2,353 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
On the Evolution of Knowledge Graphs: A Survey and Perspective
Knowledge graphs (KGs) are structured representations of diversified
knowledge. They are widely used in various intelligent applications. In this
article, we provide a comprehensive survey on the evolution of various types of
knowledge graphs (i.e., static KGs, dynamic KGs, temporal KGs, and event KGs)
and techniques for knowledge extraction and reasoning. Furthermore, we
introduce the practical applications of different types of KGs, including a
case study in financial analysis. Finally, we propose our perspective on the
future directions of knowledge engineering, including the potential of
combining the power of knowledge graphs and large language models (LLMs), and
the evolution of knowledge extraction, reasoning, and representation
Complex Temporal Question Answering on Knowledge Graphs
Question answering over knowledge graphs (KG-QA) is a vital topic in IR. Questions with temporal intent are a special class of practical importance, but have not received much attention in research. This work presents EXAQT, the first end-to-end system for answering complex temporal questions that have multiple entities and predicates, and associated temporal conditions. EXAQT answers natural language questions over KGs in two stages, one geared towards high recall, the other towards precision at top ranks. The first step computes question-relevant compact subgraphs within the KG, and judiciously enhances them with pertinent temporal facts, using Group Steiner Trees and fine-tuned BERT models. The second step constructs relational graph convolutional networks (R-GCNs) from the first step's output, and enhances the R-GCNs with time-aware entity embeddings and attention over temporal relations. We evaluate EXAQT on TimeQuestions, a large dataset of 16k temporal questions we compiled from a variety of general purpose KG-QA benchmarks. Results show that EXAQT outperforms three state-of-the-art systems for answering complex questions over KGs, thereby justifying specialized treatment of temporal QA
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