Neighborhood Mixture Model for Knowledge Base Completion A revised version of our CoNLL 2016 paper to update latest related work.
Table 4: Micro-averaged accuracy results (in %) for triple classification on WN11 (labeled as W11) and FB13 (labeled as F13) test sets. Scores in bold and underline are the best and second best scores, respectively.
['[BOLD] Method', '[BOLD] W11', '[BOLD] F13']
The first 6 rows report the performance of models that use TransE to initialize the entity and relation vectors. The last 11 rows present the accuracy of models with randomly initialized parameters. It is not surprising as many entity names in WordNet and FreeBase are lexically meaningful. It is possible for all other embedding models to utilize the pre-trained word vectors as well. However, as pointed out by \newciteAAAI148531 and \newciteguu-miller-liang:2015:EMNLP, averaging the pre-trained word vectors for initializing entity vectors is an open problem and it is not always useful since entity names in many domain-specific KBs are not lexically meaningful. Our high results of the TransE model are probably due to a careful grid search and using the “Bernoulli” trick. Note that \newciteAAAI159571, \newciteji-EtAl:2015:ACL-IJCNLP and \newciteJiLH016 did not report the TransE results used for initializing TransR, TransD and TranSparse, respectively. They directly copied the TransE results previously reported in \newciteAAAI148531. So it is difficult to determine exactly how much TransR, TransD and TranSparse gain over TransE. These models might obtain better results than previously reported when the TransE used for initalization performs as well as reported in this paper. Furthermore, \newcitegarciaduran-bordes-usunier:2015: EMNLP, \newcitelin-EtAl:2015:EMNLP1, \newciteGarcia-DuranBUG15 and \newciteNickel:2016: HEK:3016100.3016172 also showed that for entity prediction TransE obtains very competitive results which are much higher than the TransE results originally published in \newciteNIPS2013_5071.