and recognised practice for guaranteeing good quality ontology engineering. There are several ODP repositories where ODPs are shared as well as ontology design methodologies recommending their reuse. Performing rigorous testing is recommended as well for supporting ontology maintenance and validating the resulting resource against its motivating requirements. Nevertheless, it is less than straightforward to find guidelines on how to apply such methodologies for developing domain-specific knowledge graphs. ArCo is the knowledge graph of Italian Cultural Heritage and has been developed by using eXtreme Design (XD), an ODP- and test-driven methodology. During its development, XD has been adapted to the need of the CH domain e.g. gathering requirements from an open, diverse community of consumers, a new ODP has been defined and many have been specialised to address specific CH requirements. This paper presents ArCo and describes how to apply XD to the development and validation of a CH knowledge graph, also detailing the (intellectual) process implemented for matching the encountered modelling problems to ODPs. Relevant contributions also include a novel web tool for supporting unit-testing of knowledge graphs, a rigorous evaluation of ArCo, and a discussion of methodological lessons learned during ArCo development