Abstract Tenders are powerful means of investment of public funds and represent a strategic development resource. Despite the efforts made so far by governments at national and international levels to digitalise documents related to the Public Administration sector, most of the information is still available in an unstructured format only. With the aim of bridging this gap, we present OIE4PA, our latest study on extracting and classifying relations from tenders of the Public Administration. Our work focuses on the Italian language, where the availability of linguistic resources to perform Natural Language Processing tasks is considerably limited. Nevertheless, OIE4PA adopts a multilingual approach so it can be applied to several languages by providing appropriate training data. Rather than purely training a classifier on a portion of the extracted relations, the backbone idea of our learning strategy is to put a supervised method based on self-training to the proof and to assess whether or not it improves the performance of the classifier. For evaluation purposes, we built a dataset composed of 2,000 triples which have been manually annotated by two human experts. The in-vitro evaluation shows that OIE4PA achieves a MacroF $$_1$$ 1 equal to 0.89 and a 91 $$\%$$ % accuracy. In addition, OIE4PA was used as the pillar of a prototype search engine, which has been evaluated through an in-vivo experiment with positive feedback from 32 final users, obtaining a SUS score equal to 83.98.

Recommender systems have become ubiquitous in daily life, but their limitations in interacting with human users have become evident. Deep learning approaches have led to the development of data-driven algorithms that identify connections between users and items, but they often miss a critical actor in the loop - the end-user. Knowledge-based approaches are gaining attention due to the availability of knowledge-graphs, such as DBpedia and Wikidata, which provide semantics-aware information on different knowledge domains. These approaches are being used for recommendation and challenges such as knowledge graph embeddings, hybrid recommendation, and interpretable recommendation. Moreover, the emergence of neural-symbolic systems, which combine data-driven and symbolic methods, can significantly improve recommendation systems. A growing number of research papers on such topics demonstrate the growing interest and research potential of these systems. Furthermore, content features become crucial when interaction requires it. The development of conversational recommender systems presents new challenges, as they require multi-turn dialogues between users and systems, blurring the line between recommendation and retrieval. Evaluation of these systems goes beyond simple accuracy metrics and is hampered by the limited availability of datasets. While research and development into conversational recommender systems has been less prominent in the past, recent literature shows growing interest and potential for these systems.

Tenders are powerful means of investment of public funds and represent a strategic development resource. Thus, improving the efficiency of procuring entities and developing evaluation models turn out to be essential to facilitate e-procurement procedures. With this contribution, we introduce our research to create a supporting system for the decision-making and monitoring process during the entire course of investments and contracts. This system employs artificial intelligence techniques based on natural language processing, focused on providing instruments for extracting useful information from both structured and unstructured (i.e., text) data. Therefore, we developed a framework based on a web app that provides integrated tools such as a semantic search engine, a summarizer, an open information extraction engine in the form of triples (subject–predicate–object) for tender documents, and dashboards for analysing tender data.

Abstract The Italian Public Administration (PA) relies on costly manual analyses to ensure the GDPR compliance of public documents and secure personal data. Despite recent advances in Artificial Intelligence (AI) have benefited many legal fields, the automation of workflows for data protection of public documents is still only marginally affected. The main aim of this work is to design a framework that can be effectively adopted to check whether PA documents written in Italian meet the GDPR requirements. The main outcome of our interdisciplinary research is INTREPID (art ficial i elligence for gdp complianc of ublic adm nistration ocuments), an AI-based framework that can help the Italian PA to ensure GDPR compliance of public documents. INTREPID is realized by tuning some linguistic resources for Italian language processing (i.e. SpaCy and Tint) to the GDPR intelligence. In addition, we set the foundations for a text classification methodology to recognise the public documents published by the Italian PA, which perform data breaches. We show the effectiveness of the framework over a text corpus of public documents that were published online by the Italian PA. We also perform an inter-annotator study and analyse the agreement of the annotation predictions of the proposed methodology with the annotations by domain experts. Finally, we evaluate the accuracy of the proposed text classification model in detecting breaches of security.

In the last few years, a renewed interest of the research community in conversational recommender systems (CRSs) has been emerging. This is likely due to the massive proliferation of Digital Assistants (DAs) such as Amazon Alexa, Siri, or Google Assistant that are revolutionizing the way users interact with machines. DAs allow users to execute a wide range of actions through an interaction mostly based on natural language utterances. However, although DAs are able to complete tasks such as sending texts, making phone calls, or playing songs, they still remain at an early stage in terms of their recommendation capabilities via a conversation. In addition, we have been witnessing the advent of increasingly precise and powerful recommendation algorithms and techniques able to effectively assess users’ tastes and predict information that may be of interest to them. Most of these approaches rely on the collaborative paradigm (often exploiting machine learning techniques) and neglect the huge amount of knowledge, both structured and unstructured, describing the domain of interest of a recommendation engine. Although very effective in predicting relevant items, collaborative approaches miss some very interesting features that go beyond the accuracy of results and move in the direction of providing novel and diverse results as well as generating explanations for recommended items. Knowledge-aware side information becomes crucial when a conversational interaction is implemented, in particular for preference elicitation, explanation, and critiquing steps.

In the last few years, a renewed interest of the research community on conversational recommender systems (CRSs) is emerging. This is probably due to the great diffusion of Digital Assistants (DAs) such as Amazon Alexa, Siri, or Google Assistant that are revolutionizing the way users interact with machines. DAs allow users to execute a wide range of actions through an interaction mostly based on natural language messages. However, although DAs are able to complete tasks such as sending texts, making phone calls, or playing songs, they are still at an early stage on offering recommendation capabilities by using the conversational paradigm.

More and more precise and powerful recommendation algorithms and techniques have been proposed over the last years able to effectively assess users' tastes and predict information that would probably be of interest for them. Most of these approaches rely on the collaborative paradigm (often exploiting machine learning techniques) and do not take into account the huge amount of knowledge, both structured and non-structured ones, describing the domain of interest for the recommendation engine. The aim of knowledge-aware and conversational recommender systems is to go beyond the traditional accuracy goal and to start a new generation of algorithms and interactive approaches which exploit the knowledge encoded in ontological and logic-based knowledge bases, knowledge graphs as well as the semantics emerging from the analysis and exploitation of semi-structured textual sources.

This paper presents T-RecS (Temporal analysis of Recommender Systems conference proceedings), a framework that supplies services to analyze the Recommender Systems Conference proceedings from the first edition, held in 2007, to the last one, held in 2015, under a temporal point of view. The idea behind T-RecS is to identify linguistic phenomena that reflect some interesting variations for the research community, such as topic drift, or how the correlation between two terms changed over time, or how similarity between two authors evolved over time.

A number of works have shown that the aggregation of several information Retrieval (IR) systems works better than each system working individually. Nevertheless, early investigation in the context of CLEF Robust-WSD task, in which semantics is involved, showed that aggregation strategies achieve only slight improvements. This paper proposes a re-ranking approach which relies on inter-document similarities. The novelty of our idea is twofold: the output of a semantic based IR, system is exploited to re-weigh documents and a new strategy based on Semantic Vectors is used to compute inter-document similarities.

Content-based recommender systems try to recommend items similar to those a given user has liked in the past. The basic process consists of matching up the attributes of a user profile, in which preferences and interests are stored, with the attributes of a content object (item).

Recommender systems (RSs) proved to make easier the task of accessing relevant information in a broad range of domains. In content-based RSs, preferences on content items expressed by users turned out to be reliable indicators to suggest and filter interesting contents. Item representation plays a key role in content-based RSs, thus choosing proper facets to represent items is a fundamental task for deploying effective RSs. Contextual facets are often marginally relevant to predict user preferences, but in some domains disregarding contextual facets makes recommendations useless. This paper proposes a strategy to improve the effectiveness of a content-based RS that dynamically suggests tours within a museum by exploiting contextual facets such the physical layout of items and the interaction of users with the environment.

Basic content personalization consists in matching up the attributes of a user profile, in which preferences and interests are stored, with the attributes of a content object. The Web 2.0 (r)evolution and the advent of user generated content have changed the game for personalization, since the role of people has evolved from passive consumers of information to that of active contributors. One of the forms of user generated content that has drawn more attention from the research community is folksonomy, a taxonomy generated by users who collaboratively annotate and categorize resources of interests with freely chosen keywords called tags.