Representation Learning for Large Knowledge Graph Completion

知识图是以机器可读格式表示知识的数据结构，在搜索引擎、推荐系统和数字助理等多个应用中被广泛使用。这样的知识图谱通常是不完整的，因为它们不包含一个领域的所有现有知识，并且它们确实包含的知识可能会过时。因此，知识图补全是在知识图中添加缺失知识的过程。识别这种缺失信息的最重要的方法之一是使用知识图嵌入方法来学习表示法。大量的研究集中在基于知识图的结构来学习这种表示，直到最近，研究人员才开始探索使用其他形式，如文本、图像和音频来产生更好的表示。此外，大多数可用的嵌入方法都不是归纳的，这意味着它们只作用于图的现有元素，不能适应随着领域知识的发展而添加的新元素。最后，嵌入方法的一个重要考虑因素是它们对知识图(如拥有超过1亿个节点的Wikidata)的可伸缩性。在此基础上，本研究项目将集中于以下问题：i)如何利用大型演化知识图的结构(例如节点周围路径的模式)来预测缺失链接？iii)知识图嵌入在多大程度上适用于现实世界的知识图？ii)哪些学习策略最适合基本知识图嵌入模型？

Knowledge graphs are data structures that represent knowledge in machine readable format and are being used extensively in several applications including search engines, recommender systems and digital assistants. Such Knowledge Graphs are usually incomplete because they do not contain all the existing knowledge of a domain and the knowledge they do contain may become outdated. As such, Knowledge Graph completion is the process of adding missing knowledge in a Knowledge Graph. One of the most prominent approaches for identifying this missing information is using Knowledge Graph embedding methods to learn representations. A large body of research has focused on learning such representations based on the structure of the Knowledge Graph and only recently researchers have started exploring the use of additional modalities such as text, images, and audio to produce better representations. Moreover, most of the available embedding methods are not inductive, which means that they only work on existing elements of the graph and cannot accommodate new elements being added as the knowledge of a domain evolves. Finally, an important consideration for embedding methods is their scalability to Knowledge Graphs such as Wikidata, which has more than 100 million nodes. Based on the above, this research project will focus on the following questions:i) How can the structure (e.g. patterns in paths surrounding nodes) of a large and evolving Knowledge Graph be used to predict missing links?iii) To what extent do Knowledge Graph embeddings work in real-world Knowledge Graphs?ii) Which learning strategies are best suited to foundational Knowledge Graph embedding models?