论文标题
使用图像识别和知识图嵌入学习语义图像属性
Learning semantic Image attributes using Image recognition and knowledge graph embeddings
论文作者
论文摘要
传统上，从文本中提取结构化知识已用于知识基础。但是，可以将其他信息来源（例如图像）利用到此过程中，以建立更完整，更丰富的知识库。图像和知识图嵌入内容的结构化语义表示可以提供图像实体之间语义关系的独特表示。多年来，在知识图中链接已知实体和使用语言模型学习开放世界图像引起了很多兴趣。在本文中，我们提出了一种共享的学习方法，通过将知识图嵌入模型与图像的公认属性相结合，以学习图像的语义属性。提出的模型前提，以帮助我们了解图像实体之间的语义关系，并通过知识图嵌入模型为提取的实体提供链接。在使用有限数据的自定义用户定义的知识库的局限性下，该模型提出了明显的准确性，并为早期方法提供了新的替代方案。提出的方法是迈出框架之间差距的一步，这些框架是从大量数据和框架中学习的，这些数据和框架使用有限的谓词来推断新知识。
Extracting structured knowledge from texts has traditionally been used for knowledge base generation. However, other sources of information, such as images can be leveraged into this process to build more complete and richer knowledge bases. Structured semantic representation of the content of an image and knowledge graph embeddings can provide a unique representation of semantic relationships between image entities. Linking known entities in knowledge graphs and learning open-world images using language models has attracted lots of interest over the years. In this paper, we propose a shared learning approach to learn semantic attributes of images by combining a knowledge graph embedding model with the recognized attributes of images. The proposed model premises to help us understand the semantic relationship between the entities of an image and implicitly provide a link for the extracted entities through a knowledge graph embedding model. Under the limitation of using a custom user-defined knowledge base with limited data, the proposed model presents significant accuracy and provides a new alternative to the earlier approaches. The proposed approach is a step towards bridging the gap between frameworks which learn from large amounts of data and frameworks which use a limited set of predicates to infer new knowledge.
