Knowledge Graph Embedding for Topical and Entity Classification in Multi-Source Social Network Data

Historically, online data has provided meaningful insights for information mining, leading to the adoption of knowledge graphs for application to online data. Knowledge embedding has become an important aspect of encoding and decoding links, relationships, and predicting the ties of an entity to an existing knowledge graph. This study applied topic modeling to extract topics, entities, and themes from heterogeneous web data from different sources around the Indo-Pacific region and modeled a knowledge graph. The knowledge graph was subjected to knowledge embedding by applying four scoring mechanisms: ComplEx, TransE, DistMult, and HolE, on a domain knowledge graph of Indo-Pacific Belt and Road initiatives to determine whether it was capable of revealing missing insights. This work significantly uses knowledge graphs and embedding to understand socioeconomic-related discussions online. Valuable insights were gained from the data in this research's clustering results of knowledge embedding. Important themes such as NASAKOM and BRI were identified in Cluster 0. Cluster 1 contained themes that discussed Marxist movements synonymous with Indonesia, and Cluster 2 showed themes on China's road policies, such as Asia-Pacific Economic Cooperation and Export-Import Bank China. Cluster 3 focused mainly on China's economic policies and the Philippines. Overall, this study demonstrates the usefulness of topic modeling and knowledge embedding in uncovering insights from online data and has implications for understanding socioeconomic trends in the Indo-Pacific region.