Approximate Knowledge Graph Query Answering: From Ranking to Binary Classification. van Bakel, R., Aleksiev, T., Daza, D., Alivanistos, D., & Cochez, M. In Cochez, M., Croitoru, M., Marquis, P., & Rudolph, S., editors, Graph Structures for Knowledge Representation and Reasoning, pages 107–124, Cham, September, 2021. Springer International Publishing. abstract bibtex Large, heterogeneous datasets are characterized by missing or even erroneous information. This is more evident when they are the product of community effort or automatic fact extraction methods from external sources, such as text. A special case of the aforementioned phenomenon can be seen in knowledge graphs, where this mostly appears in the form of missing or incorrect edges and nodes.
@inproceedings{van_bakel_approximate_2021,
address = {Cham},
title = {Approximate {Knowledge} {Graph} {Query} {Answering}: {From} {Ranking} to {Binary} {Classification}},
isbn = {978-3-030-72308-8},
abstract = {Large, heterogeneous datasets are characterized by missing or even erroneous information. This is more evident when they are the product of community effort or automatic fact extraction methods from external sources, such as text. A special case of the aforementioned phenomenon can be seen in knowledge graphs, where this mostly appears in the form of missing or incorrect edges and nodes.},
booktitle = {Graph {Structures} for {Knowledge} {Representation} and {Reasoning}},
publisher = {Springer International Publishing},
author = {van Bakel, Ruud and Aleksiev, Teodor and Daza, Daniel and Alivanistos, Dimitrios and Cochez, Michael},
editor = {Cochez, Michael and Croitoru, Madalina and Marquis, Pierre and Rudolph, Sebastian},
month = sep,
year = {2021},
pages = {107--124},
}
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