Fine-grained Type Prediction of Entities using Knowledge Graph Embeddings

Wikipedia is the largest online encyclopedia, which appears in more than 301 different languages, with the English version containing more than 5.9 million articles. However, using Wikipedia means reading it and searching through pages to find the needed information. On the other hand, DBpedia contains the information of Wikipedia in a structured manner, that is easy to reuse. Knowledge bases such as DBpedia and Wikidata have been recognised as the foundation for diverse applications in the field of data mining, information retrieval and natural language processing. A knowledge base describes real-world objects and the interrelations between them as entities and properties. The entities that share common characteristics are associated with a corresponding type. One of the most important pieces of information in knowledge bases is the type of the entities described. However, it has been observed that type information is often noisy or incomplete. In general, there is a need for well-defined type information for the entities of a knowledge base. In this thesis, the task of fine-grained entity typing of entities of a knowledge base, more specifically - DBpedia, is addressed. There are a lot of entities in DBpedia that are not assigned to a fine-grained type information, rather assigned to either coarse-grained type information or to rdf:type owl:Thing. Fine-grained entity typing aims at assigning more specific types, which are more informative than the coarse-grained ones. This thesis explores and evaluates different approaches for type prediction of entities in DBpedia - the unsupervised approach vector similarity using knowledge graph embeddings, as well as the supervised one - CNN classification. Knowledge graph embeddings from the pre-trained RDF2Vec model are used

Entity Type Prediction in Knowledge Graphs using Embeddings

Open Knowledge Graphs (such as DBpedia, Wikidata, YAGO) have been recognized as the backbone of diverse applications in the field of data mining and information retrieval. Hence, the completeness and correctness of the Knowledge Graphs (KGs) are vital. Most of these KGs are mostly created either via an automated information extraction from Wikipedia snapshots or information accumulation provided by the users or using heuristics. However, it has been observed that the type information of these KGs is often noisy, incomplete, and incorrect. To deal with this problem a multi-label classification approach is proposed in this work for entity typing using KG embeddings. We compare our approach with the current state-of-the-art type prediction method and report on experiments with the KGs

Contextual language models for knowledge graph completion

Knowledge Graphs (KGs) have become the backbone of various machine learning based applications over the past decade. However, the KGs are often incomplete and inconsistent. Several representation learning based approaches have been introduced to complete the missing information in KGs. Besides, Neural Language Models (NLMs) have gained huge momentum in NLP applications. However, exploiting the contextual NLMs to tackle the Knowledge Graph Completion (KGC) task is still an open research problem. In this paper, a GPT-2 based KGC model is proposed and is evaluated on two benchmark datasets. The initial results obtained from the fine-tuning of the GPT-2 model for triple classification strengthens the importance of usage of NLMs for KGC. Also, the impact of contextual language models for KGC has been discussed

Contextual Language Models for Knowledge Graph Completion

Knowledge Graphs (KGs) have become the backbone of various machine learning based applications over the past decade. However, the KGs are often incomplete and inconsistent. Several representation learning based approaches have been introduced to complete the missing information in KGs. Besides, Neural Language Models (NLMs) have gained huge momentum in NLP applications. However, exploiting the contextual NLMs to tackle the Knowledge Graph Completion (KGC) task is still an open research problem. In this paper, a GPT-2 based KGC model is proposed and is evaluated on two benchmark datasets. The initial results obtained from the _ne-tuning of the GPT-2 model for triple classi_cation strengthens the importance of usage of NLMs for KGC. Also, the impact of contextual language models for KGC has been discussed