Gianluca Cima
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View article: Advances in Logic-Based Entity Resolution: Enhancing ASPEN with Local Merges and Optimality Criteria
Advances in Logic-Based Entity Resolution: Enhancing ASPEN with Local Merges and Optimality Criteria Open
In this paper, we present ASPEN+, which extends an existing ASP-based system, ASPEN,for collective entity resolution with two important functionalities: support for local merges and new optimality criteria for preferred solutions. Indeed, …
Recent Advances in Logic-Based Entity Resolution Open
Entity resolution (ER) is a central task in data quality, which is concerned with identifying pairs of distinct constants or tuples that refer to the same real-world entity. Declarative approaches, based upon logical rules and constraints,…
Ontology-Based Schema-Level Data Quality: The Case of Consistency Open
The quality of metadata plays a crucial role in many data FAIRification processes. So much so, in fact, that all the four main principles of data FAIRification prescribe the use of high-quality metadata. One of the main data management par…
Indistinguishability in controlled query evaluation over prioritized description logic ontologies Open
In this paper we study Controlled Query Evaluation (CQE), a declarative approach to privacy-preserving query answering over databases, knowledge bases, and ontologies. CQE is based on the notion of censor, which defines the answers to each…
View article: ASPEN: ASP-Based System for Collective Entity Resolution
ASPEN: ASP-Based System for Collective Entity Resolution Open
In this paper, we present ASPEN, an answer set programming (ASP) implementation of a recently proposed declarative framework for collective entity resolution (ER). While an ASP encoding had been previously suggested, several practical issu…
View article: ASPEN: ASP-Based System for Collective Entity Resolution
ASPEN: ASP-Based System for Collective Entity Resolution Open
In this paper, we present ASPEN, an answer set programming (ASP) implementation of a recently proposed declarative framework for collective entity resolution (ER). While an ASP encoding had been previously suggested, several practical issu…
DualCast: A Model to Disentangle Aperiodic Events from Traffic Series Open
Traffic forecasting is crucial for transportation systems optimisation. Current models minimise the mean forecasting errors, often favouring periodic events prevalent in the training data, while overlooking critical aperiodic ones like tra…
What Does a Query Answer Tell You? Informativeness of Query Answers for Knowledge Bases Open
Query answering for Knowledge Bases (KBs) amounts to extracting information from the various models of a KB, and presenting the user with an object that represents such information. In the vast majority of cases, this object consists of th…
View article: REPLACE: A Logical Framework for Combining Collective Entity Resolution and Repairing
REPLACE: A Logical Framework for Combining Collective Entity Resolution and Repairing Open
This paper considers the problem of querying dirty databases, which may contain both erroneous facts and multiple names for the same entity. While both of these data quality issues have been widely studied in isolation, our contribution is…
View article: Combining Global and Local Merges in Logic-based Entity Resolution
Combining Global and Local Merges in Logic-based Entity Resolution Open
In the recently proposed LACE framework for collective entity resolution, logical rules and constraints are used to identify pairs of entity references (e.g. author or paper ids) that denote the same entity. This identification is global: …
The notion of Abstraction in Ontology-based Data Management Open
We study a novel reasoning task in Ontology-based Data Management (OBDM), called Abstraction, which aims at associating formal semantic descriptions to data services. In OBDM a domain ontology is used to provide a semantic layer mapped to …
A review of data abstraction Open
It is well-known that Artificial Intelligence (AI), and in particular Machine Learning (ML), is not effective without good data preparation, as also pointed out by the recent wave of data-centric AI. Data preparation is the process of gath…
Separability and Its Approximations in Ontology-based Data Management Open
Given two datasets, i.e., two sets of tuples of constants, representing positive and negative examples, logical separability is the reasoning task of finding a formula in a certain target query language that separates them. As already poin…
View article: Combining Global and Local Merges in Logic-based Entity Resolution
Combining Global and Local Merges in Logic-based Entity Resolution Open
In the recently proposed Lace framework for collective entity resolution, logical rules and constraints are used to identify pairs of entity references (e.g. author or paper ids) that denote the same entity. This identification is global: …
View article: Analysis of Relationship between Training Load and Recovery Status in Adult Soccer Players: a Machine Learning Approach
Analysis of Relationship between Training Load and Recovery Status in Adult Soccer Players: a Machine Learning Approach Open
Periods of intensified training may increase athletes’ fatigue and impair their recovery status. Therefore, understanding internal and external load markers-related to fatigue is crucial to optimize their weekly training loads. The current…
CQE in OWL 2 QL: A "Longest Honeymoon" Approach (extended version) Open
Controlled Query Evaluation (CQE) has been recently studied in the context of Semantic Web ontologies. The goal of CQE is concealing some query answers so as to prevent external users from inferring confidential information. In general, th…
View article: LACE: A Logical Approach to Collective Entity Resolution
LACE: A Logical Approach to Collective Entity Resolution Open
In this paper, we revisit the problem of entity resolution and propose a novel, logical framework, LACE, which mixes declarative and procedural elements to achieve a number of desirable properties. Our approach is fundamentally declarative…
View article: LACE: A Logical Approach to Collective Entity Resolution
LACE: A Logical Approach to Collective Entity Resolution Open
International audience
Monotone Abstractions in Ontology-Based Data Management Open
International audience
Predictive Analytic Techniques to Identify Hidden Relationships between Training Load, Fatigue and Muscle Strains in Young Soccer Players Open
This study aimed to analyze different predictive analytic techniques to forecast the risk of muscle strain injuries (MSI) in youth soccer based on training load data. Twenty-two young soccer players (age: 13.5 ± 0.3 years) were recruited, …
Controlled Query Evaluation over Ontologies through Policies with Numerical Restrictions Open
We study Controlled Query Evaluation (CQE), a declarative approach to privacy-preserving query answering. In particular, we focus on the application of CQE to ontologies and analyze the possibility of using role cardinality restrictions in…
A Data Mining Approach to Predict Non-Contact Injuries in Young Soccer Players Open
Predicting and avoiding an injury is a challenging task. By exploiting data mining techniques, this paper aims to identify existing relationships between modifiable and non-modifiable risk factors, with the final goal of predicting non-con…
Query Definability and Its Approximations in Ontology-based Data Management Open
Given an input dataset (i.e., a set of tuples), query definability in Ontology-based Data Management (OBDM) amounts to finding a query over the ontology whose certain answers coincide with the tuples in the given dataset. We refer to such …
QDEF and Its Approximations in OBDM Open
Given an input dataset (i.e., a set of tuples), query definability in Ontology-based Data Management (OBDM) amounts to find a query over the ontology whose certain answers coincide with the tuples in the given dataset. We refer to such a q…
Abstraction in Data Integration Open
International audience
On Information Disclosure in Ontology-based Data Access (Extended Abstract). Open
This extended abstract summarizes our recent work about Controlled Query Evaluation over Ontology-based data access systems.