  In relation extraction, a key process is to obtain good detectors that find
relevant sentences describing the target relation. To minimize the necessity of
labeled data for refining detectors, previous work successfully made use of
BabelNet, a semantic graph structure expressing relationships between synsets,
as side information or prior knowledge. The goal of this paper is to enhance
the use of graph structure in the framework of random walk with a few
adjustable parameters. Actually, a straightforward application of random walk
degrades the performance even after parameter optimization. With the insight
from this unsuccessful trial, we propose SynsetRank, which adjusts the initial
probability so that high degree nodes influence the neighbors as strong as low
degree nodes. In our experiment on 13 relations in the FB15K-237 dataset,
SynsetRank significantly outperforms baselines and the plain random walk
approach.
