Life-long Learning for Reasoning-based Semantic Communication

Semantic communication is an emerging paradigm that focuses on understanding and delivering semantics or meaning of messages. Most existing semantic communication solutions define semantic meaning as the labels of objects recognized from a given form of source signal, while ignoring intrinsic information that cannot be directly observed. Since the models for recognizing labels need to be pre-trained with labelled dataset, the total number of semantic objects are often limited by a fixed set. In this paper, we propose a novel reasoning-based semantic communication architecture in which the semantic meaning is represented by a graph-based knowledge structure in terms of semantic-entity, relationships, and reasoning rules. An embedding-based semantic interpretation framework is proposed to convert the high-dimensional graph-based representation of semantic meaning into a low-dimensional representation, which is efficient for channel transmission. We develop a novel inference function-based approach that can automatically infer hidden information such as missing entities and relations that cannot be directly observed from the message. Finally, we introduce a life-long model updating approach in which the receiver can learn from previously received messages and automatically update the rules for reasoning the hidden information when new unknown semantic entities and relations have been discovered. Extensive experiments are conducted based on a real-world knowledge database and numerical results show that our proposed solution achieves 76% interpretation accuracy of the hidden meaning at the receiver when some entities are missing in the transmitted message.