Iteratively Learning Representations for Unseen Entities with Inter-Rule Correlations

Recent work on knowledge graph completion (KGC) focused on learning embeddings of entities and relations in knowledge graphs. These embedding methods require that all test entities are observed at training time, resulting in a time-consuming retraining process for out-of-knowledge-graph (OOKG) entities. To address this issue, current inductive knowledge embedding methods employ graph neural networks (GNNs) to represent unseen entities by aggregating information of known neighbors. They face three important challenges: (i) data sparsity, (ii) the presence of complex patterns in knowledge graphs (e.g., inter-rule correlations), and (iii) the presence of interactions among rule mining, rule inference, and embedding. In this paper, we propose a virtual neighbor network with inter-rule correlations (VNC) that consists of three stages: (i) rule mining, (ii) rule inference, and (iii) embedding. In the rule mining process, to identify complex patterns in knowledge graphs, both logic rules and inter-rule correlations are extracted from knowledge graphs based on operations over relation embeddings. To reduce data sparsity, virtual neighbors for OOKG entities are predicted and assigned soft labels by optimizing a rule-constrained problem. We also devise an iterative framework to capture the underlying relations between rule learning and embedding learning. In our experiments, results on both link prediction and triple classification tasks show that the proposed VNC framework achieves state-of-the-art performance on four widely-used knowledge graphs. Further analysis reveals that VNC is robust to the proportion of unseen entities and effectively mitigates data sparsity.Comment: Accepted at CIKM 202

The International Postal Network and Other Global Flows as Proxies for National Wellbeing.

The digital exhaust left by flows of physical and digital commodities provides a rich measure of the nature, strength and significance of relationships between countries in the global network. With this work, we examine how these traces and the network structure can reveal the socioeconomic profile of different countries. We take into account multiple international networks of physical and digital flows, including the previously unexplored international postal network. By measuring the position of each country in the Trade, Postal, Migration, International Flights, IP and Digital Communications networks, we are able to build proxies for a number of crucial socioeconomic indicators such as GDP per capita and the Human Development Index ranking along with twelve other indicators used as benchmarks of national well-being by the United Nations and other international organisations. In this context, we have also proposed and evaluated a global connectivity degree measure applying multiplex theory across the six networks that accounts for the strength of relationships between countries. We conclude by showing how countries with shared community membership over multiple networks have similar socioeconomic profiles. Combining multiple flow data sources can help understand the forces which drive economic activity on a global level. Such an ability to infer proxy indicators in a context of incomplete information is extremely timely in light of recent discussions on measurement of indicators relevant to the Sustainable Development Goals.Project LASAGNE Contract No. 318132 (STREP) - funded by the European CommissionThis is the final version of the article. It first appeared from PLOS via http://dx.doi.org/10.1371/journal.pone.015597

Unveiling human interactions : approaches and techniques toward the discovery and representation of interactions in networks

L'abstract è presente nell'allegato / the abstract is in the attachmen