3,785 research outputs found
Iteratively Learning Representations for Unseen Entities with Inter-Rule Correlations
Recent work on knowledge graph completion (KGC) focused on learning
embeddings of entities and relations in knowledge graphs. These embedding
methods require that all test entities are observed at training time, resulting
in a time-consuming retraining process for out-of-knowledge-graph (OOKG)
entities. To address this issue, current inductive knowledge embedding methods
employ graph neural networks (GNNs) to represent unseen entities by aggregating
information of known neighbors. They face three important challenges: (i) data
sparsity, (ii) the presence of complex patterns in knowledge graphs (e.g.,
inter-rule correlations), and (iii) the presence of interactions among rule
mining, rule inference, and embedding. In this paper, we propose a virtual
neighbor network with inter-rule correlations (VNC) that consists of three
stages: (i) rule mining, (ii) rule inference, and (iii) embedding. In the rule
mining process, to identify complex patterns in knowledge graphs, both logic
rules and inter-rule correlations are extracted from knowledge graphs based on
operations over relation embeddings. To reduce data sparsity, virtual neighbors
for OOKG entities are predicted and assigned soft labels by optimizing a
rule-constrained problem. We also devise an iterative framework to capture the
underlying relations between rule learning and embedding learning. In our
experiments, results on both link prediction and triple classification tasks
show that the proposed VNC framework achieves state-of-the-art performance on
four widely-used knowledge graphs. Further analysis reveals that VNC is robust
to the proportion of unseen entities and effectively mitigates data sparsity.Comment: Accepted at CIKM 202
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
Iteratively Learning Embeddings and Rules for Knowledge Graph Reasoning
Reasoning is essential for the development of large knowledge graphs,
especially for completion, which aims to infer new triples based on existing
ones. Both rules and embeddings can be used for knowledge graph reasoning and
they have their own advantages and difficulties. Rule-based reasoning is
accurate and explainable but rule learning with searching over the graph always
suffers from efficiency due to huge search space. Embedding-based reasoning is
more scalable and efficient as the reasoning is conducted via computation
between embeddings, but it has difficulty learning good representations for
sparse entities because a good embedding relies heavily on data richness. Based
on this observation, in this paper we explore how embedding and rule learning
can be combined together and complement each other's difficulties with their
advantages. We propose a novel framework IterE iteratively learning embeddings
and rules, in which rules are learned from embeddings with proper pruning
strategy and embeddings are learned from existing triples and new triples
inferred by rules. Evaluations on embedding qualities of IterE show that rules
help improve the quality of sparse entity embeddings and their link prediction
results. We also evaluate the efficiency of rule learning and quality of rules
from IterE compared with AMIE+, showing that IterE is capable of generating
high quality rules more efficiently. Experiments show that iteratively learning
embeddings and rules benefit each other during learning and prediction.Comment: This paper is accepted by WWW'1
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