TY - GEN
T1 - Towards Understanding the Impact of Graph Structure on Knowledge Graph Embeddings
AU - Dave, Brandon
AU - Christou, Antrea
AU - Shimizu, Cogan
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
PY - 2024
Y1 - 2024
N2 - Knowledge graphs (KGs) are an established paradigm for integrating heterogeneous data and representing knowledge. As such, there are many different methodologies for producing KGs, which span notions of expressivity, and are tailored for different use-cases and domains. Now, as neurosymbolic methods rise in prominence, it is important to understand how the development of KGs according to these methodologies impact downstream tasks, such as link prediction using KG embeddings (KGE). In this paper, we modify FB15k-237 in several ways (e.g., by increasingly including semantic metadata). This significantly changes the graph structure (e.g., centrality). We assess how these changes impact the link prediction task, using six KGE models.
AB - Knowledge graphs (KGs) are an established paradigm for integrating heterogeneous data and representing knowledge. As such, there are many different methodologies for producing KGs, which span notions of expressivity, and are tailored for different use-cases and domains. Now, as neurosymbolic methods rise in prominence, it is important to understand how the development of KGs according to these methodologies impact downstream tasks, such as link prediction using KG embeddings (KGE). In this paper, we modify FB15k-237 in several ways (e.g., by increasingly including semantic metadata). This significantly changes the graph structure (e.g., centrality). We assess how these changes impact the link prediction task, using six KGE models.
KW - FB15k-237
KW - Knowledge Graph
KW - Knowledge Graph Embedding
UR - http://corescholar.libraries.wright.edu/cse/704
UR - https://www.scopus.com/pages/publications/85204929639
UR - https://www.scopus.com/pages/publications/85204929639#tab=citedBy
U2 - 10.1007/978-3-031-71170-1_5
DO - 10.1007/978-3-031-71170-1_5
M3 - Conference contribution
AN - SCOPUS:85204929639
SN - 9783031711695
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 41
EP - 50
BT - Neural-Symbolic Learning and Reasoning - 18th International Conference, NeSy 2024, Proceedings
A2 - Besold, Tarek R.
A2 - d’Avila Garcez, Artur
A2 - Jimenez-Ruiz, Ernesto
A2 - Madhyastha, Pranava
A2 - Wagner, Benedikt
A2 - Confalonieri, Roberto
PB - Springer Science and Business Media Deutschland GmbH
T2 - 18th International Conference on Neural-Symbolic Learning and Reasoning, NeSy 2024
Y2 - 9 September 2024 through 12 September 2024
ER -