17,952 research outputs found
A Study on Knowledge Graph Embeddings and Graph Neural Networks for Web Of Things
Graph data structures are widely used to store relational information between
several entities. With data being generated worldwide on a large scale, we see
a significant growth in the generation of knowledge graphs. Thing in the future
is Orange's take on a knowledge graph in the domain of the Web Of Things (WoT),
where the main objective of the platform is to provide a digital representation
of the physical world and enable cross-domain applications to be built upon
this massive and highly connected graph of things. In this context, as the
knowledge graph grows in size, it is prone to have noisy and messy data. In
this paper, we explore state-of-the-art knowledge graph embedding (KGE) methods
to learn numerical representations of the graph entities and, subsequently,
explore downstream tasks like link prediction, node classification, and triple
classification. We also investigate Graph neural networks (GNN) alongside KGEs
and compare their performance on the same downstream tasks. Our evaluation
highlights the encouraging performance of both KGE and GNN-based methods on
node classification, and the superiority of GNN approaches in the link
prediction task. Overall, we show that state-of-the-art approaches are relevant
in a WoT context, and this preliminary work provides insights to implement and
evaluate them in this context
Interaction Embeddings for Prediction and Explanation in Knowledge Graphs
Knowledge graph embedding aims to learn distributed representations for
entities and relations, and is proven to be effective in many applications.
Crossover interactions --- bi-directional effects between entities and
relations --- help select related information when predicting a new triple, but
haven't been formally discussed before. In this paper, we propose CrossE, a
novel knowledge graph embedding which explicitly simulates crossover
interactions. It not only learns one general embedding for each entity and
relation as most previous methods do, but also generates multiple triple
specific embeddings for both of them, named interaction embeddings. We evaluate
embeddings on typical link prediction tasks and find that CrossE achieves
state-of-the-art results on complex and more challenging datasets. Furthermore,
we evaluate embeddings from a new perspective --- giving explanations for
predicted triples, which is important for real applications. In this work, an
explanation for a triple is regarded as a reliable closed-path between the head
and the tail entity. Compared to other baselines, we show experimentally that
CrossE, benefiting from interaction embeddings, is more capable of generating
reliable explanations to support its predictions.Comment: This paper is accepted by WSDM201
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