Mining the Semantic Web: Statistical learning for next generation knowledge bases
Abstract In the Semantic Web vision of the World Wide Web, content will not only be
accessible to humans but will also be available in machine interpretable form as ontological …
accessible to humans but will also be available in machine interpretable form as ontological …
Probabilistic (logic) programming concepts
L De Raedt, A Kimmig - Machine Learning, 2015 - Springer
A multitude of different probabilistic programming languages exists today, all extending a
traditional programming language with primitives to support modeling of complex, structured …
traditional programming language with primitives to support modeling of complex, structured …
ILP turns 20: biography and future challenges
Abstract Inductive Logic Programming (ILP) is an area of Machine Learning which has now
reached its twentieth year. Using the analogy of a human biography this paper recalls the …
reached its twentieth year. Using the analogy of a human biography this paper recalls the …
Analysing symbolic music with probabilistic grammars
Recent developments in computational linguistics offer ways to approach the analysis of
musical structure by inducing probabilistic models (in the form of grammars) over a corpus of …
musical structure by inducing probabilistic models (in the form of grammars) over a corpus of …
Structured machine learning: the next ten years
The field of inductive logic programming (ILP) has made steady progress, since the first ILP
workshop in 1991, based on a balance of developments in theory, implementations and …
workshop in 1991, based on a balance of developments in theory, implementations and …
[HTML][HTML] The joy of probabilistic answer set programming: semantics, complexity, expressivity, inference
Abstract Probabilistic Answer Set Programming (PASP) combines rules, facts, and
independent probabilistic facts. We argue that a very useful modeling paradigm is obtained …
independent probabilistic facts. We argue that a very useful modeling paradigm is obtained …
[PDF][PDF] A Credal Least Undefined Stable Semantics for Probabilistic Logic Programs and Probabilistic Argumentation.
We present an approach to probabilistic logic programming and probabilistic argumentation
that combines elements of the L-stable semantics and the credal semantics. We derive the …
that combines elements of the L-stable semantics and the credal semantics. We derive the …
New advances in logic-based probabilistic modeling by PRISM
We review a logic-based modeling language PRISM and report recent developments
including belief propagation by the generalized inside-outside algorithm and generative …
including belief propagation by the generalized inside-outside algorithm and generative …
On the semantics and complexity of probabilistic logic programs
We examine the meaning and the complexity of probabilistic logic programs that consist of a
set of rules and a set of independent probabilistic facts (that is, programs based on Sato's …
set of rules and a set of independent probabilistic facts (that is, programs based on Sato's …
Improving scalability of inductive logic programming via pruning and best-effort optimisation
Abstract Inductive Logic Programming (ILP) combines rule-based and statistical artificial
intelligence methods, by learning a hypothesis comprising a set of rules given background …
intelligence methods, by learning a hypothesis comprising a set of rules given background …