One connector, four scholarly indexes, real DOIs and legal free PDFs. Works in Claude and ChatGPT.
Paste it into Claude or ChatGPT as a custom connector. 3-day free trial.
Shaoxiong Ji, Shirui Pan, Erik Cambria, Pekka Marttinen, Philip S. Yu
Human knowledge provides a formal understanding of the world. Knowledge graphs that represent structural relations between entities have become an increasingly popular research direction toward cognition and human-level intelligence. In this survey, we provide a comprehensive review of the 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 metarelational learning, commonsense reasoning, and temporal knowledge graphs. To facilitate future research on knowledge graphs, we also provide a curated collection of data sets and open-source libraries on different tasks. In the end, we have a thorough outlook on several promising research directions.
Formatted from the source metadata, not typed by hand. For a whole reference list, use the citation generator or ask your assistant through Find Research.
Ji, S., Pan, S., Cambria, E., Marttinen, P., & Yu, P. S. (2021). A Survey on Knowledge Graphs: Representation, Acquisition, and Applications. IEEE Transactions on Neural Networks and Learning Systems, 33(2), 494–514. https://doi.org/10.1109/tnnls.2021.3070843
Ji, Shaoxiong, et al. "A Survey on Knowledge Graphs: Representation, Acquisition, and Applications." IEEE Transactions on Neural Networks and Learning Systems, vol. 33, no. 2, 2021, pp. 494–514, https://doi.org/10.1109/tnnls.2021.3070843.
Ji, Shaoxiong, Shirui Pan, Erik Cambria, Pekka Marttinen, and Philip S. Yu. "A Survey on Knowledge Graphs: Representation, Acquisition, and Applications." IEEE Transactions on Neural Networks and Learning Systems 33, no. 2 (2021): 494–514. https://doi.org/10.1109/tnnls.2021.3070843.
Ji S., Pan S., Cambria E. [и др.] A Survey on Knowledge Graphs: Representation, Acquisition, and Applications // IEEE Transactions on Neural Networks and Learning Systems. — 2021. — Vol. 33, № 2. — P. 494–514. — URL: https://doi.org/10.1109/tnnls.2021.3070843
@article{ji2021survey,
title = {{A Survey on Knowledge Graphs: Representation, Acquisition, and Applications}},
author = {Shaoxiong Ji and Shirui Pan and Erik Cambria and Pekka Marttinen and Philip S. Yu},
journal = {IEEE Transactions on Neural Networks and Learning Systems},
year = {2021},
volume = {33},
number = {2},
pages = {494--514},
doi = {10.1109/tnnls.2021.3070843},
url = {https://doi.org/10.1109/tnnls.2021.3070843}
}TY - JOUR AU - Ji, Shaoxiong AU - Pan, Shirui AU - Cambria, Erik AU - Marttinen, Pekka AU - Yu, Philip S. TI - A Survey on Knowledge Graphs: Representation, Acquisition, and Applications JO - IEEE Transactions on Neural Networks and Learning Systems VL - 33 IS - 2 SP - 494 EP - 514 PY - 2021 DA - 2021/04/26 DO - 10.1109/tnnls.2021.3070843 UR - https://doi.org/10.1109/tnnls.2021.3070843 ER -
Add Find Research to Claude or ChatGPT and ask in plain language. The assistant reads this record, finds the papers around it and cites only what it actually found:
Setup takes about a minute: add the connector, then start the free trial.
Record from OpenAlex; open-access location from Unpaywall and the indexes. Find Research links only to legal copies.