253 research outputs found
A Survey on Knowledge Graphs: Representation, Acquisition and Applications
Human knowledge provides a formal understanding of the world. Knowledge
graphs that represent structural relations between entities have become an
increasingly popular research direction towards cognition and human-level
intelligence. In this survey, we provide a comprehensive review of knowledge
graph covering overall research topics about 1) knowledge graph representation
learning, 2) knowledge acquisition and completion, 3) temporal knowledge graph,
and 4) knowledge-aware applications, and summarize recent breakthroughs and
perspective directions to facilitate future research. We propose a full-view
categorization and new taxonomies on these topics. Knowledge graph embedding is
organized from four aspects of representation space, scoring function, encoding
models, and auxiliary information. For knowledge acquisition, especially
knowledge graph completion, embedding methods, path inference, and logical rule
reasoning, are reviewed. We further explore several emerging topics, including
meta relational learning, commonsense reasoning, and temporal knowledge graphs.
To facilitate future research on knowledge graphs, we also provide a curated
collection of datasets and open-source libraries on different tasks. In the
end, we have a thorough outlook on several promising research directions
Embedding Based Link Prediction for Knowledge Graph Completion
Knowledge Graphs (KGs) are the most widely used representation of structured information about a particular domain consisting of billions of facts in the form of entities (nodes) and relations (edges) between them. Besides, the KGs also encapsulate the semantic type information of the entities. The last two decades have witnessed a constant growth of KGs in various domains such as government, scholarly data, biomedical domains, etc. KGs have been used in Machine Learning based applications such as entity linking, question answering, recommender systems, etc. Open KGs are mostly heuristically created, automatically generated from heterogeneous resources such as text, images, etc., or are human-curated. However, these KGs are often incomplete, i.e., there are missing links between the entities and missing links between the entities and their corresponding entity types. This thesis focuses on addressing these two challenges of link prediction for Knowledge Graph Completion (KGC):
\textbf{(i)} General Link Prediction in KGs that include head and tail prediction, triple classification, and
\textbf{(ii)} Entity Type Prediction.
Most of the graph mining algorithms are proven to be of high complexity, deterring their usage in KG-based applications. In recent years, KG embeddings have been trained to represent the entities and relations in the KG in a low-dimensional vector space preserving the graph structure. In most published works such as the translational models, convolutional models, semantic matching, etc., the triple information is used to generate the latent representation of the entities and relations.
In this dissertation, it is argued that contextual information about the entities obtained from the random walks, and textual entity descriptions, are the keys to improving the latent representation of the entities for KGC. The experimental results show that the knowledge obtained from the context of the entities supports the hypothesis. Several methods have been proposed for KGC and their effectiveness is shown empirically in this thesis. Firstly, a novel multi-hop attentive KG embedding model MADLINK is proposed for Link Prediction. It considers the contextual information of the entities by using random walks as well as textual entity descriptions of the entities. Secondly, a novel architecture exploiting the information contained in a pre-trained contextual Neural Language Model (NLM) is proposed for Triple Classification. Thirdly, the limitations of the current state-of-the-art (SoTA) entity type prediction models have been analysed and a novel entity typing model CAT2Type is proposed that exploits the Wikipedia Categories which is one of the most under-treated features of the KGs. This model can also be used to predict missing types of unseen entities i.e., the newly added entities in the KG.
Finally, another novel architecture GRAND is proposed to predict the missing entity types in KGs using multi-label, multi-class, and hierarchical classification by leveraging different strategic graph walks in the KGs. The extensive experiments and ablation studies show that all the proposed models outperform the current SoTA models and set new baselines for KGC.
The proposed models establish that the NLMs and the contextual information of the entities in the KGs together with the different neural network architectures benefit KGC. The promising results and observations open up interesting scopes for future research involving exploiting the proposed models in domain-specific KGs such as scholarly data, biomedical data, etc. Furthermore, the link prediction model can be exploited as a base model for the entity alignment task as it considers the neighbourhood information of the entities
BERT Based Clinical Knowledge Extraction for Biomedical Knowledge Graph Construction and Analysis
Background : Knowledge is evolving over time, often as a result of new
discoveries or changes in the adopted methods of reasoning. Also, new facts or
evidence may become available, leading to new understandings of complex
phenomena. This is particularly true in the biomedical field, where scientists
and physicians are constantly striving to find new methods of diagnosis,
treatment and eventually cure. Knowledge Graphs (KGs) offer a real way of
organizing and retrieving the massive and growing amount of biomedical
knowledge.
Objective : We propose an end-to-end approach for knowledge extraction and
analysis from biomedical clinical notes using the Bidirectional Encoder
Representations from Transformers (BERT) model and Conditional Random Field
(CRF) layer.
Methods : The approach is based on knowledge graphs, which can effectively
process abstract biomedical concepts such as relationships and interactions
between medical entities. Besides offering an intuitive way to visualize these
concepts, KGs can solve more complex knowledge retrieval problems by
simplifying them into simpler representations or by transforming the problems
into representations from different perspectives. We created a biomedical
Knowledge Graph using using Natural Language Processing models for named entity
recognition and relation extraction. The generated biomedical knowledge graphs
(KGs) are then used for question answering.
Results : The proposed framework can successfully extract relevant structured
information with high accuracy (90.7% for Named-entity recognition (NER), 88%
for relation extraction (RE)), according to experimental findings based on
real-world 505 patient biomedical unstructured clinical notes.
Conclusions : In this paper, we propose a novel end-to-end system for the
construction of a biomedical knowledge graph from clinical textual using a
variation of BERT models
Zero-Shot Learning with Common Sense Knowledge Graphs
Zero-shot learning relies on semantic class representations such as
hand-engineered attributes or learned embeddings to predict classes without any
labeled examples. We propose to learn class representations from common sense
knowledge graphs. Common sense knowledge graphs are an untapped source of
explicit high-level knowledge that requires little human effort to apply to a
range of tasks. To capture the knowledge in the graph, we introduce ZSL-KG, a
general-purpose framework with a novel transformer graph convolutional network
(TrGCN) for generating class representations. Our proposed TrGCN architecture
computes non-linear combinations of the node neighbourhood and shows
improvements on zero-shot learning tasks in language and vision. Our results
show ZSL-KG outperforms the best performing graph-based zero-shot learning
framework by an average of 2.1 accuracy points with improvements as high as 3.4
accuracy points. Our ablation study on ZSL-KG with alternate graph neural
networks shows that our TrGCN adds up to 1.2 accuracy points improvement on
these tasks
- …