Search Results for author:
Found 12 papers, 8 papers with code
Beyond Transduction: A Survey on Inductive, Few Shot, and Zero Shot Link Prediction in Knowledge Graphs
Consequently, a larger body of works focuses on the completion of missing information in KGs, which is commonly referred to as link prediction (LP).
Knowledge Graphs for the Life Sciences: Recent Developments, Challenges and Opportunities
The term life sciences refers to the disciplines that study living organisms and life processes, and include chemistry, biology, medicine, and a range of other related disciplines.
PyGraft: Configurable Generation of Synthetic Schemas and Knowledge Graphs at Your Fingertips
In some data-sensitive fields such as education or medicine, access to public datasets is even more limited.
Relevant Entity Selection: Knowledge Graph Bootstrapping via Zero-Shot Analogical Pruning
Knowledge Graph Construction (KGC) can be seen as an iterative process starting from a high quality nucleus that is refined by knowledge extraction approaches in a virtuous loop.
Schema First! Learn Versatile Knowledge Graph Embeddings by Capturing Semantics with MASCHInE
These models learn a vector representation of knowledge graph entities and relations, a. k. a.
Treat Different Negatives Differently: Enriching Loss Functions with Domain and Range Constraints for Link Prediction
In an extensive and controlled experimental setting, we show that the proposed loss functions systematically provide satisfying results which demonstrates both the generality and superiority of our proposed approach.
Sem@$K$: Is my knowledge graph embedding model semantic-aware?
That is why, in this paper, we extend our previously introduced metric Sem@K that measures the capability of models to predict valid entities w. r. t.
DAGOBAH: Table and Graph Contexts for Efficient Semantic Annotation of Tabular Data
In this paper, we present the latest improvements of the DAGOBAH system that performs automatic pre-processing and semantic interpretation of tables.
Ranked #1 on
Column Type Annotation
on ToughTables-WD
Investigating ADR mechanisms with knowledge graph mining and explainable AI
We propose to mine knowledge graphs for identifying biomolecular features that may enable reproducing automatically expert classifications that distinguish drug causative or not for a given type of ADR.
Discovering alignment relations with Graph Convolutional Networks: a biomedical case study
In this article, we propose to match nodes within a knowledge graph by (i) learning node embeddings with Graph Convolutional Networks such that similar nodes have low distances in the embedding space, and (ii) clustering nodes based on their embeddings, in order to suggest alignment relations between nodes of a same cluster.
Tackling scalability issues in mining path patterns from knowledge graphs: a preliminary study
Features mined from knowledge graphs are widely used within multiple knowledge discovery tasks such as classification or fact-checking.
Knowledge-Based Matching of $n$-ary Tuples
In particular, units should be matched within and across sources, and their level of relatedness should be classified into equivalent, more specific, or similar.
