A review: Knowledge reasoning over knowledge graph
X Chen, S Jia, Y Xiang - Expert systems with applications, 2020 - Elsevier
Mining valuable hidden knowledge from large-scale data relies on the support of reasoning
technology. Knowledge graphs, as a new type of knowledge representation, have gained …
technology. Knowledge graphs, as a new type of knowledge representation, have gained …
Reasoning like human: Hierarchical reinforcement learning for knowledge graph reasoning
Knowledge Graphs typically suffer from incompleteness. A popular approach to knowledge
graph completion is to infer missing knowledge by multi-hop reasoning over the information …
graph completion is to infer missing knowledge by multi-hop reasoning over the information …
[HTML][HTML] KGen: a knowledge graph generator from biomedical scientific literature
Background Knowledge is often produced from data generated in scientific investigations.
An ever-growing number of scientific studies in several domains result into a massive …
An ever-growing number of scientific studies in several domains result into a massive …
[HTML][HTML] Building knowledge graphs from unstructured texts: applications and impact analyses in cybersecurity education
Knowledge graphs gained popularity in recent years and have been useful for concept
visualization and contextual information retrieval in various applications. However …
visualization and contextual information retrieval in various applications. However …
Coarse-to-fine knowledge graph domain adaptation based on distantly-supervised iterative training
Modern supervised learning neural network models require a large amount of manually
labeled data, which makes the construction of domain-specific knowledge graphs time …
labeled data, which makes the construction of domain-specific knowledge graphs time …
A survey on state-of-the-art techniques for knowledge graphs construction and challenges ahead
Global datasphere is increasing fast, and it is expected to reach 175 Zettabytes by 20251.
However, most of the content is unstructured and is not understandable by machines …
However, most of the content is unstructured and is not understandable by machines …
An automatic knowledge graph creation framework from natural language text
N Kertkeidkachorn, R Ichise - IEICE TRANSACTIONS on …, 2018 - search.ieice.org
Knowledge graphs (KG) play a crucial role in many modern applications. However,
constructing a KG from natural language text is challenging due to the complex structure of …
constructing a KG from natural language text is challenging due to the complex structure of …
Multimodal knowledge graph for deep learning papers and code
AV Kannan, D Fradkin, I Akrotirianakis… - Proceedings of the 29th …, 2020 - dl.acm.org
Keeping up with the rapid growth of Deep Learning (DL) research is a daunting task. While
existing scientific literature search systems provide text search capabilities and can identify …
existing scientific literature search systems provide text search capabilities and can identify …
[HTML][HTML] Information extraction pipelines for knowledge graphs
In the last decade, a large number of knowledge graph (KG) completion approaches were
proposed. Albeit effective, these efforts are disjoint, and their collective strengths and …
proposed. Albeit effective, these efforts are disjoint, and their collective strengths and …
[HTML][HTML] LILLIE: Information extraction and database integration using linguistics and learning-based algorithms
E Smith, D Papadopoulos, M Braschler, K Stockinger - Information Systems, 2022 - Elsevier
Querying both structured and unstructured data via a single common query interface such as
SQL or natural language has been a long standing research goal. Moreover, as methods for …
SQL or natural language has been a long standing research goal. Moreover, as methods for …