2 research outputs found
Active Learning for Entity Alignment
In this work, we propose a novel framework for the labeling of entity
alignments in knowledge graph datasets. Different strategies to select
informative instances for the human labeler build the core of our framework. We
illustrate how the labeling of entity alignments is different from assigning
class labels to single instances and how these differences affect the labeling
efficiency. Based on these considerations we propose and evaluate different
active and passive learning strategies. One of our main findings is that
passive learning approaches, which can be efficiently precomputed and deployed
more easily, achieve performance comparable to the active learning strategies.Comment: to be published in ECIR'21; fix typo and add acknowledgemen
Meta Diagram based Active Social Networks Alignment
Network alignment aims at inferring a set of anchor links matching the shared
entities between different information networks, which has become a
prerequisite step for effective fusion of multiple information networks. In
this paper, we will study the network alignment problem to fuse online social
networks specifically. Social network alignment is extremely challenging to
address due to several reasons, i.e., lack of training data, network
heterogeneity and one-to-one constraint. Existing network alignment works
usually require a large number of training data, but such a demand can hardly
be met in applications, as manual anchor link labeling is extremely expensive.
Significantly different from other homogeneous network alignment works,
information in online social networks is usually of heterogeneous categories,
the incorporation of which in model building is not an easy task. Furthermore,
the one-to-one cardinality constraint on anchor links renders their inference
process intertwistingly correlated. To resolve these three challenges, a novel
network alignment model, namely ActiveIter, is introduced in this paper.
ActiveIter defines a set of inter-network meta diagrams for anchor link feature
extraction, adopts active learning for effective label query and uses greedy
link selection for anchor link cardinality filtering. Extensive experiments are
conducted on real-world aligned networks datasets, and the experimental results
have demonstrated the effectiveness of ActiveIter compared with other
state-of-the-art baseline methods.Comment: Published at ICDE 201