Learning Structured Embeddings of Knowledge Bases
Learning Structured Embeddings of Knowledge Bases
复制标题
DOI：
10.1609/aaai.v25i1.7917
复制
发表时间：
2011-08
期刊：
影响因子：
--
通讯作者：
Antoine Bordes;J. Weston;R. Collobert;Yoshua Bengio
中科院分区：
文献类型：
--
作者：
Antoine Bordes;J. Weston;R. Collobert;Yoshua Bengio
许多知识库（KBs）现在都很容易获得，并且由于长期的资金投入（例如WordNet，OpenCyc）或协作过程（例如Freebase，DBpedia），包含了大量的信息。然而，它们中的每一个都基于不同的严格符号框架，这使得它们的数据很难在其他系统中使用。这是不幸的，因为如此丰富的结构化知识可能会导致人工智能的许多其他领域的巨大飞跃，如自然语言处理（词义消歧，自然语言理解等），视觉（场景分类、图像语义标注等）或者协同过滤。在本文中，我们提出了一种基于创新的神经网络架构的学习过程，该架构旨在将这些符号表示中的任何一种嵌入到一个更灵活的连续向量空间中，在该空间中保留并增强了原始知识。这些学习的嵌入将使来自任何KB的数据能够轻松地用于最近的机器学习方法中，用于预测和信息检索。我们说明了我们的方法WordNet和Freebase，也提出了一种方法来适应它的知识提取从原始文本。
Many Knowledge Bases (KBs) are now readily available and encompass colossal quantities of information thanks to either a long-term funding effort (e.g. WordNet, OpenCyc) or a collaborative process (e.g. Freebase, DBpedia). However, each of them is based on a different rigorous symbolic framework which makes it hard to use their data in other systems. It is unfortunate because such rich structured knowledge might lead to a huge leap forward in many other areas of AI like nat- ural language processing (word-sense disambiguation, natural language understanding, ...), vision (scene classification, image semantic annotation, ...) or collaborative filtering. In this paper, we present a learning process based on an innovative neural network architecture designed to embed any of these symbolic representations into a more flexible continuous vector space in which the original knowledge is kept and enhanced. These learnt embeddings would allow data from any KB to be easily used in recent machine learning meth- ods for prediction and information retrieval. We illustrate our method on WordNet and Freebase and also present a way to adapt it to knowledge extraction from raw text.