  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.
