Most of classical decision making processes aim at selecting the "best" alternative or at ranking alternatives based on the opinions of decision makers. Often, such a process occurs among people (experts or decision makers) who are expected to achieve some shared consensus in ranking the alternatives. However, this is not likely to happen (especially for a large and heterogeneous collection of people) and decision makers tend to reveal groups characteristics derived from their different opinions. A major problem is that inconsistency in opinions arises as each expert has a limited knowledge, errors and misinterpretation of data can occur and thus it is not clear how groups can be identified to be internally consistent and non-conflicting. In this paper, we investigate the conditions under which experts can be split into different sub-groups that share coherent and consistent opinions but are mutually in conflict in the ordering of the alternatives. We face this problem by presenting a non-linear integer programming model where each decision maker specifies incomplete preferences on pairs of alternatives and the objective is to obtain groups having the least possible degree of inconsistency. From a theoretical standpoint, we show that the proposed problem is non-convex and NP-Hard. Moreover, we validate the proposed approach with respect to a case study related to the 2018 Italian political elections. Specifically, we analyze the opinions of 33 decision makers and we show that the proposed technique is able to identify sub-groups characterized by large internal consistency, i.e., the members of each sub-groups express similar judgements upon the different options, while such options are evaluated very differently by the different sub-groups. Interestingly, while dividing the decision makers in three sub-groups, we obtain group rankings that reflect the structure of the Italian political parties or coalitions at the time, i.e., left-wing, right-wing and populists, even if such kind of information has not been directly provided by the decision makers nor used within the proposed case study.

Access to e-Government data is challenging due to the heterogeneity and complexity of the public information ecosystem. Controlled Vocabularies (CVs) provide a key to disclosing the potential of Open Government data, by supplying common terms for marking up metadata and data in a consistent and coherent way. However, quality information is needed to help public institutions decide whether to adopt an existing or newly created CV. The paper discusses how to evaluate and document CV quality thus facilitating a comparison of different controlled vocabularies based on contextual information. The Analytical Hierarchy Process (AHP) is adopted to assess the overall quality and rank of a controlled vocabulary, by integrating various quality dimensions according to the decision maker's needs. A set of e-Government controlled vocabularies that facilitate the semantic interoperability of e-Government data are selected as a testbed, and updated quality values are made available as Linked Data. Multi-step guidelines are also defined promoting and complementing the adoption of W3C recommendations to provide machine-readable quality metadata. This fosters reliability and re-usability by providing consumers with information on the assessment process carried out and the outcomes achieved. We illustrate the application of these guidelines by focusing on provenance and quality documentation.

Thesauri usability, within a Spatial Data Infrastructure for the Environment, is pivotal for metadata compilation and data discovery. Thesauri effectiveness is affected by their quality. Diverse quality measures are available taking into account different facets, nevertheless an overall measure is needed whenever thesauri have to be compared in order to identify those to be improved for a proper reuse. The paper proposes a methodology for the quality assessment of linked thesauri aimed at providing an overall quality ranking. It provides a proof of concept of the Analytic Hierarchy Process adoption to the set of linked data thesauri deployed in the Thesaurus Framework for the Environment (LusTRE) developed within the EU funded project eENVplus.

Recent studies report that Internet users are grow- ingly looking for health information through the Wikipedia Medicine Portal, a collaboratively edited multitude of articles with contents often comparable with professionally edited material. Automatic quality assessment of the Wikipedia medical articles has not received much attention by Academia and it presents open distinctive challenges. In this paper, we propose to tag the medical articles on the Wikipedia Medicine Portal, clearly stating their maturity degree, intended as a summarizing measure of several article properties. For this purpose, we adopt the Analytic Hierarchy Process, a well known methodology for decision making, and we evaluate the maturity degree of more than 24000 Wikipedia medical articles. The obtained results show how the qualitative analysis of medical content not always overlap with a quantitative analysis (an example of which is shown in the paper), since important properties of an article can hardly be synthesized by quantitative features. This seems particularly true when the analysis considers the concept of maturity, defined and verified in this work.

In recent years spatial analysis supported by Geographical Information Systems (GIS) combined with Analytic Hierarchy Process (AHP) has been used to support decision-making processes, especially in the field of land-use planning. The paper extends this integration in the perspective of "Integrated Assessments" in order to consider both the technical knowledge of the decision-making problem and the lay knowledge of the local community in the construction of shared planning choices. The integration of AHP and GIS is fruitful in land management where the role of local agents, their relations, objectives and values may be considered as structuring components for a spatial and dynamic evaluative model.