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 …
[图书][B] Plan, activity, and intent recognition: Theory and practice
Plan recognition, activity recognition, and intent recognition together combine and unify
techniques from user modeling, machine vision, intelligent user interfaces, human/computer …
techniques from user modeling, machine vision, intelligent user interfaces, human/computer …
Online learning of event definitions
Systems for symbolic event recognition infer occurrences of events in time using a set of
event definitions in the form of first-order rules. The Event Calculus is a temporal logic that …
event definitions in the form of first-order rules. The Event Calculus is a temporal logic that …
Interactive machine learning in data exploitation
The goal of interactive machine learning is to help scientists and engineers exploit more
specialized data from within their deployed environment in less time, with greater accuracy …
specialized data from within their deployed environment in less time, with greater accuracy …
A prototype for credit card fraud management: Industry paper
To prevent problems and capitalise on opportunities before they even occur, the research
project SPEEDD proposed a methodology, and developed a prototype for proactive event …
project SPEEDD proposed a methodology, and developed a prototype for proactive event …
Semi-supervised online structure learning for composite event recognition
Online structure learning approaches, such as those stemming from statistical relational
learning, enable the discovery of complex relations in noisy data streams. However, these …
learning, enable the discovery of complex relations in noisy data streams. However, these …
: Online Structure Learning Using Background Knowledge Axiomatization
We present OSL α—an online structure learner for Markov Logic Networks (MLNs) that
exploits background knowledge axiomatization in order to constrain the space of possible …
exploits background knowledge axiomatization in order to constrain the space of possible …
Online learning of weighted relational rules for complex event recognition
Abstract Systems for symbolic complex event recognition detect occurrences of events in
time using a set of event definitions in the form of logical rules. The Event Calculus is a …
time using a set of event definitions in the form of logical rules. The Event Calculus is a …
Learning hierarchical probabilistic logic programs
Probabilistic logic programming (PLP) combines logic programs and probabilities. Due to its
expressiveness and simplicity, it has been considered as a powerful tool for learning and …
expressiveness and simplicity, it has been considered as a powerful tool for learning and …
Scalable structure learning for probabilistic soft logic
Statistical relational frameworks such as Markov logic networks and probabilistic soft logic
(PSL) encode model structure with weighted first-order logical clauses. Learning these …
(PSL) encode model structure with weighted first-order logical clauses. Learning these …