Comments (4)
请问最终得到的sim_mat矩阵的含义是什么,对应位置的元素代表测试数据中KG1和KG2两两实体的相似度吗?
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同学你好,
感谢你对我们工作的关注。
OpenEA是面向知识图谱(KG)实体对齐任务的工具包,类似于你提到的实体链接。train_links的已知的对齐实体对,用于训练模型。输出文件中的alignment_results_12是预测的实体对的id,kg1_ent_embeds_txt等文件是输出的embeddings (text format)。sim_mat是相似度矩阵。
目前OpenEA不支持非KG场景的对齐任务。不过,你可以用其他表示学习技术给实体学习embeddings,对齐方法可以借鉴OpenEA里面的。
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感谢作者解答,还有一个问题想请教下作者,我看到测试数据test_links也是一些实体对,这些实体对不一定是对齐的吧?还有想问的就是如果KG中有些实体没有关系的话是否这些实体就没有对应的embedding表示了,因为看到代码中实体编号只是从关系三元组中的实体得到的,没有加上属性三元组中的实体:ent_ids1, ent_ids2 = generate_mapping_id(kg1.relation_triples_set, kg1.entities_set,kg2.relation_triples_set, kg2.entities_set),而entities_set则是包含了关系和属性中所有涉及到的实体,这个地方貌似有点问题,希望能得到解答,感谢。
from openea.
同学你好,很抱歉回复晚了。
当前数据集下面test_links都是对齐的。
目前数据集里面实体都是有关系的,然后因为目前的方法都考虑了关系,所以我先抽取的关系图和其中的实体,然后抽取这些实体的属性,所以有属性的实体都是有关系的。这里,未来工作,大家可以考虑更加复杂的情况。欢迎探索!
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Related Issues (20)
- 关于entity name
- 关于RDF图数据融合 HOT 2
- How to resolve DBPedia and Yago Resource IDs? HOT 3
- 输出文件alignment_results_12的一些问题 HOT 2
- ValueError: Input contains NaN, infinity or a value too large for dtype('float32') HOT 4
- Could you please release the Google Translation version for cross-lingual datasets? HOT 2
- 配置环境的问题
- Marginal Ranking and Background Ranking HOT 1
- word embedding file: wiki-news-300d-1M.vec HOT 1
- Accessing aligned pairs of trained model HOT 1
- 请问下没有GPU,CPU可以运行吗? HOT 2
- 实体对齐后的步骤 HOT 2
- tensorflow version issue HOT 1
- AttributeError: 'Graph' object has no attribute 'label' HOT 1
- Alinet的实验结果
- graph-tool version 2.29 not available through conda HOT 1
- 关于在使用python语句运行程序时的问题 HOT 1
- 关于评价指标的问题
- ValueError: Cannot feed value of shape (20480,) for Tensor triple_placeholder/Variable/Read/ReadVariableOp:0, which has shape (1,) HOT 1
- ModuleNotFoundError: No module named 'tensorflow'请问碰到类似问题如何解决 HOT 1
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