778 research outputs found
Efficient Federated Learning on Knowledge Graphs via Privacy-preserving Relation Embedding Aggregation
Federated Learning (FL) on knowledge graphs (KGs) has yet to be as well
studied as other domains, such as computer vision and natural language
processing. A recent study FedE first proposes an FL framework that shares
entity embeddings of KGs across all clients. However, compared with model
sharing in vanilla FL, entity embedding sharing from FedE would incur severe
privacy leakage. Specifically, the known entity embedding can be used to infer
whether a specific relation between two entities exists in a private client. In
this paper, we first develop a novel attack that aims to recover the original
data based on embedding information, which is further used to evaluate the
vulnerabilities of FedE. Furthermore, we propose a Federated learning paradigm
with privacy-preserving Relation embedding aggregation (FedR) to tackle the
privacy issue in FedE. Compared to entity embedding sharing, relation embedding
sharing policy can significantly reduce the communication cost due to its
smaller size of queries. We conduct extensive experiments to evaluate FedR with
five different embedding learning models and three benchmark KG datasets.
Compared to FedE, FedR achieves similar utility and significant (nearly 2X)
improvements in both privacy and efficiency on link prediction task.Comment: Accepted to ACL 2022 Workshop on Federated Learning for Natural
Language Processin
SE-KGE: A Location-Aware Knowledge Graph Embedding Model for Geographic Question Answering and Spatial Semantic Lifting
Learning knowledge graph (KG) embeddings is an emerging technique for a
variety of downstream tasks such as summarization, link prediction, information
retrieval, and question answering. However, most existing KG embedding models
neglect space and, therefore, do not perform well when applied to (geo)spatial
data and tasks. For those models that consider space, most of them primarily
rely on some notions of distance. These models suffer from higher computational
complexity during training while still losing information beyond the relative
distance between entities. In this work, we propose a location-aware KG
embedding model called SE-KGE. It directly encodes spatial information such as
point coordinates or bounding boxes of geographic entities into the KG
embedding space. The resulting model is capable of handling different types of
spatial reasoning. We also construct a geographic knowledge graph as well as a
set of geographic query-answer pairs called DBGeo to evaluate the performance
of SE-KGE in comparison to multiple baselines. Evaluation results show that
SE-KGE outperforms these baselines on the DBGeo dataset for geographic logic
query answering task. This demonstrates the effectiveness of our
spatially-explicit model and the importance of considering the scale of
different geographic entities. Finally, we introduce a novel downstream task
called spatial semantic lifting which links an arbitrary location in the study
area to entities in the KG via some relations. Evaluation on DBGeo shows that
our model outperforms the baseline by a substantial margin.Comment: Accepted to Transactions in GI
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