1,721,119 research outputs found
Proceedings of the 9th Italian Convention on Computational Logic, Rome, Italy, June 6-7, 2012
No full textAL-QuIn: An Onto-Relational Learning System for Semantic Web Mining
No full textOnto-Relational Learning is an extension of Relational Learning aimed at accounting for ontologies in a clear, well-founded and elegant manner. The system AL-QuIn supports a variant of the frequent pattern discovery task by following the Onto-Relational Learning approach. It takes taxonomic ontologies into account during the discovery process and produces descriptions of a given relational database at multiple granularity levels. The functionalities of the system are illustrated by means of examples taken from a Semantic Web Mining case study concerning the analysis of relational data extracted from the on-line CIA World Fact Book
Inductive Logic Programming in Databases: from Datalog to DL+log
No full textIn this paper we address an issue that has been brought to the attention of the database community with the advent of the Semantic Web, i.e., the issue of how ontologies (and semantics conveyed by them) can help solving typical database problems, through a better understanding of Knowledge Representation (KR) aspects related to databases. In particular, we investigate this issue from the ILP perspective by considering two database problems, (i) the definition of views and (ii) the definition of constraints, for a database whose schema is represented also by means of an ontology. Both can be reformulated as ILP problems and can benefit from the expressive and deductive power of the KR framework DL+LOG. We illustrate the application scenarios by means of examples
Building Rules on Top of Ontologies for the Semantic Web with Inductive Logic Programming
No full textBuilding rules on top of ontologies is the ultimate goal of the logical layer of the Semantic Web. To this aim, an ad-hoc markup language for this layer is currently under discussion. It is intended to follow the tradition of hybrid knowledge representation and reasoning systems, such as AL-log that integrates the description logic ALC and the function-free Horn clausal language DATALOG. In this paper, we consider the problem of automating the acquisition of these rules for the Semantic Web. We propose a general framework for rule induction that adopts the methodological apparatus of Inductive Logic Programming and relies on the expressive and deductive power of AL-log. The framework is valid whatever the scope of induction (description versus prediction) is. Yet, for illustrative purposes, we also discuss an instantiation of the framework which aims at description and turns out to be useful in Ontology Refinement
Recent advances of research on Computational Logic in Italy (CILC 2012): In memoriam of Elio Lanzarone (1945–2011)
No full textWill AI ever support design thinking?
No full textThis paper addresses the question of whether AI will ever support Design Thinking, with a focus on Architecture and Urban Planning, by analyzing the current trends of research in AI and related fields
Inducing Multi-Level Association Rules for Multiple Relations
No full textRecently there has been growing interest both to extend ILP to description logics and to apply it to knowledge discovery in databases. In this paper we present a novel approach to association rule mining which deals with multiple levels of description granularity. It relies on the hybrid language .4.C-IOg which allows a unified treatment of both the relational and structural features of data. A generality order and a downward refinement operator for AC-log pattern spaces is defined on the basis of query subsumption. This framework has been implemented in SPADA, an ILP system, for mining multi-level association rules from spatial data. As an illustrative example, we report experimental results obtained by running the new version of SPADA on geo-referenced census data of Manchester Stockport
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