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Datasets that include alignments between natural language and Knowledge Graphs are fundamental to a wide variety of Natural Language Processing and Generation tasks. Current state-of-the-art aligned datasets, though, are significantly impacted by reduced size and scarcity of covered domains, and their quality is difficult to evaluate. To compensate for these issues, we introduce SEALIon, a tool for extracting RDF triples from natural language textual corpora based on a human-in-the-loop approach. We present our first results of SEALIon’s approach, paving the way for further researches in the field of human-in-the-loop triple extraction.

In the video game industry, great importance is given to the experience that the user has while playing a game. In particular, this experience benefits from the players' perceived sense of being in the game or immersion. The level of user immersion depends not only on the game's content but also on how the game is displayed, thus on its User Interface (UI ) and the Head’s-Up Display (HUD). Another factor influencing immersiveness that has been found in the literature is the player's expertise: the more experience the user has with a specific game, the less they need information on the screen to be immersed in the game. Player's level of immersion can be accessed by using both questionnaires of their perceived experience and exploiting their behavioural and physiological responses while playing the target game. Therefore, in this paper, we propose an experimental protocol to access immersiveness of gamers while playing a third-person shooter (Fortnite) with UIs with a standard, a dietetic, and a proposed HUD. A subjective evaluation of the immersion will be provided by completing the Immersive Experience Questionnaire (IEQ), while objective indicators will be provided by face tracking, behaviour and physiological responses analyses. The ultimate goal of this study is to define guidelines for video game UI development that can enhance the players' immersion.

This paper describes s-elBat, a Semantic Table Interpretation approach. The approach inherits and improves the part of the techniques belonging to the MantisTable, an approach used and tested in previous editions of the SemTab challenge. s-elBat adds an innovative and optimised lookup approach for generating candidate entities for the annotation.

Better understanding human visual attention during reading can provide valuable insights for developing user-centred computations models. A considerable amount of data, presented in a tabular form, is used in daily activities and is available on the Web nowadays. Several approaches have proposed an automated table summarisation method to improve the users' experience and give them succinct summaries of tables. However, there has been little attention to considering user behaviour in the design of automated table summarisation. In this paper, we present the findings of an empirical study, where we investigate, with the help of standard User Experience tools (eye-tracking technology and surveys), how users approach the reading of a table. We focus on evaluating how the domain knowledge and interest of the users influence their comprehension, eventually identifying four possible user-profiles and their different information needs. In order to show the impact of our findings on the selection of the information to keep in summary, we present and release a tool that, in addition to supporting the development of similar experiments, allows checking the information presented in summary in the form of Resource Description Framework (RDF) triples, by exploiting the semantic annotation of the table.

Poverty is a multidimensional concept that, besides the economic status and financial resources, should consider the lack of access to resources enabling a minimum standard of living and participation in society. In particular, elderly people are likely to require help with some or everyday activities and the total costs of this help can be very high and absorb a significant amount of their income, especially when they are alone and not in good health. This work proposes a strategy based on Bayesian Network to identify the risk of poverty in elderly people, relying on multidimensional indicators learned from heterogeneous sources of information, including the difficulty of accessing services, social exclusion and health status. Data cleaning and integration that include socio-demographic indicators are proposed here, and an overall framework of analysis that can be exportable to several other categories of the population at risk of poverty is presented.

When information available in unstructured or semi-structured formats, e.g., tables or texts, comes in, finding links between strings appearing in these sources and the entities they refer to in some background Knowledge Graphs (KGs) is a key step to integrate, enrich and extend the data and/or KGs. This Entity Linking task is usually decomposed into Entity Retrieval and Entity Disambiguation because of the large entity search space. This paper presents an Entity Retrieval service (LamAPI) and discusses the impact of different retrieval configurations, i.e., query and filtering strategies, on the retrieval of entities. The approach is to augment the search activity with extra information, like types, associated with the strings in the original datasets. The results have been empirically validated against public datasets.

In this paper, we present MammoTab, a dataset composed of 1M Wikipedia tables extracted from over 20M Wikipedia pages and annotated through Wikidata. The lack of this kind of datasets in the state- of-the-art makes MammoTab a good resource for testing and training Semantic Table Interpretation approaches. The dataset has been designed to cover several key challenges, such as disambiguation, homonymy, and NIL-mentions. The dataset has been evaluated using MTab, one of the best approaches of the SemTab challenge.

Despite the rapid development in very recent years of Artificial Intelligence models to predict poverty, this problem still remains an unsolved open issue especially in a multidimensional perspective. In this work we present our proposal to face multidimensional poverty in case of a fragile population, the older adults, starting from an unlabelled dataset, collected administering a proper questionnaire to about 500 individuals. Firstly a model that allows to label the collected data into three classes of poverty is proposed. Then, XGBoost and Naive Bayes classifiers are considered to solve the classification problem. Finally, after having determined the relative importance of each feature, a novel Naive Bayes model is proposed that relies on new aggregated features that represent five poverty dimensions. These aggregated features are obtained by properly combining the variables collected through the questionnaire with cut-offs defined by a domain expert.

It could be affirmed that with the fall of the Berlin Wall a new history of migration in Europe began, challenging previous models of interpretation of the phenomenon. I propose to observe and analyse the transformation on three different scales. The first one - that of the national state - regards the constellation of citizenship regimes and alteration of models of integration within Western European countries in the perspective of the ‘failure of multiculturalism’. The second - transnational internal European scale - concerns the dialectics between the EU citizens’ residence and labour mobility regime within the territory of the Union, and the persistent consideration of these people as ‘internal migrants’. The third one - the global scale - tackles the issue of ‘people on the move’ - regarding the constant waves of (forced) migration along Mediterranean and Balkan routes - that has been producing the ‘refugee crisis’ since 2015. Understanding the phenomenon and contrasting either scientifically or publicly the symbolic and effective violence and victims it has been producing, has became a challenge for the ‘public face of sociology’ and its aim of defending the interests of humanity (Burawoy 2005, XI Thesis: 24).

In this paper, we present MantisTable V, a novel unsupervised and automatic approach for the Semantic Table Interpretation. The approach is performed against DBpedia and Wikidata, and it can be easily adapted to any other Knowledge Graph. Moreover, we provide a tool (LamAPI) that allows to efficiently fetch data needed for Semantic Table Interpretation tasks from the Knowledge Graph dumps. The approach is manageable through a User Interface (tUI), a separated tool, which allows the visualisation and modification of table data and semantic annotations.

Much information is conveyed within tables, which can be semantically annotated by humans or (semi)automatic approaches. Nevertheless, many applications cannot take full advantage of semantic annotations because of the low quality. A few methodologies exist for the quality assessment of semantic annotation of tabular data, but they do not automatically assess the quality as a multidimensional concept through different quality dimensions. The quality dimensions are implemented in STILTool 2, a web application to automate the quality assessment of the annotations. The evaluation is carried out by comparing the quality of semantic annotations with gold standards. The work presented here has been applied to at least three use cases. The results show that our approach can give us hints about the quality issues and how to address them.

In recent years, there has been an increasing interest in extracting and annotating tables on the Web. This activity allows the transformation of text data into machine-readable formats to enable the execution of various artificial intelligence tasks, e.g. semantic search and dataset extension. Semantic Table Interpretation is the process of annotating elements in a table. Current approaches are mainly based on lexical matching algorithms that rely on metadata associated with tables or custom Knowledge Graphs. Their main limitations are due to the lack of metadata, the little use of contextual semantics, and the incompleteness of the proposed methods that do not include all the necessary steps. In this paper, we propose a comprehensive approach and a tool that provides an unsupervised method to annotate independent tables, possibly without header row or other external information. The approach is based on the definition of a context created from the elements within the table in order to discriminate among matching entities found in shared Knowledge Graphs and create high-quality annotations. The approach has achieved excellent results in an international challenge, thus proving its effectiveness.

In this paper, we present a novel unsupervised and automatic approach for Semantic Table Interpretation. The technique presented is performed against DBpedia and Wikidata, and it can be easily adapted to any other Knowledge Graph (KG). Moreover, we provide a tool (LamAPI) that allows to efficiently fetch data needed for Semantic Table Interpretation (STI) tasks from the KG dumps.

Understanding users’ reading behaviour can facilitate and support the development of data lexicalisation models based on users’ characteristics. A significant amount of data can be found online in tabular form, and several models have been developed to provide the user with summaries of the content of such tables. Nevertheless, studies analysing table reading patterns are almost entirely lacking in the literature, making it almost impossible to integrate findings on user reading behaviour into lexicalisation models. This work aims to suggest a new line of gaze-related research that can integrate insights about user behaviour and characteristics into data summarisation algorithms to provide textual content that meets the user’s information needs. An overview of human-gaze studies applied to natural language will be presented to outline a study on human interaction with tables. Potential fields of application and challenges in applying these results to the field of table summarisation, namely the task of producing short summaries of tabular data, from a user-centred perspective will be discussed.

BACKGROUND: Aging is a process that does not refer only to the accumulation of damage in a human being over time but rather to individual changes determined by genetic, lifestyle, social and environmental factors. Aging is one of the greatest known risk factors for most human diseases. An older person in good health has a good level of independence, weighs less on the national health system and plays a productive and active role in his/her community; thus, the concept of 'healthy aging', reflecting older adult-environment fit should be promoted. The interactions between lifestyle, including nutrition, and health play a fundamental role in the aging process; eating habits and eating behaviours are recognized as important modifiable factors potentially leading to a healthy 'phenotype'. OBJECTIVE AND METHODS: A multidisciplinary consortium with three Italian universities (the University of Milano-Bicocca, University of Pavia and University of Calabria) and Italian Small-Medium Enterprises proposed the Food Social Network (Food NET) project. Food NET overall outcome is to achieve target-specific guidelines and exact technologies for accessible functional foods, aimed at improving the quality of life and nutritional status of citizens (aged>65) of the Metropolitan City of Milan (Italy). CONCLUSIONS: This project is part of the 'Smart Living' and food-related strategies aimed at responding to the needs of this target population, developing new food products, appropriate to meet the specific requirements and ensuring and promoting sustainable diets for healthy aging by effective food policy approaches.

In this paper, we present MammoTab, a dataset composed of 1M Wikipedia tables extracted from over 20M Wikipedia pages and annotated through Wikidata. The lack of this kind of datasets in the state- of-the-art makes MammoTab a good resource for testing and training Semantic Table Interpretation approaches. The dataset has been designed to cover several key challenges, such as disambiguation, homonymy, and NIL-mentions. The dataset has been evaluated using MTab, one of the best approaches of the SemTab challenge.

Natural Language Generation is a field that is becoming relevant in several domains, including journalism. Natural Language Generation techniques can be of great help to journalists, allowing a substantial reduction in the time required to complete repetitive tasks. In this position paper, we enforce the idea that automated tools can reduce the effort required to journalist when writing articles; at the same time we introduce GazelLex (Gazette Lexicalization), a prototype that covers several steps of Natural Language Generation, in order to create soccer articles automatically, using data from Knowledge Graphs, leaving journalists the possibility of refining and editing articles with additional information. We shall present our first results and current limits of the approach, and we shall also describe some lessons learned that might be useful to readers that want to explore this field.

This paper discusses the time and steps of the introduction of intensive agriculture and evolution of irrigation systems to sustain crops in the palaeo-oasis of Salut in the northern Sultanate of Oman. Various geoarchaeological methods allow reconstructing the exploitation of the natural resources of the region and technological development of irrigation methods since the Mid-Holocene. Intensive agriculture started during the Bronze Age and continued with some spatial and intensity fluctuations up to the Islamic period. Cultivations were initially sustained by surface irrigation systems and later replaced by a dense net of aflaj, the typical surface/underground system adopted in the Levant, Arabian Peninsula and western Asia to collect water from deep piedmont aquifers and redistribute it to the fields located in the lowlands. Our results indicate that the aflaj were in use for a long period in the palaeo-oasis formed along Wadi Sayfam and surrounding the citadel of Salut. Uranium-Thorium dating of calcareous tufa formed in the underground tunnels of the aflaj suggests that they were used between ∼540 BCE and ∼1150 CE. After ∼1150 CE Wadi Sayfam were abandoned and the size of the oasis shrank substantially. During the late Islamic period, a surface aqueduct descending from the piedmont of Jabal Shams secured water supply. Our work confirms that in arid lands archaeological and historical communities were able to actively modulate their response to climate changes by using a variety of technological strategies.

Esiste un numero significativo di documenti, report e pagine Web – un'analisi riporta 233 milioni di tabelle relazionali nel repository Common Crawl contenente un totale 2,85 miliardi di documenti – che fanno uso di tabelle per fornire informazioni che non possono essere facilmente elaborate dagli umani o capite dai computer. Per risolvere questo problema proponiamo un nuovo approccio che permetterà ai computer di interpretare la semantica di una tabella, e fornirà agli umani una rappresentazione più accessibile dei dati contenuti in essa. Per conseguire questo obiettivo, il problema principale è stato suddiviso in tre sotto-problemi: (i) la definizione di un metodo per fornire un'interpretazione semantica dei dati di una tabella; (ii) la definizione di un modello descrittivo che permetta ai computer di capire e condividere dati di una tabella; e (iii) la definizione di processi, tecniche e algoritmi per generare rappresentazioni dei dati in linguaggio naturale. Per quanto riguarda il sotto-problema (i), la rappresentazione semantica dei dati è stata ottenuta attraverso l'applicazione di tecniche di interpretazione di tabelle (table interpretation), che aiuta gli utenti ad identificare, in una maniera semi-automatica, il significato dei dati di una tabella e le relazioni tra di essi. Queste tecniche considerano in input una tabella e un Knowledge Graph, e restituiscono una rappresentazione RDF – un set di tuple – del contenuto della tabella, facendo riferimento ai concetti e alle proprietà del KG. Questa dissertazione presenta un nuovo approccio che, a partire dai lavori presenti in letteratura, ha portato allo sviluppo di un nuovo strumento, chiamato MantisTable, che effettua automaticamente un'interpretazione semantica completa della tabella. Gli esperimenti condotti hanno mostrato buoni risultati, rispetto alle tecniche e ai tool simili. Il sotto-problema (ii) è stato affrontato con la definizione di nuovi modi di rappresenta, A significant number of documents, reports and Web pages –an analysis reports 233M relational tables within the Common Crawl repository of 1.81 billion documents– makes use of tables to convey information that cannot be easily processed by humans, and understood by computers. To address this issue, we propose a new approach that allows computers to interpret the semantics of a table, and provides humans with a more accessible representation of the data contained in a table. To achieve the objective, the general problem has been broken down into three sub-problems: (i) define a method to provide a semantic interpretation of table data; (ii) define a descriptive model that allows computers to understand and share table data; and (iii) define processes, techniques and algorithms to generate natural language representation of the table data. Regarding sub-problem (i), the semantic representation of a data has been obtained through the application of table interpretation techniques, which supports users to identify in a semi-automatic way the meaning of the data in the table and the relationships between them. Such techniques take a table and a Knowledge Graph (KG) as input, and deliver as output an RDF representation –a set of tuples –. The output contains the input table annotated with the KG concepts and properties. This thesis presents a new approach, rooted in the existing literature, to laid the foundations for the development of a new tool -called MantisTable- which automatically performs a complete semantic interpretation of a table. The conducted experiments have shown good results compared to similar techniques. Sub-problem (ii) has been tackled by defining new ways of representing data. A new kind of description has been defined that combines the OpenAPI specification with the JSON-LD. The results of semantic table interpretation techniques are exploited to enhance a popular description format and allow automatic retrieval and proce

This paper describes STILTool, an open-source tool for the automatic evaluation of the quality of semantic annotations computed by semantic table interpretation approaches. STILTool provides a graphical interface allowing users to analyse the correctness of the annotations of tabular data. The tool also provides a set of statistics in order to identify the most common error patterns.

This paper describes MantisTable, an open source Semantic Table Interpretation tool, which automatically annotates tables using a Knowledge Graph. MantisTable provides a graphical interface allowing users to analyse the results of the semantic table interpretation process and validate the final annotations. The tool also provides a guided mode for viewing and editing annotations by users. Thanks to MantisTable features, it is possible to create semantic annotations and favour the publication and exchange of tabular data.