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Found 5 papers, 3 papers with code
Approximate Answering of Graph Queries
We will first provide an overview of the different query types which can be supported by these methods and datasets typically used for evaluation, as well as an insight into their limitations.
BioBLP: A Modular Framework for Learning on Multimodal Biomedical Knowledge Graphs
We train models using a biomedical KG containing approximately 2 million triples, and evaluate the performance of the resulting entity embeddings on the tasks of link prediction, and drug-protein interaction prediction, comparing against methods that do not take attribute data into account.
Prompting as Probing: Using Language Models for Knowledge Base Construction
ProP implements a multi-step approach that combines a variety of prompting techniques to achieve this.
Query Embedding on Hyper-relational Knowledge Graphs
Besides that, we propose a method to answer such queries and demonstrate in our experiments that qualifiers improve query answering on a diverse set of query patterns.
Approximate Knowledge Graph Query Answering: From Ranking to Binary Classification
Structured querying on such incomplete graphs will result in incomplete sets of answers, even if the correct entities exist in the graph, since one or more edges needed to match the pattern are missing.
