Veronika Thost
Veronika Thost
MIT-IBM Watson AI Lab, IBM Research
Verified email at - Homepage
Cited by
Cited by
Project codenet: a large-scale AI for code dataset for learning a diversity of coding tasks
R Puri, DS Kung, G Janssen, W Zhang, G Domeniconi, V Zolotov, J Dolby, ...
Advances in Neural Information Processing Systems, 2021
Directed acyclic graph neural networks
V Thost, J Chen
International Conference on Learning Representations (ICLR), 2021
Exploring software naturalness through neural language models
L Buratti, S Pujar, M Bornea, S McCarley, Y Zheng, G Rossiello, A Morari, ...
arXiv preprint arXiv:2006.12641, 2020
Temporal Query Answering in the Description Logic DL-Lite
S Borgwardt, M Lippmann, V Thost
International Symposium on Frontiers of Combining Systems, 165-180, 2013
Temporalizing rewritable query languages over knowledge bases
S Borgwardt, M Lippmann, V Thost
Journal of Web Semantics 33, 50-70, 2015
Metric temporal description logics with interval-rigid names
F Baader, S Borgwardt, P Koopmann, A Ozaki, V Thost
ACM Transactions on Computational Logic (TOCL) 21 (4), 1-46, 2020
Infusing knowledge into the textual entailment task using graph convolutional networks
P Kapanipathi, V Thost, SS Patel, S Whitehead, I Abdelaziz, ...
AAAI Conference on Artificial Intelligence 34 (05), 8074-8081, 2020
Temporal query answering in the description logic EL
S Borgwardt, V Thost
International Joint Conference on Artificial Intelligence, 2015
Logic on MARS: Ontologies for Generalised Property Graphs.
M Marx, M Krötzsch, V Thost
International Joint Conference on Artificial Intelligence, 1188-1194, 2017
Ontologies for knowledge graphs: Breaking the rules
M Krötzsch, V Thost
The Semantic Web–ISWC 2016: 15th International Semantic Web Conference, Kobe …, 2016
Attributed Description Logics: Reasoning on Knowledge Graphs.
M Krötzsch, M Marx, A Ozaki, V Thost
International Joint Conference on Artificial Intelligence, 5309-5313, 2018
Improving graph neural network representations of logical formulae with subgraph pooling
M Crouse, I Abdelaziz, C Cornelio, V Thost, L Wu, K Forbus, A Fokoue
arXiv preprint arXiv:1911.06904, 2019
A deep reinforcement learning approach to first-order logic theorem proving
M Crouse, I Abdelaziz, B Makni, S Whitehead, C Cornelio, P Kapanipathi, ...
AAAI Conference on Artificial Intelligence, 2019
Data-efficient graph grammar learning for molecular generation
M Guo, V Thost, B Li, P Das, J Chen, W Matusik
arXiv preprint arXiv:2203.08031, 2022
Improving inductive link prediction using hyper-relational facts
M Ali, M Berrendorf, M Galkin, V Thost, T Ma, V Tresp, J Lehmann
The Semantic Web–ISWC 2021: 20th International Semantic Web Conference, ISWC …, 2021
Temporal Query Answering in DL-Lite with Negation.
S Borgwardt, V Thost
Global Conference on Artificial Intelligence, 51-65, 2015
Attributed description logics: Ontologies for knowledge graphs
M Krötzsch, M Marx, A Ozaki, V Thost
International Semantic Web Conference, 418-435, 2017
Situation recognition for service management systems using OWL 2 reasoners
W Dargie, J Mendez, C Möbius, K Rybina, V Thost, AY Turhan
2013 IEEE International Conference on Pervasive Computing and Communications …, 2013
Software vulnerability detection via deep learning over disaggregated code graph representation
Y Zhuang, S Suneja, V Thost, G Domeniconi, A Morari, J Laredo
arXiv preprint arXiv:2109.03341, 2021
An Analysis of Virtual Nodes in Graph Neural Networks for Link Prediction
EJ Hwang, V Thost, SS Dasgupta, T Ma
The First Learning on Graphs Conference, 2022
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