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Found 6 papers, 1 papers with code
SAT-Based PAC Learning of Description Logic Concepts
We propose bounded fitting as a scheme for learning description logic concepts in the presence of ontologies.
On the non-efficient PAC learnability of conjunctive queries
This note serves three purposes: (i) we provide a self-contained exposition of the fact that conjunctive queries are not efficiently learnable in the Probably-Approximately-Correct (PAC) model, paying clear attention to the complicating fact that this concept class lacks the polynomial-size fitting property, a property that is tacitly assumed in much of the computational learning theory literature; (ii) we establish a strong negative PAC learnability result that applies to many restricted classes of conjunctive queries (CQs), including acyclic CQs for a wide range of notions of "acyclicity"; (iii) we show that CQs (and UCQs) are efficiently PAC learnable with membership queries.
Conjunctive Queries: Unique Characterizations and Exact Learnability
We answer the question which conjunctive queries are uniquely characterized by polynomially many positive and negative examples, and how to construct such examples efficiently.
Learning Multilingual Word Embeddings Using Image-Text Data
There has been significant interest recently in learning multilingual word embeddings -- in which semantically similar words across languages have similar embeddings.
Declarative Statistical Modeling with Datalog
By virtue of extending Datalog, our framework offers a natural integration with the database, and has a robust declarative semantics.
Ontology-based Data Access: A Study through Disjunctive Datalog, CSP, and MMSNP
Ontology-based data access is concerned with querying incomplete data sources in the presence of domain-specific knowledge provided by an ontology.
