Foundations of logic programming; (2nd extended ed.)
Foundations of logic programming; (2nd extended ed.)
Logic programming and databases
Logic programming and databases
New Generation Computing - Selected papers from the international workshop on algorithmic learning theory,1990
WordNet: a lexical database for English
Communications of the ACM
An Information-Theoretic Definition of Similarity
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Introduction to the special issue on word sense disambiguation: the state of the art
Computational Linguistics - Special issue on word sense disambiguation
A Generalization Model Based on OI-implication for Ideal Theory Refinement
Fundamenta Informaticae - Intelligent Systems
A General Similarity Framework for Horn Clause Logic
Fundamenta Informaticae
A taxonomic generalization technique for natural language processing
ISMIS'11 Proceedings of the 19th international conference on Foundations of intelligent systems
Plugging numeric similarity in first-order logic horn clauses comparison
AI*IA'11 Proceedings of the 12th international conference on Artificial intelligence around man and beyond
Improving robustness and flexibility of concept taxonomy learning from text
NFMCP'12 Proceedings of the First international conference on New Frontiers in Mining Complex Patterns
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Horn clause Logic is a powerful representation language exploited in Logic Programming as a computer programming framework and in Inductive Logic Programming as a formalism for expressing examples and learned theories in domains where relations among objects must be expressed to fully capture the relevant information. While the predicates that make up the description language are defined by the knowledge engineer and handled only syntactically by the interpreters, they sometimes express information that can be properly exploited only with reference to a taxonomic background knowledge in order to capture unexpressed and underlying relationships among the concepts described. This is typical when the representation predicates are not purposely engineered but rather derive from the particular words found in a text. This work proposes the exploitation of a taxonomic background knowledge to better assess the similarity between two First-Order Logic (Horn clause) descriptions, beyond the simple syntactical matching between predicates. To this aim, an existing distance framework is extended by applying the underlying distance measure also to parameters coming from the taxonomic background knowledge. The viability of the solution is demonstrated on sample problems.