A Comprehensive Survey on Automatic Knowledge Graph Construction
By Lingfeng Zhong, Jia Wu, Qian Li, Hao Peng, Xindong Wu. ACM Computing Surveys, 2023
347 citations recorded by the source corpus.
Automatic knowledge graph construction aims at manufacturing structured human knowledge. To this end, much effort has historically been spent extracting informative fact patterns from different data sources. However, more recently, research interest has shifted to acquiring conceptualized structured knowledge beyond informative data. In addition, researchers have also been exploring new ways of handling sophisticated construction tasks in diversified scenarios. Thus, there is a demand for a systematic review of paradigms to organize knowledge structures beyond data-level mentions. To meet this demand, we comprehensively survey more than 300 methods to summarize the latest developments in knowledge graph construction. A knowledge graph is built in three steps: knowledge acquisition, knowledge refinement, and knowledge evolution. The processes of knowledge acquisition are reviewed in detail, including obtaining entities with fine-grained types and their conceptual linkages to knowledge graphs; resolving coreferences; and extracting entity relationships in complex scenarios. The survey covers models for knowledge refinement, including knowledge graph completion, and knowledge fusion. Methods to handle knowledge evolution are also systematically presented, including condition knowledge acquisition, condition knowledge graph completion, and knowledge dynamic. We present the paradigms to compare the distinction among these methods along the axis of the data environment, motivation, and architecture. Additionally, we also provide briefs on accessible resources that can help readers to develop practical knowledge graph systems. The survey concludes with discussions on the challenges and possible directions for future exploration.
Subfields: Artificial Intelligence, Management Science and Operations Research
View Paper: https://www.semanticscholar.org/paper/9ea5874d261359e287eabb735de38a8edba1e091
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
In Management Science and Operations Research
Citations in the corpus
- Large language models for generative information extraction: a survey (2023, background): “This discrepancy may be attributed to the critical importance of NER, its utility in various downstream tasks, and its relatively simpler structured outputs, which facilitate large-scale supervised fine-tuning [1].”
- Unifying Large Language Models and Knowledge Graphs: A Roadmap (2023, background): “Nevertheless, KGs are difficult to construct [25], and current approaches in KGs [27], [33], [34] are inadequate in handling the incomplete and dynamically changing nature of real-world KGs.”