Semantic Communication With Conceptual Spaces

Wheeler, Dylan; Tripp, Erin E.; Natarajan, Balasubramaniam

doi:10.1109/lcomm.2022.3230246

Cited by 8 publications

(8 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Specifically, the performance begins to improve at around -20dB for the semantic system, while this does not occur until around 0dB for the non-semantic system. These findings are similar to those in our earlier work [7], and suggest that our approach can provide robustness in low-SNR scenarios.…”

Section: Resultssupporting

confidence: 91%

“…Therefore, there is currently no consensus on how semantic information should be modeled and quantified. In our previous work [7],…”

Section: A Related Workmentioning

confidence: 94%

“…For example, in our previous work we showed that effectively transmitting image semantics with conceptual spaces can reduce the required data rate by over 99% [7].…”

Section: B Building a Semantic Languagementioning

confidence: 99%

“…First and foremost, it forces us to reconsider fundamental notions, such as how we define and quantify information. In this article, we focus on the semantic level and draw upon insights from our previous work [7] where we first introduced the use of Peter Gärdenfors' theory of conceptual spaces [8], [9] as a framework for modeling semantics. We introduce for the first time both theoretical and practical aspects of our approach, and demonstrate the potential of conceptual spaces as a knowledge representation technique which can serve as the foundation for reasoning-driven semantic communication systems.…”

mentioning

confidence: 99%

See 3 more Smart Citations

Engineering Semantic Communication: A Survey

Wheeler

Natarajan

2023

IEEE Access

View full text Add to dashboard Cite

As the global demand for data has continued to rise exponentially, some have begun turning to the idea of semantic communication as a means of efficiently meeting this demand. Pushing beyond the boundaries of conventional communication systems, semantic communication focuses on the accurate recovery of the meaning conveyed from source to receiver, as opposed to the accurate recovery of transmitted symbols. In this survey, we aim to provide a comprehensive view of the history and current state of semantic communication and the techniques for engineering this higher level of communication. A survey of the current literature reveals four broad approaches to engineering semantic communication. We term the earliest of these approaches classical semantic information, which seeks to extend informationtheoretic results to include semantic information. A second approach makes use of knowledge graphs to achieve semantic communication, and a third utilizes the power of modern deep learning techniques to facilitate this communication. The fourth approach focuses on the significance of information, rather than its meaning, to achieve efficient, goal-oriented communication. We discuss each of these four approaches and their corresponding studies in detail, and provide some challenges and opportunities that pertain to each approach. Finally, we introduce a novel approach to semantic communication, which we term contextbased semantic communication. Inspired by the way in which humans naturally communicate with one another, this context-based approach provides a general, optimization-based design framework for semantic communication systems. Together, this survey provides a useful guide for the design and implementation of semantic communication systems.

show abstract

Section: Resultssupporting

confidence: 91%

“…Therefore, there is currently no consensus on how semantic information should be modeled and quantified. In our previous work [7],…”

Section: A Related Workmentioning

confidence: 94%

“…For example, in our previous work we showed that effectively transmitting image semantics with conceptual spaces can reduce the required data rate by over 99% [7].…”

Section: B Building a Semantic Languagementioning

confidence: 99%

mentioning

confidence: 99%

See 2 more Smart Citations

Engineering Semantic Communication: A Survey

Wheeler

Natarajan

2023

IEEE Access

View full text Add to dashboard Cite

show abstract

“…In addition to the aforementioned wireless SemCom techniques, the rapidly evolving state-of-the-art research landscape of SemCom also encompasses numerous SemCom techniques and trends such as cognitive SemCom [122]; implicit SemCom [123]; adaptive SemCom [124]; contextbased SemCom [99], [125]; digital SemCom [126], [127]; SemCom with conceptual spaces [128]; inverse SemCom [129]; one-to-many SemCom [130]; cooperative SemCom [131]; strategic SemCom [132]; and encrypted SemCom [133]. These wireless SemCom techniques have also been corroborated to outperform traditional wireless communication techniques in low SNR regimes.…”

Section: A Wireless Semcommentioning

confidence: 99%

Making Sense of Meaning: A Survey on Metrics for Semantic and Goal-Oriented Communication

2023

View full text Add to dashboard Cite

Semantic communication (SemCom) aims to convey the meaning behind a transmitted message by transmitting only semantically-relevant information. This semantic-centric design helps to minimize power usage, bandwidth consumption, and transmission delay. SemCom and goal-oriented SemCom (or effectiveness-level SemCom) are therefore promising enablers of 6G and developing rapidly. Despite the surge in their swift development, the design, analysis, optimization, and realization of robust and intelligent SemCom as well as goal-oriented SemCom are fraught with many fundamental challenges. One of the challenges is that the lack of unified/universal metrics of SemCom and goal-oriented SemCom can stifle research progress on their respective algorithmic, theoretical, and implementation frontiers. Consequently, this survey paper documents the existing metrics -scattered in many references -of wireless SemCom, optical SemCom, quantum SemCom, and goal-oriented wireless SemCom. By doing so, this paper aims to inspire the design, analysis, and optimization of a wide variety of SemCom and goal-oriented SemCom systems. This article also stimulates the development of unified/universal performance assessment metrics of SemCom and goal-oriented SemCom, as the existing metrics are purely statistical and hardly applicable to reasoning-type tasks that constitute the heart of 6G and beyond.

show abstract

Reasoning with Concepts: A Unifying Framework

Gärdenfors

Osta-Vélez

2023

Minds & Machines

View full text Add to dashboard Cite

Over the past few decades, cognitive science has identified several forms of reasoning that make essential use of conceptual knowledge. Despite significant theoretical and empirical progress, there is still no unified framework for understanding how concepts are used in reasoning. This paper argues that the theory of conceptual spaces is capable of filling this gap. Our strategy is to demonstrate how various inference mechanisms which clearly rely on conceptual information—including similarity, typicality, and diagnosticity-based reasoning—can be modeled using principles derived from conceptual spaces. Our first topic analyzes the role of expectations in inductive reasoning and their relation to the structure of our concepts. We examine the relationship between using generic expressions in natural language and common-sense reasoning as a second topic. We propose that the strength of a generic can be described by distances between properties and prototypes in conceptual spaces. Our third topic is category-based induction. We demonstrate that the theory of conceptual spaces can serve as a comprehensive model for this type of reasoning. The final topic is analogy. We review some proposals in this area, present a taxonomy of analogical relations, and show how to model them in terms of distances in conceptual spaces. We also briefly discuss the implications of the model for reasoning with concepts in artificial systems.

show abstract

Semantic Communication With Conceptual Spaces

Cited by 8 publications

References 13 publications

Engineering Semantic Communication: A Survey

Engineering Semantic Communication: A Survey

Making Sense of Meaning: A Survey on Metrics for Semantic and Goal-Oriented Communication

Reasoning with Concepts: A Unifying Framework

Contact Info

Product

Resources

About