Now showing items 1-6 of 6

  • Ontological quality control in large-scale, applied ontology matching

    Legg, Catherine; Sarjant, Samuel (2013)
    To date, large-scale applied ontology mapping has relied greatly on label matching and other relatively simple syntactic features. In search of more holistic and accurate alignment, we offer a suite of partially overlapping ...
  • Policy Search Based Relational Reinforcement Learning using the Cross-Entropy Method

    Sarjant, Samuel (University of Waikato, 2013)
    Relational Reinforcement Learning (RRL) is a subfield of machine learning in which a learning agent seeks to maximise a numerical reward within an environment, represented as collections of objects and relations, by ...
  • Massive ontology interface

    Stannett, Matt; Legg, Catherine; Sarjant, Samuel (ACM, 2013)
    This paper describes the Massive Ontology Interface (MOI), a web portal which facilitates interaction with a large ontology (over 200,000 concepts and 1.6M assertions) that is built automatically using OpenCyc as a backbone. ...
  • Bill Gates is not a parking meter: Philosophical quality control in automated ontology building

    Legg, Catherine; Sarjant, Samuel (The International Association for Computing and Philosophy, 2012)
    The somewhat old-fashioned concept of philosophical categories is revived and put to work in automated ontology building. We describe a project harvesting knowledge from Wikipedia’s category network in which the principled ...
  • Using the online cross-entropy method to learn relational policies for playing different games

    Sarjant, Samuel; Pfahringer, Bernhard; Driessens, Kurt; Smith, Tony C. (IEEE, 2011)
    By defining a video-game environment as a collection of objects, relations, actions and rewards, the relational reinforcement learning algorithm presented in this paper generates and optimises a set of concise, human-readable ...
  • “All you can eat” ontology-building: Feeding Wikipedia to Cyc

    Sarjant, Samuel; Legg, Catherine; Robinson, Michael; Medelyan, Olena (IEEE Computer Society, 2009)
    In order to achieve genuine web intelligence, building some kind of large general machine-readable conceptual scheme (i.e. ontology) seems inescapable. Yet the past 20 years have shown that manual ontology-building is not ...