A Knowledge-Based Semisupervised Hierarchical Online Topic Detection Framework

Knowledge-Based Interactive Postmining of User-Preferred Co-Location Patterns Using Ontologies

Co-location pattern mining plays an important role in spatial data mining. With the rapid growth of spatial datasets, the usefulness of co-location patterns is strongly limited by the huge amount of discovered patterns. Although several methods have been proposed to reduce the number of discovered patterns, these statistical algorithms are unable to guarantee that the extracted co-location patterns are user preferred. Therefore, it is crucial to help the decision maker discover his/her preferred co-location patterns via efficient interactive procedures. This article proposes a new interactive approach that enables the user to discover his/her preferred co-location patterns. First, we present a novel and flexible interactive framework to assist the user in discovering his/her preferred co-location patterns. Second, we propose using ontologies to measure the similarity of two co-location patterns. Furthermore, we design a pruning scheme by introducing a pattern filtering model for expressing the user's preference, to reduce the number of the final output. By applying our proposed approach over voluminous sets of co-location patterns, we show that the number of filtered co-location patterns is reduced to several dozen or less and, on average, 80% of the selected co-location patterns are user preferred.

Learning Knowledge Graph Embedding With Heterogeneous Relation Attention Networks

Knowledge graph (KG) embedding aims to study the embedding representation to retain the inherent structure of KGs. Graph neural networks (GNNs), as an effective graph representation technique, have shown impressive performance in learning graph embedding. However, KGs have an intrinsic property of heterogeneity, which contains various types of entities and relations. How to address complex graph data and aggregate multiple types of semantic information simultaneously is a critical issue. In this article, a novel heterogeneous GNNs framework based on attention mechanism is proposed. Specifically, the neighbor features of an entity are first aggregated under each relation-path. Then the importance of different relation-paths is learned through the relation features. Finally, each relation-path-based features with the learned weight values are aggregated to generate the embedding representation. Thus, the proposed method not only aggregates entity features from different semantic aspects but also allocates appropriate weights to them. This method can capture various types of semantic information and selectively aggregate informative features. The experiment results on three real-world KGs demonstrate superior performance when compared with several state-of-the-art methods.