Potential Energy to Improve Link Prediction With Relational Graph Neural Networks

Simone Colombo; Dimitrios Alivanistos; Michael Cochez

Potential Energy to Improve Link Prediction With Relational Graph Neural Networks

Simone Colombo, Dimitrios Alivanistos, Michael Cochez

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Original language	English
Title of host publication	Proceedings of the AAAI 2022 Spring Symposium on Machine Learning and Knowledge Engineering for Hybrid Intelligence (AAAI-MAKE 2022), Stanford University, Palo Alto, California, USA, March 21-23, 2022
Editors	Andreas Martin, Knut Hinkelmann, Hans-Georg Fill, Aurona Gerber, Doug Lenat, Reinhard Stolle, Frank van Harmelen
Publisher	CEUR-WS.org
Volume	3121
State	Published - Mar 1 2022
Externally published	Yes

Publication series

Name	CEUR Workshop Proceedings
Publisher	CEUR-WS.org

Access to Document

http://ceur-ws.org/Vol-3121/short2.pdf

DL: ICAI Discovery Lab
van Harmelen, F. (CoPI), De Rijke, M. (CoI), Siebert, M. (CoI), Hoekstra, R. (CoPI), Tsatsaronis, G. (CoPI), Groth, P. (CoPI), Cochez, M. (CoI), Pernisch, R. (CoI), Alivanistos, D. (CoI), Mansoury, M. (CoI), van Hoof, H. (CoI), Pal, V. (CoI), Pijnenburg, T. (CoI), Mitra, P. (CoI), Bey, T. (CoI) & de Waard, A. (CoPI)
10/1/19 → 03/31/25
Project: Research

Cite this

Colombo, S., Alivanistos, D., & Cochez, M. (2022). Potential Energy to Improve Link Prediction With Relational Graph Neural Networks. In A. Martin, K. Hinkelmann, H.-G. Fill, A. Gerber, D. Lenat, R. Stolle, & F. V. Harmelen (Eds.), Proceedings of the AAAI 2022 Spring Symposium on Machine Learning and Knowledge Engineering for Hybrid Intelligence (AAAI-MAKE 2022), Stanford University, Palo Alto, California, USA, March 21-23, 2022 (Vol. 3121). (CEUR Workshop Proceedings). CEUR-WS.org. http://ceur-ws.org/Vol-3121/short2.pdf

Colombo, Simone ; Alivanistos, Dimitrios ; Cochez, Michael. / Potential Energy to Improve Link Prediction With Relational Graph Neural Networks. Proceedings of the AAAI 2022 Spring Symposium on Machine Learning and Knowledge Engineering for Hybrid Intelligence (AAAI-MAKE 2022), Stanford University, Palo Alto, California, USA, March 21-23, 2022. editor / Andreas Martin ; Knut Hinkelmann ; Hans-Georg Fill ; Aurona Gerber ; Doug Lenat ; Reinhard Stolle ; Frank van Harmelen. Vol. 3121 CEUR-WS.org, 2022. (CEUR Workshop Proceedings).

@inproceedings{c9c1ed2beef740aaa03e7905736d0dbc,

title = "Potential Energy to Improve Link Prediction With Relational Graph Neural Networks",

author = "Simone Colombo and Dimitrios Alivanistos and Michael Cochez",

year = "2022",

month = mar,

day = "1",

language = "English",

volume = "3121",

series = "CEUR Workshop Proceedings",

publisher = "CEUR-WS.org",

editor = "Andreas Martin and Knut Hinkelmann and Hans-Georg Fill and Aurona Gerber and Doug Lenat and Reinhard Stolle and Harmelen, {Frank van}",

booktitle = "Proceedings of the AAAI 2022 Spring Symposium on Machine Learning and Knowledge Engineering for Hybrid Intelligence (AAAI-MAKE 2022), Stanford University, Palo Alto, California, USA, March 21-23, 2022",

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Colombo, S, Alivanistos, D & Cochez, M 2022, Potential Energy to Improve Link Prediction With Relational Graph Neural Networks. in A Martin, K Hinkelmann, H-G Fill, A Gerber, D Lenat, R Stolle & FV Harmelen (eds), Proceedings of the AAAI 2022 Spring Symposium on Machine Learning and Knowledge Engineering for Hybrid Intelligence (AAAI-MAKE 2022), Stanford University, Palo Alto, California, USA, March 21-23, 2022. vol. 3121, CEUR Workshop Proceedings, CEUR-WS.org. <http://ceur-ws.org/Vol-3121/short2.pdf>

Potential Energy to Improve Link Prediction With Relational Graph Neural Networks. / Colombo, Simone; Alivanistos, Dimitrios; Cochez, Michael.
Proceedings of the AAAI 2022 Spring Symposium on Machine Learning and Knowledge Engineering for Hybrid Intelligence (AAAI-MAKE 2022), Stanford University, Palo Alto, California, USA, March 21-23, 2022. ed. / Andreas Martin; Knut Hinkelmann; Hans-Georg Fill; Aurona Gerber; Doug Lenat; Reinhard Stolle; Frank van Harmelen. Vol. 3121 CEUR-WS.org, 2022. (CEUR Workshop Proceedings).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Potential Energy to Improve Link Prediction With Relational Graph Neural Networks

AU - Colombo, Simone

AU - Alivanistos, Dimitrios

AU - Cochez, Michael

PY - 2022/3/1

Y1 - 2022/3/1

M3 - Conference contribution

VL - 3121

T3 - CEUR Workshop Proceedings

BT - Proceedings of the AAAI 2022 Spring Symposium on Machine Learning and Knowledge Engineering for Hybrid Intelligence (AAAI-MAKE 2022), Stanford University, Palo Alto, California, USA, March 21-23, 2022

A2 - Martin, Andreas

A2 - Hinkelmann, Knut

A2 - Fill, Hans-Georg

A2 - Gerber, Aurona

A2 - Lenat, Doug

A2 - Stolle, Reinhard

A2 - Harmelen, Frank van

PB - CEUR-WS.org

ER -

Colombo S, Alivanistos D, Cochez M. Potential Energy to Improve Link Prediction With Relational Graph Neural Networks. In Martin A, Hinkelmann K, Fill HG, Gerber A, Lenat D, Stolle R, Harmelen FV, editors, Proceedings of the AAAI 2022 Spring Symposium on Machine Learning and Knowledge Engineering for Hybrid Intelligence (AAAI-MAKE 2022), Stanford University, Palo Alto, California, USA, March 21-23, 2022. Vol. 3121. CEUR-WS.org. 2022. (CEUR Workshop Proceedings).

Potential Energy to Improve Link Prediction With Relational Graph Neural Networks

Publication series

Access to Document

Projects

DL: ICAI Discovery Lab

Cite this