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Found 9 papers, 5 papers with code
SlotGAN: Detecting Mentions in Text via Adversarial Distant Learning
We present SlotGAN, a framework for training a mention detection model that only requires unlabeled text and a gazetteer.
GRAPES: Learning to Sample Graphs for Scalable Graph Neural Networks
Graph neural networks (GNNs) learn the representation of nodes in a graph by aggregating the neighborhood information in various ways.
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.
Harnessing the Web and Knowledge Graphs for Automated Impact Investing Scoring
The Sustainable Development Goals (SDGs) were introduced by the United Nations in order to encourage policies and activities that help guarantee human prosperity and sustainability.
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.
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.
Complex Query Answering with Neural Link Predictors
Finally, we demonstrate that it is possible to explain the outcome of our model in terms of the intermediate solutions identified for each of the complex query atoms.
Ranked #1 on
Complex Query Answering
on NELL995
Inductive Entity Representations from Text via Link Prediction
However, the extent to which these representations learned for link prediction generalize to other tasks is unclear.
Ranked #1 on
Inductive knowledge graph completion
on WN18RR-ind
Inductive knowledge graph completion
Inductive Link Prediction
+8
Message Passing Query Embedding
The generality of our method allows it to encode a more diverse set of query types in comparison to previous work.
