Representation Learning for Large Knowledge Graph Completion

大型知识图补全的表示学习

• 批准号: 2739427
• 金额: --
• 依托单位: King's College London
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2022
• 资助国家: 英国
• 起止时间: 2022 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2739427
• 关键词: Representation; Learning; Large; Knowledge; Graph

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because they do not contain all the existing knowledge of a domain and the knowledge they do contain may become outdated.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?

知识图是一种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，而且它们所包含的知识可能会过时。因此，知识图谱补全就是在知识图谱中添加缺失知识的过程。识别这些缺失信息的最突出的方法之一是使用知识图嵌入方法来学习表示。大量的研究集中在基于知识图谱的结构学习这种表示，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，这意味着它们只对图的现有元素起作用，而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Wikidata）的可扩展性，它有超过1亿个节点。基于上述，本研究项目将重点关注以下问题：i)如何使用一个庞大且不断发展的知识图谱的结构（例如节点周围路径中的模式）来预测缺失链接？iii)知识图嵌入在现实世界的知识图中工作到什么程度？ii)哪些学习策略最适合基础知识图嵌入模型？