Learning First-Order Definitions of Functions

First-order learning involves finding a clause-form definition of a relation from examples of the relation and relevant background information. In this paper, a particular first-order learning system is modified to customize it for finding definitions of functional relations. This restriction leads to faster learning times and, in some cases, to definitions that have higher predictive accuracy. Other first-order learning systems might benefit from similar specialization.Comment: See http://www.jair.org/ for any accompanying file

Constructing knowledge: an experience of active and collaborative learning in an ICT classroom

This paper reports on the impact of the implementation of active and collaborative practices in ICT (information and communication technologies) classrooms. Both of these approaches convey a lot of responsibility from the teacher to the students and the hoping, as backed up by the literature, is to promote deeper learning and reasoning skills at a higher level. The question is: how do you do all that? This research describes a specific environment that makes use of collaborative tools, like wikis and forums within an e-learning platform and of specific CRM (customer relationship management) software. In order to analyze how this learning environment gets learners actively involved in learning and working together in productive ways, students were surveyed by responding to questionnaires. Several cause-effect relations underlying the teaching-learning methodology and the students’ performance are discussed

Interfirm co-operation and learning within SME networks - two cases from the Styrian Automotive cluster

Recent publications in the cluster-related literature have shown that interfirm links imply the potential to foster higher forms of learning within clusters. Especially networks deserve in this context a particular focus of attention. The purpose of this paper is to show essential conditions that should be present at cluster level in order to enable such forms of learning between the firms. This will be done in order to give a first advice for public and semi-public cluster institutions to facilitate interfirm collaborations and cluster related activities. Two case-studies of SME-networks selected from Styrian clusters will give the opportunity to get deeper insights into the conditions that enable clusters to bring forth double loop learning activities. In a first step particular criteria for the presence of double-loop learning will be established. In a second step the conditions for this specific type of learning will be dealt with. Among the categories of conditions that will be examined in detail are the relations and interactions in the network, the types of joint projects carried out between the firms and the specific organizational culture that prevails at the individual firm level. Key words: SME-networks, learning, organizational culture.

Factor Graph Neural Networks

In recent years, we have witnessed a surge of Graph Neural Networks (GNNs), most of which can learn powerful representations in an end-to-end fashion with great success in many real-world applications. They have resemblance to Probabilistic Graphical Models (PGMs), but break free from some limitations of PGMs. By aiming to provide expressive methods for representation learning instead of computing marginals or most likely configurations, GNNs provide flexibility in the choice of information flowing rules while maintaining good performance. Despite their success and inspirations, they lack efficient ways to represent and learn higher-order relations among variables/nodes. More expressive higher-order GNNs which operate on k-tuples of nodes need increased computational resources in order to process higher-order tensors. We propose Factor Graph Neural Networks (FGNNs) to effectively capture higher-order relations for inference and learning. To do so, we first derive an efficient approximate Sum-Product loopy belief propagation inference algorithm for discrete higher-order PGMs. We then neuralize the novel message passing scheme into a Factor Graph Neural Network (FGNN) module by allowing richer representations of the message update rules; this facilitates both efficient inference and powerful end-to-end learning. We further show that with a suitable choice of message aggregation operators, our FGNN is also able to represent Max-Product belief propagation, providing a single family of architecture that can represent both Max and Sum-Product loopy belief propagation. Our extensive experimental evaluation on synthetic as well as real datasets demonstrates the potential of the proposed model.Comment: Accepted by JML

Hypernode Graphs for Spectral Learning on Binary Relations over Sets

Paper accepted for publication at ECML/PKDD 2014International audienceWe introduce hypernode graphs as weighted binary relations between sets of nodes: a hypernode is a set of nodes, a hyperedge is a pair of hypernodes, and each node in a hypernode of a hyperedge is given a non negative weight that represents the node contribution to the relation. Hypernode graphs model binary relations between sets of individuals while allowing to reason at the level of individuals. We present a spectral theory for hypernode graphs that allows us to introduce an unnormalized Laplacian and a smoothness semi-norm. In this framework, we are able to extend spectral graph learning algorithms to the case of hypernode graphs. We show that hypernode graphs are a proper extension of graphs from the expressive power point of view and from the spectral analysis point of view. Therefore hypernode graphs allow to model higher order relations whereas it is not true for hypergraphs as shown in~\cite{Agarwal2006}. In order to prove the potential of the model, we represent multiple players games with hypernode graphs and introduce a novel method to infer skill ratings from game outcomes. We show that spectral learning algorithms over hypernode graphs obtain competitive results with skill ratings specialized algorithms such as Elo duelling and TrueSkill

Building Contextual Knowledge Graphs for Personalized Learning Recommendations using Text Mining and Semantic Graph Completion

Modelling learning objects (LO) within their context enables the learner to advance from a basic, remembering-level, learning objective to a higher-order one, i.e., a level with an application- and analysis objective. While hierarchical data models are commonly used in digital learning platforms, using graph-based models enables representing the context of LOs in those platforms. This leads to a foundation for personalized recommendations of learning paths. In this paper, the transformation of hierarchical data models into knowledge graph (KG) models of LOs using text mining is introduced and evaluated. We utilize custom text mining pipelines to mine semantic relations between elements of an expert-curated hierarchical model. We evaluate the KG structure and relation extraction using graph quality-control metrics and the comparison of algorithmic semantic-similarities to expert-defined ones. The results show that the relations in the KG are semantically comparable to those defined by domain experts, and that the proposed KG improves representing and linking the contexts of LOs through increasing graph communities and betweenness centrality

Finding voices: authentic learning online in the field of public communication and citizenship

A creative re-acculturation of teachers and students is occurring in virtual classrooms as traditional learning resources, pedagogy, and technology intersect in unexpected ways. This paper reports on a case of authentic, experiential, and constructivist learning developed for tertiary public relations&nbsp; students. A subject titled &lsquo;Public Communication and Citizenship&rsquo; (PCC) at&nbsp; Deakin University in Australia asked students to examine the problematic and contentious areas of self interest, persuasion, power, and ethics in&nbsp; contemporary contexts of mass media and globalisation. Feedback from&nbsp; those students suggests that, in this case, online teaching strategies&nbsp; successfully integrated with the total learning environment to achieve&nbsp; higher-order learning. PCC is one example of PR pedagogy combining&nbsp; theory and technology to move beyond &lsquo;skilling for jobs&rsquo;.<br /