Category: Meetings

Comparison of definitions and values

Alizia Mantovani will present a survey about analogies and differences between Geoheritage and of Geodiversity.

In literature, the relation between geoheritage and geodiversity is well rooted. In fact, the term geodiversity is always present in papers that concern geoheritage. However, the methods of assessment and the characteristics that describe them have different approaches and use terms that are sometimes similar and sometimes different. In literature, it is possible to find different lists of values that characterize the elements of geoheritage, as well as there is a system of characterization of the services. Those lists, even if applied to concepts that are often associated, are rarely associated and cross-used. In this talk, she will explore different points of view on the characterization of geoheritage and of geodiversity through the comparison of the assessment approaches, and how they are similar and different. 

When: 08/04/2022

Where: in presence and online

Simone Bocca (University of Trento), will present iTelos – A methodology for building reusable purpose-specific Knowledge Graphs.

Knowledge Graphs (KGs) have become more and more popular in recent years, due to their efficiency in handling, representing and integrating information. Within different areas of interest KGs are exploited, for several objectives, by applications, services, as well as data analysis and visualization. Such popularity increased the need of building KGs for many different purposes stated by users, sometimes, without a clear understanding about the several issues to be addressed while building a KG. We propose iTelos, a KG building methodology designed to support the user in resolving those issues. In other words, iTelos aims to reduce the effort in building KGs as suitable as possible for the purpose expressed by the final users. To this end, the methodology is based on two key ideas; (i) to stratify the resources involved into different semantic interoperability levels, in order to deal with multiple types of data heterogeneity; (ii) to enhance as much as possible the reuse of already existing data and knowledge resources during the KG building process, thus reducing the effort required for the construction, and producing in turn highly reusable resources. iTelos is currently taught in the Knowledge and Data Integration (KDI) master course in University of Trento (Italy) and Jilin University (China), as well as adopted in EU projects by KnowDive group (University of Trento, Department of Information engineering and Computer Science).

Related links:

Giunchiglia, F., Bocca, S., Fumagalli, M., Bagchi, M., & Zamboni, A. (2021). iTelos–Purpose Driven Knowledge Graph Generation. arXiv preprint arXiv:2105.09418.
https://arxiv.org/abs/2105.09418

Giunchiglia, F., Zamboni, A., Bagchi, M., & Bocca, S. (2021). Stratified data integration. arXiv preprint arXiv:2105.09432. https://arxiv.org/abs/2105.09432

Giunchiglia, F., Khuyagbaatar B., Gabor, B.: Understanding and exploiting language diversity. In: IJCAI (2017) https://www.ijcai.org/proceedings/2017/0560.pdf

When: 25/03/22

Where: online and in presence

Rossana Damiano, Stefano De Martino (Dipartimento di Studi Storici) and Elena Devecchi (Dipartimento di Studi Storici) will present the findings of an interesting investigation performed in the PRIN project  “Writing uses: Transmission of Knowledge, Administrative Practices and Political Control in Anatolian and Syro-Anatolian Polities in the II and I millennium BC”.

Title: Models and vocabularies for ancient Near Eastern prosopographies 

Prosopographies, intended as the large scale study of the people’s life events as they emerge from written sources, have been largely used in the last decade to study the social structure of ancient societies. The analysis of professional, kinship, administrative and political relations of the past, informed on real data, can confirm the models put forth by historians and archaeologists through traditional research paradigms, and in some cases suggest new ones. In this sense, the availability of agreed-upon, formally expressed vocabularies for describing these data and the relations to sources is a key factor to the development of methods for the analysis of social networks from the past in support of the work of historians. 

The PRIN project  “Writing uses: Transmission of Knowledge, Administrative Practices and Political Control in Anatolian and Syro-Anatolian Polities in the II and I millennium BC.” (2020-2022) has investigated the adaptation of a factoid-based model of prosopographic data to the case study of Hittite and Kassite civilizations. Aimed at the collaborative creation of prosopographic datasets for large-scale study of Hittite and Kassite social networks, the project has collected a corpus of person records and relations in a Linked Data format. In this seminar, she will describe the design of the vocabularies for the construction of the datasets and the research methods being developed from these data.  

Related links:

https://hfpo.di.unito.it/

https://kfpo.di.unito.it/

When: 11/03/2022

Where: online

Antonio Lieto

Inventing novel knowledge to solve problems is a crucial, creative, mechanism employed by humans, to extend their range of action. In this talk, I will show how commonsense reasoning plays a crucial role in this respect. In particular, I will present a cognitively inspired reasoning framework for knowledge invention and creative problem solving exploiting TCL: a probabilistic non-monotonic extension of a Description Logic (DL) of typicality able to combine prototypical (commonsense) descriptions of concepts in a human-like fashion. The proposed approach has been tested in a variety of fields and applications. I will present the obtained results, the lessons learned, and the road ahead of this research path.

See this page https://www.antoniolieto.net/tcl_logic.html for a list of the main papers and applications

Francesco Di Ciccio will discuss the importance to make interpretable the models, showing a first experiment with the Layer-wise Relevance Propagation (LRP) technique.

Title: Interpretability of deep neural networks for computer vision

Machine learning methods are widely used in both commercial applications and academia in order to make inferences in a wide range of areas. Extracting information from data in order to make accurate predictions is the predominant goal in many such applications, although it could come at the cost of the explainability of the applied model. A focus in the design of a model should be placed on implementing tools that assist the understanding of: output results with respect to the parameters of the model and to their consistency to the domain knowledge; choice of the hyperparameters and their interpretation with respect to the domain of the application. In some domains more than others the importance of choosing the correct features is especially important, such as in medicine or economics, where decisions are made on the assumption that the predicted results are obtained from a proper representation of the problem at hand. In such fields, there is a strong reliance on the interpretability of the model, given the higher interest in knowing ‘Why?’ and further identifying those parameters that caused the model to make such predictions. The interest in the interpretability of models is also shared by other fields, such as computer vision, which for simplicity will be used as a reference to investigate the functioning of the Layer-wise Relevance Propagation (LRP) technique. In this application, the focus is on a simple classification task with the MNIST dataset.

When: December 03 at 11.30

Where: in presentia (032_A_P03_3140) or online

Valerio Basile will introduce a new automatic method to identify annotators’ perspectives in controversial issues such as Hate Speech.

Title: Mining Annotator Perspectives from Hate Speech Corpora

Disagreement in annotation, traditionally treated mostly as noise, is now more and more often considered as a source of valuable information instead. He investigated a particular form of disagreement, occurring when the focus of an annotated dataset is a subjective and controversial phenomenon, therefore inducing a certain degree of polarization among the annotators’ judgments. He argued that the polarization is indicative of the conflicting perspectives held by different annotator groups, and propose a quantitative method to model this phenomenon. Moreover, he introduced a method to automatically identify shared perspectives stemming from a common background.
He tested this method on several corpora in English and Italian, manually annotated according to their hate speech content, validating prior knowledge about the groups of annotators, when available, and discovering characteristic traits among annotators with unknown background.
He found numerous precisely defined perspectives, described in terms of increased sensitivity towards textual content expressing attitudes such as xenophobia, Islamophobia, and homophobia.

When: February 11 at 11.30

Where: online

Muhammad Saad Amin will talk about the problems related to Sign Language Recognition, from the creation of the dataset to the design of the algorithm.

Title: Sign Language Recognition using Machine Learning

Human gesture classification and recognition is always a challenging task. Capturing human gestures and transforming these gestures into labeled digital data is mainly required to train supervised Machine Learning algorithms. Hence, SLR systems with improved accuracy and increased efficiency are the need of the hour.

In this seminar, he will discuss how to capture gestures (specifically ASL) using sensor-based prototypes? How to convert these sign gestures into digital data (dataset generation)? And how a dataset can be used for SL recognition using Supervised Machine Learning algorithms?

When: November 19 at 11.30

Where: in presentia (032_A_P03_3140) or online

With Convolutional Neural Networks

Elisa Di Nuovo and Marco Siino will present an interesting approach used to detect hate speech spreaders in the context of the shared task Profiling Hate Speech Spreaders (HSSs) proposed at PAN 2021.

Title: Detection of hate speech spreaders using convolutional neural networks

The speakers will describe a deep learning model based on a Convolutional Neural Network (CNN) to profile hate speech spreaders online. The model was developed for the Profiling Hate Speech Spreaders (HSSs) task proposed by PAN 2021 organizers and hosted at the 2021 CLEF Conference. The approach, used to classify an author as HSS or not (nHSS), takes advantage of a CNN based on a single convolutional layer. In this binary classification task, on the tests performed using a 5-fold cross validation, the proposed model reaches a maximum accuracy of 0.80 on the multilingual (i.e., English and Spanish) training set, and a minimum loss value of 0.51 on the same set. As announced by the task organizers, the model won the 2021 PAN competition on profiling HSSs, reaching an overall accuracy of 0.79 on the full test set. This overall accuracy is obtained averaging the accuracy achieved by the model on both languages. In particular, with regard to the Spanish test set the model achieves an accuracy of 0.85, while on the English test set the same model achieved an accuracy of 0.73.

When: On 22nd October at 11.30 am

Where: https://unito.webex.com/webappng/sites/unito/meeting/info/910eaf7ad0534d1ba92c5dde0a66a9a7_20211022T093000Z

Paper: http://ceur-ws.org/Vol-2936/paper-189.pdf

Mirko Lai and Marco A. Stranisci will present an innovative approach that takes into account the morality and communicative behaviour of the users to profile hate speech spreaders online.

Title: HaMor at the Profiling Hate Speech Spreaders on Twitter

In this talk, they will describe the Hate and Morality (HaMor) submission for the Profiling of Hate Speech Spreaders on Twitter, the shared task at PAN 2021.
HaMor ranked as the 19th position – over 66 participating teams – with an averaged accuracy value of 73% reached over the two languages.
This approach obtained the 43th higher accuracy for English (62%) and the 2nd higher accuracy for Spanish (84%).
In particular, it involves four types of features that help the system to infer users attitudes just from their messages: hate speech detection, users morality, named entities, and communicative behaviour.
The results of their experiments are promising and will lead to future investigations of these features in a finer grained perspective.

When: On 5th November at 11.30 am

Where: https://unito.webex.com/webappng/sites/unito/meeting/info/910eaf7ad0534d1ba92c5dde0a66a9a7_20211022T093000Z

Paper: http://ceur-ws.org/Vol-2936/paper-178.pdf

Combining Contextual Information with Textual and Dependency-Based Syntactic Features for Stance Detection

Mirko Lai and Alessandra T. Cignarella will present an innovative approach to detect stance online, proposed in the frame of the VaxxStance shared task.

Title: WordUp! at VaxxStance 2021: Combining Contextual Information with Textual and Dependency-Based Syntactic Features for Stance Detection

In this talk, they will describe the participation of the WordUp! team in the VaxxStance shared task at IberLEF 2021. The goal of the competition is to determine the author’s stance from tweets written both in Spanish and Basque on the topic of the Antivaxxers movement. Their approach, in the four different tracks proposed, combines the Logistic Regression classifier with diverse groups of features: stylistic, tweet-based, user-based, lexicon-based, dependency-based, and network-based. The outcomes of their experiments are in line with state-of-the-art results on other languages, proving the efficacy of combining methods derived from NLP and Network Science for detecting stance in Spanish and Basque.

When: On 8th October at 11.30 am

Where: https://unito.webex.com/webappng/sites/unito/meeting/info/910eaf7ad0534d1ba92c5dde0a66a9a7_20210702T093000Z?from_login=true

Paper: http://ceur-ws.org/Vol-2943/vaxx_paper3.pdf