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Tabular data plays a pivotal role in various fields, making it a popular format for data manipulation and exchange, particularly on the web. The interpretation, extraction, and processing of tabular information are invaluable for knowledge-intensive applications. Notably, significant efforts have been invested in annotating tabular data with ontologies and entities from background knowledge graphs, a process known as Semantic Table Interpretation (STI). STI automation aids in building knowledge graphs, enriching data, and enhancing web-based question answering. This survey aims to provide a comprehensive overview of the STI landscape. It starts by categorizing approaches using a taxonomy of 31 attributes, allowing for comparisons and evaluations. It also examines available tools, assessing them based on 12 criteria. Furthermore, the survey offers an in-depth analysis of the Gold Standards used for evaluating STI approaches. Finally, it provides practical guidance to help end-users choose the most suitable approach for their specific tasks while also discussing unresolved issues and suggesting potential future research directions.

Tables are crucial containers of information, but understanding their meaning may be challenging. Over the years, there has been a surge in interest in data-driven approaches based on deep learning that have increasingly been combined with heuristic-based ones. In the last period, the advent of \acf{llms} has led to a new category of approaches for table annotation. However, these approaches have not been consistently evaluated on a common ground, making evaluation and comparison difficult. This work proposes an extensive evaluation of four STI SOTA approaches: Alligator (formerly s-elbat), Dagobah, TURL, and TableLlama; the first two belong to the family of heuristic-based algorithms, while the others are respectively encoder-only and decoder-only Large Language Models (LLMs). We also include in the evaluation both GPT-4o and GPT-4o-mini, since they excel in various public benchmarks. The primary objective is to measure the ability of these approaches to solve the entity disambiguation task with respect to both the performance achieved on a common-ground evaluation setting and the computational and cost requirements involved, with the ultimate aim of charting new research paths in the field., Comment: 13 pages, 6 figures; fixed avg. accuracy-over-price plot for GPT families, fixed typos in table referencing, added evaluation and inference subsubsection

The Extreme Energy Events (EEE) Project, a joint project of the Centro Fermi (Museo Storico della Fisica e Centro Studi e Ricerche "E.Fermi") and INFN, has a dual purpose: a scientific research program on cosmic rays at ground level and an intense outreach and educational program. The project consists in a network of about 60 tracking detectors, called telescopes, mostly hosted in Italian High Schools. Each telescope is made by three Multigap Resistive Plate Chambers, operated so far with a gas mixture composed by 98% C$_2$H$_2$F$_4$ and 2% SF$_6$. Due to its high Global Warming Potential, a few years ago the EEE collaboration has started an extensive R&D on alternative mixtures environmentally sustainable and compatible with the current experimental setup and operational environment. Among other gas mixtures, the one with helium and hydrofluoroolefin R1234ze gave the best result during the preliminary tests performed with two of the network telescopes. The detector has proved to reach performance levels comparable to those obtained with previous mixtures, without any modification of the hardware. We will discuss the first results obtained with the new mixture, tested with different percentages of the two components., Comment: 14 pages, 6 figures, submitted to JINST

Classifying public tenders is a useful task for both companies that are invited to participate and for inspecting fraudulent activities. To facilitate the task for both participants and public administrations, the European Union presented a common taxonomy (Common Procurement Vocabulary, CPV) which is mandatory for tenders of certain importance; however, the contracts in which a CPV label is mandatory are the minority compared to all the Public Administrations activities. Classifying over a real-world taxonomy introduces some difficulties that can not be ignored. First of all, some fine-grained classes have an insufficient (if any) number of observations in the training set, while other classes are far more frequent (even thousands of times) than the average. To overcome those difficulties, we present a zero-shot approach, based on a pre-trained language model that relies only on label description and respects the label taxonomy. To train our proposed model, we used industrial data, which comes from contrattipubblici.org, a service by SpazioDati s.r.l. that collects public contracts stipulated in Italy in the last 25 years. Results show that the proposed model achieves better performance in classifying low-frequent classes compared to three different baselines, and is also able to predict never-seen classes., Comment: Full-length version of the short paper accepted at COMPSAC 2024

In this paper we report the detection of five strong Gamma-Ray Bursts (GRBs) by the High-Energy Particle Detector (HEPD-01) mounted on board the China Seismo-Electromagnetic Satellite (CSES-01), operational since 2018 on a Sun-synchronous polar orbit at a $\sim$ 507 km altitude and 97$^\circ$ inclination. HEPD-01 was designed to detect high-energy electrons in the energy range 3 - 100 MeV, protons in the range 30 - 300 MeV, and light nuclei in the range 30 - 300 MeV/n. Nonetheless, Monte Carlo simulations have shown HEPD-01 is sensitive to gamma-ray photons in the energy range 300 keV - 50 MeV, even if with a moderate effective area above $\sim$ 5 MeV. A dedicated time correlation analysis between GRBs reported in literature and signals from a set of HEPD-01 trigger configuration masks has confirmed the anticipated detector sensitivity to high-energy photons. A comparison between the simultaneous time profiles of HEPD-01 electron fluxes and photons from GRB190114C, GRB190305A, GRB190928A, GRB200826B and GRB211211A has shown a remarkable similarity, in spite of the different energy ranges. The high-energy response, with peak sensitivity at about 2 MeV, and moderate effective area of the detector in the actual flight configuration explain why these five GRBs, characterised by a fluence above $\sim$ 3 $\times$ 10$^{-5}$ erg cm$^{-2}$ in the energy interval 300 keV - 50 MeV, have been detected., Comment: Accepted for publication in The Astrophysical Journal (ApJ)

The Extreme Energy Events (EEE) Collaboration is fully involved in an ecological transition. The use of the standard gas mixture, \ce{C_{2}H_{2}F_{4}}+ \ce{SF_{6}}, has stopped in favor of an alternative green mixture based on \ce{C_{3}H_{2}F_{4}} with the addition of He or \ce{CO_{2}}. The choise of these new mixtures is motivated by the significant lower Global Warming Potential (GWP) to reduce the emission of gases potentially contributing to the greenhouse effect. The EEE experiment consists of 61 muon telescopes based on Multigap Resistive Plate Chambers (MRPCs), each telescope composed of 3 chambers filled with gas. Several EEE detectors are today completely fluxed with the new ecological mixture. This contribution will report recent results about the telescope performance obtained from studies with the eco-friendly alternative mixture carried out in the last years.

The large availability of datasets fosters the use of \acrshort{ml} and \acrshort{ai} technologies to gather insights, study trends, and predict unseen behaviours out of the world of data. Today, gathering and integrating data from different sources is mainly a manual activity that requires the knowledge of expert users at an high cost in terms of both time and money. It is, therefore, necessary to make the process of gathering and linking data from many different sources affordable to make datasets ready to perform the desired analysis. In this work, we propose the development of a comprehensive framework, named SemTUI, to make the enrichment process flexible, complete, and effective through the use of semantics. The approach is to promote fast integration of external services to perform enrichment tasks such as reconciliation and extension; and to provide users with a graphical interface to support additional tasks, such as refinement to correct ambiguous results provided by automatic enrichment algorithms. A task-driven user evaluation proved SemTUI to be understandable, usable, and capable of achieving table enrichment with little effort and time with user tests that involved people with different skills and experiences.

State-of-the-art approaches for metaphor detection compare their literal - or core - meaning and their contextual meaning using metaphor classifiers based on neural networks. However, metaphorical expressions evolve over time due to various reasons, such as cultural and societal impact. Metaphorical expressions are known to co-evolve with language and literal word meanings, and even drive, to some extent, this evolution. This poses the question of whether different, possibly time-specific, representations of literal meanings may impact the metaphor detection task. To the best of our knowledge, this is the first study that examines the metaphor detection task with a detailed exploratory analysis where different temporal and static word embeddings are used to account for different representations of literal meanings. Our experimental analysis is based on three popular benchmarks used for metaphor detection and word embeddings extracted from different corpora and temporally aligned using different state-of-the-art approaches. The results suggest that the usage of different static word embedding methods does impact the metaphor detection task and some temporal word embeddings slightly outperform static methods. However, the results also suggest that temporal word embeddings may provide representations of the core meaning of the metaphor even too close to their contextual meaning, thus confusing the classifier. Overall, the interaction between temporal language evolution and metaphor detection appears tiny in the benchmark datasets used in our experiments. This suggests that future work for the computational analysis of this important linguistic phenomenon should first start by creating a new dataset where this interaction is better represented., Comment: 12 pages, 4 figures

Understanding differences of viewpoints across corpora is a fundamental task for computational social sciences. In this paper, we propose the Sliced Word Embedding Association Test (SWEAT), a novel statistical measure to compute the relative polarization of a topical wordset across two distributional representations. To this end, SWEAT uses two additional wordsets, deemed to have opposite valence, to represent two different poles. We validate our approach and illustrate a case study to show the usefulness of the introduced measure., Comment: Published as a conference paper at EMNLP2021

This paper describes the simulation framework of the Extreme Energy Events (EEE) experiment. EEE is a network of cosmic muon trackers, each made of three Multi-gap Resistive Plate Chambers (MRPC), able to precisely measure the absolute muon crossing time and the muon integrated angular flux at the ground level. The response of a single MRPC and the combination of three chambers have been implemented in a GEANT4-based framework (GEMC) to study the telescope response. The detector geometry, as well as details about the surrounding materials and the location of the telescopes have been included in the simulations in order to realistically reproduce the experimental set-up of each telescope. A model based on the latest parametrization of the cosmic muon flux has been used to generate single muon events. After validating the framework by comparing simulations to selected EEE telescope data, it has been used to determine detector parameters not accessible by analysing experimental data only, such as detection efficiency, angular and spatial resolution., Comment: 16 pages, 25 figures, 3 tables, submitted to EPJC

The Extreme Energy Events (EEE) Project employs Multi-gap Resistive Plate Chamber (MRPC) for studying the secondary cosmic ray muons in Extensive Air Showers. The array consists of about 60 tracking detectors, sparse on Italian territory and at CERN. The MRPCs are flowed with a gas mixture based on $C_2H_2F_4$ and $SF_6$, both of which are fluorinated greenhouse gases with a high environmental impact on the atmosphere. Due to the restrictions imposed by the European Union, these gases are being phased out of production and their cost is largely increasing. The EEE Collaboration started a campaign to reduce the gas emission from its array with the aim of containing costs and decreasing the experiment global warming impact. One method is to reduce the gas rate in each EEE detector. Another is to develop a gas recirculation system, whose a first prototype has been installed at one of the EEE stations located at CERN. Jointly a parallel strategy is focused on searching for environmental friendly gas mixtures which are able to substitute the standard mixture without affecting the MRPC performance. An overview and first results are presented here., Comment: 11 pages, 9 figures

The Extreme Energy Events (EEE) experiment, dedicated to the study of secondary cosmic rays, is arguably the largest detector system in the world implemented by Multigap Resistive Plate Chambers. The EEE network consists of 60 telescopes distributed over all the Italian territory; each telescope is made of three MRPCs and allows to reconstruct the trajectory of cosmic muons with high efficiency and optimal angular resolution. A distinctive feature of the EEE network is that almost all telescopes are housed in High Schools and managed by groups of students and teachers, who previously took care of their construction at CERN. This peculiarity is a big plus for the experiment, which combines the scientific relevance of its objectives with effective outreach activities. The unconventional location of the detectors, mainly in standard classrooms of school buildings, with heterogeneous maintenance conditions and without controlled temperature and dedicated power lines, is a unique test field to verify the robustness, the low aging characteristics and the long-lasting performance of MRPC technology for particle monitoring and timing. Finally, it is reported how the spatial resolution, efficiency, tracking capability and stability of these chambers behave in time., Comment: 9 pages, 5 figures

The Extreme Energy Events (EEE) Project is mainly devoted to the study of the secondary cosmic ray radiation by using muon tracker telescopes made of three Multigap Resistive Plate Chambers (MRPC) each. The experiment consists of a telescope network mainly distributed across Italy, hosted in different building structures pertaining to high schools, universities and research centers. Therefore, the possibility to take into account the effects of these structures on collected data is important for the large physics programme of the project. A simulation tool, based on GEANT4 and using GEMC framework, has been implemented to take into account the muon interaction with EEE telescopes and to estimate the effects on data of the structures surrounding the experimental apparata.A dedicated event generator producing realistic muon distributions, detailed geometry and microscopic behavior of MRPCs have been included to produce experimental-like data. The comparison between simulated and experimental data, and the estimation of detector resolutions is here presented and discussed.

Knowledge graph embeddings are now a widely adopted approach to knowledge representation in which entities and relationships are embedded in vector spaces. In this chapter, we introduce the reader to the concept of knowledge graph embeddings by explaining what they are, how they can be generated and how they can be evaluated. We summarize the state-of-the-art in this field by describing the approaches that have been introduced to represent knowledge in the vector space. In relation to knowledge representation, we consider the problem of explainability, and discuss models and methods for explaining predictions obtained via knowledge graph embeddings., Comment: Federico Bianchi, Gaetano Rossiello, Luca Costabello, Matteo Plamonari, Pasquale Minervini, Knowledge Graph Embeddings and Explainable AI. In: Ilaria Tiddi, Freddy Lecue, Pascal Hitzler (eds.), Knowledge Graphs for eXplainable AI -- Foundations, Applications and Challenges. Studies on the Semantic Web, IOS Press, Amsterdam, 2020

A simulation tool based on GEMC framework to describe the MRPC telescope of the Extreme Energy Events (EEE) Project is presented. The EEE experiment is mainly devoted to the study of the secondary cosmic muons by using MRPC telescope distributed in high schools and research centres in Italy and at CERN. This takes into account the muon interactions with EEE telescopes and the structures surrounding the experimental apparata; it consists of a dedicated event generator producing realistic muon distribution and a detailed geometry description of the detector. Microscopic behaviour of MRPCs has been included to produce experimental-like data. A method to estimate the chamber effciency directly from data has been implemented and tested by comparing the experimental and simulated polar angle distribution of muons., Comment: 7 pages, 7 figure, International Workshop - Detection Systems and Techniques in Nuclear and Particle Physics - Messina (Italy), September 11-13, 2019. To be published in JP:CS

Word2vec is one of the most used algorithms to generate word embeddings because of a good mix of efficiency, quality of the generated representations and cognitive grounding. However, word meaning is not static and depends on the context in which words are used. Differences in word meaning that depends on time, location, topic, and other factors, can be studied by analyzing embeddings generated from different corpora in collections that are representative of these factors. For example, language evolution can be studied using a collection of news articles published in different time periods. In this paper, we present a general framework to support cross-corpora language studies with word embeddings, where embeddings generated from different corpora can be compared to find correspondences and differences in meaning across the corpora. CADE is the core component of our framework and solves the key problem of aligning the embeddings generated from different corpora. In particular, we focus on providing solid evidence about the effectiveness, generality, and robustness of CADE. To this end, we conduct quantitative and qualitative experiments in different domains, from temporal word embeddings to language localization and topical analysis. The results of our experiments suggest that CADE achieves state-of-the-art or superior performance on tasks where several competing approaches are available, yet providing a general method that can be used in a variety of domains. Finally, our experiments shed light on the conditions under which the alignment is reliable, which substantially depends on the degree of cross-corpora vocabulary overlap., Comment: arXiv admin note: text overlap with arXiv:1906.02376

Temporal word embeddings have been proposed to support the analysis of word meaning shifts during time and to study the evolution of languages. Different approaches have been proposed to generate vector representations of words that embed their meaning during a specific time interval. However, the training process used in these approaches is complex, may be inefficient or it may require large text corpora. As a consequence, these approaches may be difficult to apply in resource-scarce domains or by scientists with limited in-depth knowledge of embedding models. In this paper, we propose a new heuristic to train temporal word embeddings based on the Word2vec model. The heuristic consists in using atemporal vectors as a reference, i.e., as a compass, when training the representations specific to a given time interval. The use of the compass simplifies the training process and makes it more efficient. Experiments conducted using state-of-the-art datasets and methodologies suggest that our approach outperforms or equals comparable approaches while being more robust in terms of the required corpus size., Comment: Accepted at AAAI2019