Using Knowledge Graph Embedding to Classify Citation Text on CCRO Classes
Knowledge graphs on the web have become the backbone of many information retrieval systems that require access to structural knowledge, whether it be domain-specific or domain-independent. With the increase in the knowledge ingested, the size of the Knowledge Graph increases, it becomes infeasible for large-scale Knowledge Graphs to provide the desired results due to inefficiency in computing and data sparsity. Tackling this issue different knowledge graph embedding models are introduced. We are going to implement Knowledge Graph embedding on ACL Anthology Network (AAN) dataset and classify the relations between the papers based citations’ text to Citations’ Context and Reasons Ontology – CCRO classes where each class has its distinct context. For this purpose we require to implement Natural Language Processing NLP to the citations’ text and then convert these in the form of triples. Applying Knowledge Graph embedding and then classifying into classes.