Document image understanding denotes the recognition of semantically relevant components in the l... more Document image understanding denotes the recognition of semantically relevant components in the layout extracted from a document image. This recognition process is based on some visual models, whose manual specification can be a highly demanding task. In order to ...
... Therefore in order to answer a query it is necessary to collect enough derivations ending wit... more ... Therefore in order to answer a query it is necessary to collect enough derivations ending with a constrained empty clause such that every model of B satisfies the constraints associated with the final query of ... 2 Semantic Web Mining with AC-QuIn ... 'Arab':MiddleEasternEthnicGroup ...
Abstract INGENS is a prototypical GIS which integrates machine learning tools in order to discove... more Abstract INGENS is a prototypical GIS which integrates machine learning tools in order to discover geographic knowledge useful for the task of topographic map interpretation. It embeds ATRE, a novel learning system that can induce recursive logic theories from a set ...
ABSTRACT ILP is a major approach to Relational Learning that exploits previous results in concept... more ABSTRACT ILP is a major approach to Relational Learning that exploits previous results in concept learning and is characterized by the use of prior conceptual knowledge. An increasing amount of conceptual knowledge is being made available in the form of ontologies, mainly formalized with Description Logics (DLs). In this paper we consider the problem of learning rules from observations that combine relational data and ontologies, and identify the ingredients of an ILP solution to it. Our proposal relies on the expressive and deductive power of the KR framework DL\mathcal{DL} +log that allows for the tight integration of DLs and disjunctive Datalog with negation. More precisely we adopt an instantiation of this framework which integrates the DL SHIQ\mathcal{SHIQ} and positive Datalog. We claim that this proposal lays the foundations of an extension of Relational Learning, called Onto-Relational Learning, to account for ontologies.
Document image understanding denotes the recognition of semantically relevant components in the l... more Document image understanding denotes the recognition of semantically relevant components in the layout extracted from a document image. This recognition process is based on some visual models, whose manual specification can be a highly demanding task. In order to ...
... Therefore in order to answer a query it is necessary to collect enough derivations ending wit... more ... Therefore in order to answer a query it is necessary to collect enough derivations ending with a constrained empty clause such that every model of B satisfies the constraints associated with the final query of ... 2 Semantic Web Mining with AC-QuIn ... 'Arab':MiddleEasternEthnicGroup ...
Abstract INGENS is a prototypical GIS which integrates machine learning tools in order to discove... more Abstract INGENS is a prototypical GIS which integrates machine learning tools in order to discover geographic knowledge useful for the task of topographic map interpretation. It embeds ATRE, a novel learning system that can induce recursive logic theories from a set ...
ABSTRACT ILP is a major approach to Relational Learning that exploits previous results in concept... more ABSTRACT ILP is a major approach to Relational Learning that exploits previous results in concept learning and is characterized by the use of prior conceptual knowledge. An increasing amount of conceptual knowledge is being made available in the form of ontologies, mainly formalized with Description Logics (DLs). In this paper we consider the problem of learning rules from observations that combine relational data and ontologies, and identify the ingredients of an ILP solution to it. Our proposal relies on the expressive and deductive power of the KR framework DL\mathcal{DL} +log that allows for the tight integration of DLs and disjunctive Datalog with negation. More precisely we adopt an instantiation of this framework which integrates the DL SHIQ\mathcal{SHIQ} and positive Datalog. We claim that this proposal lays the foundations of an extension of Relational Learning, called Onto-Relational Learning, to account for ontologies.
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Papers by Lisi Francesca