32,837 research outputs found
Ontology of core data mining entities
In this article, we present OntoDM-core, an ontology of core data mining
entities. OntoDM-core defines themost essential datamining entities in a three-layered
ontological structure comprising of a specification, an implementation and an application
layer. It provides a representational framework for the description of mining
structured data, and in addition provides taxonomies of datasets, data mining tasks,
generalizations, data mining algorithms and constraints, based on the type of data.
OntoDM-core is designed to support a wide range of applications/use cases, such as
semantic annotation of data mining algorithms, datasets and results; annotation of
QSAR studies in the context of drug discovery investigations; and disambiguation of
terms in text mining. The ontology has been thoroughly assessed following the practices
in ontology engineering, is fully interoperable with many domain resources and
is easy to extend
Enhancing Workflow with a Semantic Description of Scientific Intent
Peer reviewedPreprin
Agents for educational games and simulations
This book consists mainly of revised papers that were presented at the Agents for Educational Games and Simulation (AEGS) workshop held on May 2, 2011, as part of the Autonomous Agents and MultiAgent Systems (AAMAS) conference in Taipei, Taiwan. The 12 full papers presented were carefully reviewed and selected from various submissions. The papers are organized topical sections on middleware applications, dialogues and learning, adaption and convergence, and agent applications
Training an adaptive dialogue policy for interactive learning of visually grounded word meanings
We present a multi-modal dialogue system for interactive learning of
perceptually grounded word meanings from a human tutor. The system integrates
an incremental, semantic parsing/generation framework - Dynamic Syntax and Type
Theory with Records (DS-TTR) - with a set of visual classifiers that are
learned throughout the interaction and which ground the meaning representations
that it produces. We use this system in interaction with a simulated human
tutor to study the effects of different dialogue policies and capabilities on
the accuracy of learned meanings, learning rates, and efforts/costs to the
tutor. We show that the overall performance of the learning agent is affected
by (1) who takes initiative in the dialogues; (2) the ability to express/use
their confidence level about visual attributes; and (3) the ability to process
elliptical and incrementally constructed dialogue turns. Ultimately, we train
an adaptive dialogue policy which optimises the trade-off between classifier
accuracy and tutoring costs.Comment: 11 pages, SIGDIAL 2016 Conferenc
Evolutionary intelligent agents for e-commerce: Generic preference detection with feature analysis
Product recommendation and preference tracking systems have been adopted extensively in e-commerce businesses. However, the heterogeneity of product attributes results in undesired impediment for an efficient yet personalized e-commerce product brokering. Amid the assortment of product attributes, there are some intrinsic generic attributes having significant relation to a customer’s generic preference. This paper proposes a novel approach in the detection of generic product attributes through feature analysis. The objective is to provide an insight to the understanding of customers’ generic preference. Furthermore, a genetic algorithm is used to find the suitable feature weight set, hence reducing the rate of misclassification. A prototype has been implemented and the experimental results are promising
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