Deep Knowledge-aware network

Author: shixiaowen03

recommendation/Basic-DKN-Demo/kg/Fast-TransX/LICENSE

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+MIT License
+
+Copyright (c) 2017 THUNLP
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

recommendation/Basic-DKN-Demo/kg/Fast-TransX/README.md

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+# Fast-TransX
+
+An extremely fast implementation of TransE [1], TransH [2], TransR [3], TransD [4], TranSparse [5] for knowledge representation learning (KRL) based on our previous pakcage KB2E ("https://github.com/thunlp/KB2E") for KRL. The overall framework is similar to KB2E, with some underlying design changes for acceleration. This implementation also supports multi-threaded training to save time.
+
+# Evaluation Results
+
+Because the overall framework is similar, we just list the result of transE(previous model) and new implemented models in datesets FB15k and WN18.
+
+CPU : Intel Core i7-6700k 4.00GHz.
+
+FB15K:
+
+| Model | MeanRank(Raw)	| MeanRank(Filter)	| Hit@10(Raw)	| Hit@10(Filter)|Time|
+| ----- |:-------------:| :----------------:|:-----------:|:-------------:|:---:|
+|TransE (n = 50, rounds = 1000)|210|82|41.9|61.3|3587s|
+|Fast-TransE (n = 50, threads = 8, rounds = 1000)|205|69|43.8|63.5|42s|
+|Fast-TransH (n = 50, threads = 8, rounds = 1000)|202|67|43.7|63.0|178s|
+|Fast-TransR (n = 50, threads = 8, rounds = 1000)|196|73|48.8|69.8|1572s|
+|Fast-TransD (n = 100, threads = 8, rounds = 1000)|236|95|49.9|75.2|231s|
+
+
+WN18:
+
+| Model | MeanRank(Raw)	| MeanRank(Filter)	| Hit@10(Raw)	| Hit@10(Filter)|Time|
+| ----- |:-------------:| :----------------:|:-----------:|:-------------:|:---:|
+|TransE (n = 50, rounds = 1000)|251|239|78.9|89.8|1674s|
+|Fast-TransE (n = 50, threads = 8, rounds = 1000)|273|261|71.5|83.3|12s|
+|Fast-TransH (n = 50, threads = 8, rounds = 1000)|285|272|79.8|92.5|121s|
+|Fast-TransR (n = 50, threads = 8, rounds = 1000)|284|271|81.0|94.6|296s|
+|Fast-TransD (n = 100, threads = 8, rounds = 1000)|309|297|78.5|91.9|201s|
+
+More results can be found in ("https://github.com/thunlp/KB2E").
+
+# Data
+
+Datasets are required in the following format, containing three files:
+
+triple2id.txt: training file, the first line is the number of triples for training. Then the follow lines are all in the format (e1, e2, rel).
+
+entity2id.txt: all entities and corresponding ids, one per line. The first line is the number of entities.
+
+relation2id.txt: all relations and corresponding ids, one per line. The first line is the number of relations.
+
+You can download FB15K from [[Download]](http://pan.baidu.com/s/1eRD9B4A), and the more datasets can also be found in ("https://github.com/thunlp/KB2E").
+
+# Compile
+
+g++ transX.cpp -o transX -pthread -O3 -march=native
+
+# Citation
+
+If you use the code, please kindly cite the following paper and other papers listed in our reference:
+
+Yankai Lin, Zhiyuan Liu, Maosong Sun, Yang Liu, Xuan Zhu. Learning Entity and Relation Embeddings for Knowledge Graph Completion. The 29th AAAI Conference on Artificial Intelligence (AAAI'15). [[pdf]](http://nlp.csai.tsinghua.edu.cn/~lzy/publications/aaai2015_transr.pdf)
+
+# Reference
+
+[1] Bordes, Antoine, et al. Translating embeddings for modeling multi-relational data. Proceedings of NIPS, 2013.
+
+[2]	Zhen Wang, Jianwen Zhang, et al. Knowledge Graph Embedding by Translating on Hyperplanes. Proceedings of AAAI, 2014.
+
+[3] Yankai Lin, Zhiyuan Liu, et al. Learning Entity and Relation Embeddings for Knowledge Graph Completion. Proceedings of AAAI, 2015.
+
+[4] Guoliang Ji, Shizhu He, et al. Knowledge Graph Embedding via Dynamic Mapping Matrix. Proceedings of ACL, 2015.
+
+[5] Guoliang Ji, Kang Liu, et al. Knowledge Graph Completion with Adaptive Sparse Transfer Matrix. Proceedings of AAAI, 2016.