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1. Survey on Semantic Interpretation of Tabular Data: Challenges and Directions

Author

Cremaschi, Marco, Spahiu, Blerina, Palmonari, Matteo, and Jimenez-Ruiz, Ernesto

Subjects
Computer Science - Artificial Intelligence, Computer Science - Information Retrieval
Abstract

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.

Published
2024

2. Evaluating LLMs on Entity Disambiguation in Tables

Author

Belotti, Federico, Dadda, Fabio, Cremaschi, Marco, Avogadro, Roberto, and Palmonari, Matteo

Subjects
Computer Science - Computation and Language, Computer Science - Artificial Intelligence
Abstract

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

Published
2024

3. Combining Knowledge Graphs and NLP to Analyze Instant Messaging Data in Criminal Investigations

Author

Pozzi, Riccardo, Barbera, Valentina, Alva Principe, Renzo, Giardini, Davide, Rubini, Riccardo, Palmonari, Matteo, Goos, Gerhard, Series Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Barhamgi, Mahmoud, editor, Wang, Hua, editor, and Wang, Xin, editor

Published
2025
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4. Zero-Shot Hierarchical Classification on the Common Procurement Vocabulary Taxonomy

Author

Moiraghi, Federico, Palmonari, Matteo, Allavena, Davide, and Morando, Federico

Subjects
Computer Science - Machine Learning, Computer Science - Computation and Language
Abstract

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

Published
2024

5. Correction to: The Semantic Web – ISWC 2024

Author

Demartini, Gianluca, Hose, Katja, Acosta, Maribel, Palmonari, Matteo, Cheng, Gong, Skaf-Molli, Hala, Ferranti, Nicolas, Hernández, Daniel, Hogan, Aidan, Goos, Gerhard, Series Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Demartini, Gianluca, editor, Hose, Katja, editor, Acosta, Maribel, editor, Palmonari, Matteo, editor, Cheng, Gong, editor, Skaf-Molli, Hala, editor, Ferranti, Nicolas, editor, Hernández, Daniel, editor, and Hogan, Aidan, editor

Published
2025
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6. SemTUI: a Framework for the Interactive Semantic Enrichment of Tabular Data

Author

Ripamonti, Marco, De Paoli, Flavio, and Palmonari, Matteo

Subjects
Computer Science - Databases, Computer Science - Artificial Intelligence
Abstract

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.

Published
2022

7. On the Impact of Temporal Representations on Metaphor Detection

Author

Ottolina, Giorgio, Palmonari, Matteo, Alam, Mehwish, and Vimercati, Manuel

Subjects
Computer Science - Computation and Language, Computer Science - Artificial Intelligence
Abstract

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

Published
2021

8. SWEAT: Scoring Polarization of Topics across Different Corpora

Author

Bianchi, Federico, Marelli, Marco, Nicoli, Paolo, and Palmonari, Matteo

Subjects
Computer Science - Computation and Language
Abstract

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

Published
2021

11. Knowledge Graph Embeddings and Explainable AI

Author

Bianchi, Federico, Rossiello, Gaetano, Costabello, Luca, Palmonari, Matteo, and Minervini, Pasquale

Subjects
Computer Science - Artificial Intelligence, Computer Science - Computation and Language
Abstract

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

Published
2020
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12. Compass-aligned Distributional Embeddings for Studying Semantic Differences across Corpora

Author

Bianchi, Federico, Di Carlo, Valerio, Nicoli, Paolo, and Palmonari, Matteo

Subjects
Computer Science - Artificial Intelligence, Computer Science - Computation and Language
Abstract

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

Published
2020

13. Named Entity Recognition and Linking for Entity Extraction from Italian Civil Judgements

Author

Pozzi, Riccardo, Rubini, Riccardo, Bernasconi, Christian, Palmonari, Matteo, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Basili, Roberto, editor, Lembo, Domenico, editor, Limongelli, Carla, editor, and Orlandini, Andrea, editor

Published
2023
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14. Training Temporal Word Embeddings with a Compass

Author

Di Carlo, Valerio, Bianchi, Federico, and Palmonari, Matteo

Subjects
Computer Science - Computation and Language, Computer Science - Machine Learning
Abstract

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

Published
2019

15. Supporting Semantic Data Enrichment at Scale

Author

Ciavotta, Michele, Cutrona, Vincenzo, De Paoli, Flavio, Nikolov, Nikolay, Palmonari, Matteo, Roman, Dumitru, Curry, Edward, editor, Auer, Sören, editor, Berre, Arne J., editor, Metzger, Andreas, editor, Perez, Maria S., editor, and Zillner, Sonja, editor

Published
2022
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18. BEEO: Semantic Support forEvent-Based Data Analytics

Author

Ciavotta, Michele, Cutrona, Vincenzo, De Paoli, Flavio, Palmonari, Matteo, Spahiu, Blerina, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Hotho, Andreas, editor, Blomqvist, Eva, editor, Dietze, Stefan, editor, Fokoue, Achille, editor, Ding, Ying, editor, Barnaghi, Payam, editor, Haller, Armin, editor, Dragoni, Mauro, editor, and Alani, Harith, editor

Published
2021
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21. Tough Tables: Carefully Evaluating Entity Linking for Tabular Data

Author

Cutrona, Vincenzo, Bianchi, Federico, Jiménez-Ruiz, Ernesto, Palmonari, Matteo, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Pan, Jeff Z., editor, Tamma, Valentina, editor, d’Amato, Claudia, editor, Janowicz, Krzysztof, editor, Fu, Bo, editor, Polleres, Axel, editor, Seneviratne, Oshani, editor, and Kagal, Lalana, editor

Published
2020
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22. Understanding the structure of knowledge graphs with ABSTAT profiles.

Author

Spahiu, Blerina, Palmonari, Matteo, Alva Principe, Renzo Arturo, and Rula, Anisa

Subjects
KNOWLEDGE graphs, ONTOLOGY
Abstract

While there has been a trend in the last decades for publishing large-scale and highly-interconnected Knowledge Graphs (KGs), their users often get overwhelmed by the task of understanding their content as a result of their size and complexity. Data profiling approaches have been proposed to summarize large KGs into concise and meaningful representations, so that they can be better explored, processed, and managed. Profiles based on schema patterns represent each triple in a KG with its schema-level counterpart, thus covering the entire KG with profiles of considerable size. In this paper, we provide empirical evidence that profiles based on schema patterns, if explored with suitable mechanisms, can be useful to help users understand the content of big and complex KGs. ABSTAT provides concise pattern-based profiles and comes with faceted interfaces for profile exploration. Using this tool we present a user study based on query completion tasks. We demonstrate that users who look at ABSTAT profiles formulate their queries better and faster than users browsing the ontology of the KGs. The latter is a pretty strong baseline considering that many KGs do not even come with a specific ontology to be explored by the users. To the best of our knowledge, this is the first attempt to investigate the impact of profiling techniques on tasks related to knowledge graph understanding with a user study. [ABSTRACT FROM AUTHOR]

Published
2024
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24. Fair Classification with Explicit Constraints: a Neuro-symbolic Approach with Logic Tensor Networks.

Author

Greco, G, SCHETTINI, RAIMONDO, PALMONARI, MATTEO LUIGI, GRECO, GRETA, Greco, G, SCHETTINI, RAIMONDO, PALMONARI, MATTEO LUIGI, and GRECO, GRETA

Abstract

Gli algoritmi sono vulnerabili a sviluppare pregiudizi che potrebbero rendere le loro decisioni ingiuste nei confronti di particolari gruppi di individui. La fairness comporta una serie di aspetti che dipendono fortemente dal dominio di applicazione che considera diverse nozioni su cosa sia una decisione giusta nelle situazioni che influiscono sugli individui della popolazione. Le precise differenze, implicazioni e ortogonalità tra queste nozioni non sono state ancora completamente analizzate e il lavoro prova a mattere ordini in questo zoo di definizioni. Quando si tratta di far rispettare tali vincoli, la maggior parte dei modelli di mitigazione in-processing incorpora vincoli di fairness come componente fondamentale della loss function e richiede quindi aggiustamenti a livello di codice per adattarsi a contesti specifici e domini. Piuttosto che fare affidamento su un approccio procedurale, il nostro modello sfrutta la struttura dichiarativa conoscenza acquisita per codificare i requisiti di equità sotto forma di regole logiche che catturano affermazioni del linguaggio naturale bigue e precise. Proponiamo un'integrazione neuro-simbolica basato su Logic Tensor Networks che combina apprendimento di reti nueurali basato sui daticonoscenze logiche di alto livello, consentendo di eseguire task di classificazione riducendo discriminazione. Le prove sperimentali mostrano che le prestazioni sono buone quanto quelle dello stato dell’arte fornendo così un quadro flessibile per tenere conto della non discriminazione, spesso a un costo modesto in termini di precisione., Algorithms are vulnerable to biases that might render their decisions unfair toward particular groups of individuals. Fairness comes with a range of facets that strongly depend on the application domain that consider different notions of what is a fair decision in situations impacting individuals in the population. The precise differences, implications and orthogonality between these notions have not yet been fully analyzed and we try to make some order out of this zoo of definitions. When it comes about enforcing such constraints, most in-processing mitigation models embed fairness constraints as fundamental component of the loss function thus requiring code-level adjustments to adapt to specific contexts and domains. Rather than relying on a procedural approach, our model leverages declarative structured knowledge to encode fairness requirements in the form of logic rules capturing unam- biguous and precise natural language statements. We propose a neuro-symbolic integration approach based on Logic Tensor Networks that combines data-driven network-based learning with high-level logical knowledge, allowing to perform classification tasks while reducing discrimination. Experimental evidence shows that performance is as good as state-of-the-art thus providing a flexible framework to account for non-discrimination often at a modest cost in terms of accuracy.

Published
2024

25. Mapping Lexical Knowledge to Distributed Models for Ontology Concept Invention

Author

Vimercati, Manuel, Bianchi, Federico, Soto, Mauricio, Palmonari, Matteo, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Alviano, Mario, editor, Greco, Gianluigi, editor, and Scarcello, Francesco, editor

Published
2019
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26. Complementing Logical Reasoning with Sub-symbolic Commonsense

Author

Bianchi, Federico, Palmonari, Matteo, Hitzler, Pascal, Serafini, Luciano, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Fodor, Paul, editor, Montali, Marco, editor, Calvanese, Diego, editor, and Roman, Dumitru, editor

Published
2019
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27. Semantically-Enabled Optimization of Digital Marketing Campaigns

Author

Cutrona, Vincenzo, De Paoli, Flavio, Košmerlj, Aljaž, Nikolov, Nikolay, Palmonari, Matteo, Perales, Fernando, Roman, Dumitru, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Ghidini, Chiara, editor, Hartig, Olaf, editor, Maleshkova, Maria, editor, Svátek, Vojtěch, editor, Cruz, Isabel, editor, Hogan, Aidan, editor, Song, Jie, editor, Lefrançois, Maxime, editor, and Gandon, Fabien, editor

Published
2019
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30. ABSTAT 1.0: Compute, Manage and Share Semantic Profiles of RDF Knowledge Graphs

Author

Principe, Renzo Arturo Alva, Spahiu, Blerina, Palmonari, Matteo, Rula, Anisa, De Paoli, Flavio, Maurino, Andrea, Hutchison, David, Series Editor, Kanade, Takeo, Series Editor, Kittler, Josef, Series Editor, Kleinberg, Jon M., Series Editor, Mattern, Friedemann, Series Editor, Mitchell, John C., Series Editor, Naor, Moni, Series Editor, Pandu Rangan, C., Series Editor, Steffen, Bernhard, Series Editor, Terzopoulos, Demetri, Series Editor, Tygar, Doug, Series Editor, Weikum, Gerhard, Series Editor, Gangemi, Aldo, editor, Gentile, Anna Lisa, editor, Nuzzolese, Andrea Giovanni, editor, Rudolph, Sebastian, editor, Maleshkova, Maria, editor, Paulheim, Heiko, editor, Pan, Jeff Z, editor, and Alam, Mehwish, editor

Published
2018
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31. Using Ontology-Based Data Summarization to Develop Semantics-Aware Recommender Systems

Author

Di Noia, Tommaso, Magarelli, Corrado, Maurino, Andrea, Palmonari, Matteo, Rula, Anisa, Hutchison, David, Series Editor, Kanade, Takeo, Series Editor, Kittler, Josef, Series Editor, Kleinberg, Jon M., Series Editor, Mattern, Friedemann, Series Editor, Mitchell, John C., Series Editor, Naor, Moni, Series Editor, Pandu Rangan, C., Series Editor, Steffen, Bernhard, Series Editor, Terzopoulos, Demetri, Series Editor, Tygar, Doug, Series Editor, Weikum, Gerhard, Series Editor, Gangemi, Aldo, editor, Navigli, Roberto, editor, Vidal, Maria-Esther, editor, Hitzler, Pascal, editor, Troncy, Raphaël, editor, Hollink, Laura, editor, Tordai, Anna, editor, and Alam, Mehwish, editor

Published
2018
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32. Adapting Named Entity Types to New Ontologies in a Microblogging Environment

Author

Fersini, Elisabetta, Manchanda, Pikakshi, Messina, Enza, Nozza, Debora, Palmonari, Matteo, Hutchison, David, Series Editor, Kanade, Takeo, Series Editor, Kittler, Josef, Series Editor, Kleinberg, Jon M., Series Editor, Mattern, Friedemann, Series Editor, Mitchell, John C., Series Editor, Naor, Moni, Series Editor, Pandu Rangan, C., Series Editor, Steffen, Bernhard, Series Editor, Terzopoulos, Demetri, Series Editor, Tygar, Doug, Series Editor, Weikum, Gerhard, Series Editor, Mouhoub, Malek, editor, Sadaoui, Samira, editor, Ait Mohamed, Otmane, editor, and Ali, Moonis, editor

Published
2018
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33. Towards Encoding Time in Text-Based Entity Embeddings

Author

Bianchi, Federico, Palmonari, Matteo, Nozza, Debora, Hutchison, David, Series Editor, Kanade, Takeo, Series Editor, Kittler, Josef, Series Editor, Kleinberg, Jon M., Series Editor, Mattern, Friedemann, Series Editor, Mitchell, John C., Series Editor, Naor, Moni, Series Editor, Pandu Rangan, C., Series Editor, Steffen, Bernhard, Series Editor, Terzopoulos, Demetri, Series Editor, Tygar, Doug, Series Editor, Weikum, Gerhard, Series Editor, Vrandečić, Denny, editor, Bontcheva, Kalina, editor, Suárez-Figueroa, Mari Carmen, editor, Presutti, Valentina, editor, Celino, Irene, editor, Sabou, Marta, editor, Kaffee, Lucie-Aimée, editor, and Simperl, Elena, editor

Published
2018
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34. Actively Learning to Rank Semantic Associations for Personalized Contextual Exploration of Knowledge Graphs

Author

Bianchi, Federico, Palmonari, Matteo, Cremaschi, Marco, Fersini, Elisabetta, Hutchison, David, Series Editor, Kanade, Takeo, Series Editor, Kittler, Josef, Series Editor, Kleinberg, Jon M., Series Editor, Mattern, Friedemann, Series Editor, Mitchell, John C., Series Editor, Naor, Moni, Series Editor, Pandu Rangan, C., Series Editor, Steffen, Bernhard, Series Editor, Terzopoulos, Demetri, Series Editor, Tygar, Doug, Series Editor, Weikum, Gerhard, Series Editor, Blomqvist, Eva, editor, Maynard, Diana, editor, Gangemi, Aldo, editor, Hoekstra, Rinke, editor, Hitzler, Pascal, editor, and Hartig, Olaf, editor

Published
2017
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38. Exposing Open Street Map in the Linked Data Cloud

Author

Anelli, Vito Walter, Calì, Andrea, Di Noia, Tommaso, Palmonari, Matteo, Ragone, Azzurra, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Fujita, Hamido, editor, Ali, Moonis, editor, Selamat, Ali, editor, Sasaki, Jun, editor, and Kurematsu, Masaki, editor

Published
2016
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39. ABSTAT: Ontology-Driven Linked Data Summaries with Pattern Minimalization

Author

Spahiu, Blerina, Porrini, Riccardo, Palmonari, Matteo, Rula, Anisa, Maurino, Andrea, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Sack, Harald, editor, Rizzo, Giuseppe, editor, Steinmetz, Nadine, editor, Mladenić, Dunja, editor, Auer, Sören, editor, and Lange, Christoph, editor

Published
2016
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40. ABSTAT: Linked Data Summaries with ABstraction and STATistics

Author

Palmonari, Matteo, Rula, Anisa, Porrini, Riccardo, Maurino, Andrea, Spahiu, Blerina, Ferme, Vincenzo, Hutchison, David, Editorial Board Member, Kanade, Takeo, Editorial Board Member, Kittler, Josef, Editorial Board Member, Kleinberg, Jon M., Editorial Board Member, Mattern, Friedemann, Editorial Board Member, Mitchell, John C., Editorial Board Member, Naor, Moni, Editorial Board Member, Pandu Rangan, C., Editorial Board Member, Terzopoulos, Demetri, Editorial Board Member, Tygar, Doug, Editorial Board Member, Weikum, Gerhard, Series Editor, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Gandon, Fabien, editor, Guéret, Christophe, editor, Villata, Serena, editor, Breslin, John, editor, Faron-Zucker, Catherine, editor, and Zimmermann, Antoine, editor

Published
2015
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41. DaCENA: Serendipitous News Reading with Data Contexts

Author

Palmonari, Matteo, Uboldi, Giorgio, Cremaschi, Marco, Ciminieri, Daniele, Bianchi, Federico, Hutchison, David, Editorial Board Member, Kanade, Takeo, Editorial Board Member, Kittler, Josef, Editorial Board Member, Kleinberg, Jon M., Editorial Board Member, Mattern, Friedemann, Editorial Board Member, Mitchell, John C., Editorial Board Member, Naor, Moni, Editorial Board Member, Pandu Rangan, C., Editorial Board Member, Terzopoulos, Demetri, Editorial Board Member, Tygar, Doug, Editorial Board Member, Weikum, Gerhard, Series Editor, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Gandon, Fabien, editor, Guéret, Christophe, editor, Villata, Serena, editor, Breslin, John, editor, Faron-Zucker, Catherine, editor, and Zimmermann, Antoine, editor

Published
2015
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42. Upper Bound for Cross-Lingual Concept Mapping with External Translation Resources

Author

Abu Helou, Mamoun, Palmonari, Matteo, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Biemann, Chris, editor, Handschuh, Siegfried, editor, Freitas, André, editor, Meziane, Farid, editor, and Métais, Elisabeth, editor

Published
2015
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44. Estimating Link Confidence for Human-in-the-Loop Table Annotation

Author

Avogadro, R, Ciavotta, M, De Paoli, F, Palmonari, M, Roman, D, Avogadro, Roberto, Ciavotta, Michele, De Paoli, Flavio, Palmonari, Matteo, Roman, Dumitru, Avogadro, R, Ciavotta, M, De Paoli, F, Palmonari, M, Roman, D, Avogadro, Roberto, Ciavotta, Michele, De Paoli, Flavio, Palmonari, Matteo, and Roman, Dumitru

Abstract

Annotating tables with high-quality semantic labels and linking cells in these tables to entities in reference knowledge graphs (KGs) serve several downstream applications such as knowledge graph construction, data enrichment, data analytics, and so on. There is an increasing interest in combining algorithms for the automatic annotation of tables with interactive applications that let users control and improve the annotations and use them for data enrichment. In this paper, we propose a supervised entity linking network that behaves as a re-ranker and score normalizer of scores returned by off-the-shelves entity retrieval systems. By combining these refined scores with additional signals of reliability of the ranking obtained thereof, we can estimate a confidence score, which tells how reliable is the decision to link a cell to the top-ranked candidate entity. The confidence score is used to decide whether to establish a link or not and to prioritize the cells to be revised by the users to improve the annotation quality. Experiments suggest that the proposed approach provides an effective approach to identifying most critical decisions and supporting link revision within a human-in-the-Ioop data enrichment paradigm.

Published
2023

45. Domain Adaptation in Fine-grained Entity Typing

Author

Vimercati, M, CIOCCA, GIANLUIGI, PALMONARI, MATTEO LUIGI, VIMERCATI, MANUEL, Vimercati, M, CIOCCA, GIANLUIGI, PALMONARI, MATTEO LUIGI, and VIMERCATI, MANUEL

Abstract

Questa tesi di dottorato si concentra su una sottotarea dell'attività di Entity Extraction, in particolare sullo step di Classificazione, denominato Entity Typing (ET). L'ET consiste nell'assegnare tipi ad una entita' trovata in una determinata frase. I tipi devono essere scelti da un vocabolario dato. Fine-grained Entity Typing è il nome dato a un problema di ET con una grande quantità di tipi (da decine a centinaia), di solito organizzati in una gerarchia. In questa tesi di dottorato, viene studiato FET sotto il punto di vista dell'adattamento ad un nuovo dominio, definendo un nuovo task. Il problema non è stato affrontato prima in FET, poiché la letteratura si concentra sulla proposta di encoder sofisticati, classificatori e metodi di eliminazione del rumore applicati a FET mantenendo fisso il dominio. L'adattamento ad un nuovo dominio è necessario quando un modello FET addeastrato, in grado di classificare le menzioni trovate in una determinata frase con tipi estratti da una gerarchia data, deve essere utilizzato in un dominio diverso da quello di addestramento. L'adattamento ad un nuovo dominio riguarda casi d'uso reali (riutilizzo del modello, specializzazione del modello e completo adattamento al dominio) formalmente descritti e indagati nella tesi. L'adattamento ad un nuovo dominio è generalmente sottoinvestigato nell'ambito di Information Extraction, anche se gli scenario di adattamento ad un nuovo dominio sono comuni nella progettazione di sistemi di estrazione di informazioni nel mondo reale. L'adattamento da un dominio sorgente a un dominio target FET è altamente correlato ai tipi presenti nelle gerarchie di tipi sorgente e target, poiché durante il processo di adattamento il modello FET è destinato a imparare i nuovi tipi target sfruttando le sue capacità sui tipi sorgente. Le tecniche di adattamento proposte in questa tesi sono basate sulla individuazione esplicita di relazioni di equivalenza, generalizzazione, specializzazione o disgiunzion, This Ph.D. thesis focuses on a subtask of entity extraction, particularly of the Classification step, named Entity Typing (ET). ET is the task of assigning types to an entity already found in a given sentence. Types have to be chosen from a given vocabulary. Fine-grained Entity Typing is the name given to an ET problem with a large amount of types (from dozens to hundreds), usually organized in a hierarchy. In this Ph.D. thesis, FET is studied under the lenses of Domain Adaptation, defining a novel task. The problem has not been addressed before in FET, since the literature is focused on proposing sophisticated encoders, classifiers, and denoising methods applied to in-domain FET. Domain Adaptation is needed when a trained FET model, able to classify mentions found in a given sentence with types from a given hierarchy, has to be used in a domain that differs from the training one. Domain adaptation concerns real use cases (model reuse, model specialization, and full-fledged domain adaptation) formally described and investigated in the thesis. Domain adaptation is generally underinvestigated in information extraction, even if the described domain adaptation scenarios are common in the design of real world information extraction systems. The adaptation from a source to a target FET domain is highly related to the types present in the source and target type hierarchies, since during the adaptation process the FET model is expected to learn the new target types by exploiting its capabilities on source types. The adaptation techniques proposed in this thesis are based on the exlpicit individuation of equivalence, generalization, specialization or disjunction relations between source and target types, such relations are injected during the adaptation process with a neurosymbolic integration approach from literature, i.e., Knowledge Enhanced Neural Network (KENN). Moreover, the approaches proposed to face domain adaptation in FET reflect the first attempt to use a neuro-sy

Published
2023

48. Pay-As-You-Go Multi-user Feedback Model for Ontology Matching

Author

Cruz, Isabel F., Loprete, Francesco, Palmonari, Matteo, Stroe, Cosmin, Taheri, Aynaz, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Kobsa, Alfred, Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Goebel, Randy, Series editor, Tanaka, Yuzuru, Series editor, Wahlster, Wolfgang, Series editor, Siekmann, Jörg, Series editor, Janowicz, Krzysztof, editor, Schlobach, Stefan, editor, Lambrix, Patrick, editor, and Hyvönen, Eero, editor

Published
2014
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49. Extracting Facets from Lost Fine-Grained Categorizations in Dataspaces

Author

Porrini, Riccardo, Palmonari, Matteo, Batini, Carlo, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Kobsa, Alfred, editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Weikum, Gerhard, editor, Jarke, Matthias, editor, Mylopoulos, John, editor, Quix, Christoph, editor, Rolland, Colette, editor, Manolopoulos, Yannis, editor, Mouratidis, Haralambos, editor, and Horkoff, Jennifer, editor

Published
2014
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50. Hybrid Acquisition of Temporal Scopes for RDF Data

Author

Rula, Anisa, Palmonari, Matteo, Ngonga Ngomo, Axel-Cyrille, Gerber, Daniel, Lehmann, Jens, Bühmann, Lorenz, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Kobsa, Alfred, editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Weikum, Gerhard, editor, Presutti, Valentina, editor, d’Amato, Claudia, editor, Gandon, Fabien, editor, d’Aquin, Mathieu, editor, Staab, Steffen, editor, and Tordai, Anna, editor

Published
2014
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