Search Results for author: Chiara Ghidini

Found 16 papers, 0 papers with code

Leveraging pre-trained language models for conversational information seeking from text

no code implementations31 Mar 2022 Patrizio Bellan, Mauro Dragoni, Chiara Ghidini

Recent advances in Natural Language Processing, and in particular on the construction of very large pre-trained language representation models, is opening up new perspectives on the construction of conversational information seeking (CIS) systems.

Few-Shot Learning

PET: A new Dataset for Process Extraction from Natural Language Text

no code implementations9 Mar 2022 Patrizio Bellan, Han van der Aa, Mauro Dragoni, Chiara Ghidini, Simone Paolo Ponzetto

Although there is a long tradition of work in NLP on extracting entities and relations from text, to date there exists little work on the acquisition of business processes from unstructured data such as textual corpora of process descriptions.

Computing unsatisfiable cores for LTLf specifications

no code implementations9 Mar 2022 Marco Roveri, Claudio Di Ciccio, Chiara Di Francescomarino, Chiara Ghidini

In this paper, we investigate the problem of extracting the unsatisfiable core in LTLf specifications.

Explainable Predictive Process Monitoring: A User Evaluation

no code implementations15 Feb 2022 Williams Rizzi, Marco Comuzzi, Chiara Di Francescomarino, Chiara Ghidini, Suhwan Lee, Fabrizio Maria Maggi, Alexander Nolte

The results of the user evaluation show that, although explanation plots are overall understandable and useful for decision making tasks for Business Process Management users -- with and without experience in Machine Learning -- differences exist in the comprehension and usage of different plots, as well as in the way users with different Machine Learning expertise understand and use them.

Decision Making Predictive Process Monitoring

Exploring Business Process Deviance with Sequential and Declarative Patterns

no code implementations24 Nov 2021 Giacomo Bergami, Chiara Di Francescomarino, Chiara Ghidini, Fabrizio Maria Maggi, Joonas Puura

Business process deviance refers to the phenomenon whereby a subset of the executions of a business process deviate, in a negative or positive way, with respect to {their} expected or desirable outcomes.

Process Extraction from Text: state of the art and challenges for the future

no code implementations7 Oct 2021 Patrizio Bellan, Mauro Dragoni, Chiara Ghidini

Automatic Process Discovery aims at developing algorithmic methodologies for the extraction and elicitation of process models as described in data.

Process discovery on deviant traces and other stranger things

no code implementations30 Sep 2021 Federico Chesani, Chiara Di Francescomarino, Chiara Ghidini, Daniela Loreti, Fabrizio Maria Maggi, Paola Mello, Marco Montali, Sergio Tessaris

As the need to understand and formalise business processes into a model has grown over the last years, the process discovery research field has gained more and more importance, developing two different classes of approaches to model representation: procedural and declarative.

A formalisation of BPMN in Description Logics

no code implementations22 Sep 2021 Chiara Ghidini, Marco Rospocher, Luciano Serafini

In this paper we present a textual description, in terms of Description Logics, of the BPMN Ontology, which provides a clear semantic formalisation of the structural components of the Business Process Modelling Notation (BPMN), based on the latest stable BPMN specifications from OMG [BPMN Version 1. 1 -- January 2008].

How do I update my model? On the resilience of Predictive Process Monitoring models to change

no code implementations8 Sep 2021 Williams Rizzi1, Chiara Di Francescomarino, Chiara Ghidini, Fabrizio Maria Maggi

Existing well investigated Predictive Process Monitoring techniques typically construct a predictive model based on past process executions, and then use it to predict the future of new ongoing cases, without the possibility of updating it with new cases when they complete their execution.

Incremental Learning Predictive Process Monitoring

Solving reachability problems on data-aware workflows

no code implementations27 Sep 2019 Riccardo De Masellis, Chiara Di Francescomarino, Chiara Ghidini, Sergio Tessaris

Recent advances in the field of Business Process Management have brought about several suites able to model complex data objects along with the traditional control flow perspective.

Incremental Predictive Process Monitoring: How to Deal with the Variability of Real Environments

no code implementations11 Apr 2018 Chiara Di Francescomarino, Chiara Ghidini, Fabrizio Maria Maggi, Williams Rizzi, Cosimo Damiano Persia

The results provide a first evidence of the potential of incremental learning strategies for predicting process monitoring in real environments, and of the impact of different case encoding strategies in this setting.

Incremental Learning Predictive Process Monitoring

Enhancing workflow-nets with data for trace completion

no code implementations1 Jun 2017 Riccardo De Masellis, Chiara Di Francescomarino, Chiara Ghidini, Sergio Tessaris

The growing adoption of IT-systems for modeling and executing (business) processes or services has thrust the scientific investigation towards techniques and tools which support more complex forms of process analysis.

Using Recurrent Neural Network for Learning Expressive Ontologies

no code implementations14 Jul 2016 Giulio Petrucci, Chiara Ghidini, Marco Rospocher

Recently, Neural Networks have been proven extremely effective in many natural language processing tasks such as sentiment analysis, question answering, or machine translation.

Machine Translation Question Answering +2

Abducing Compliance of Incomplete Event Logs

no code implementations17 Jun 2016 Federico Chesani, Riccardo De Masellis, Chiara Di Francescomarino, Chiara Ghidini, Paola Mello, Marco Montali, Sergio Tessaris

The capability to store data about business processes execution in so-called Event Logs has brought to the diffusion of tools for the analysis of process executions and for the assessment of the goodness of a process model.

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