CSP-106: Building a Cross-Domain Concept Representation Framework with 106 Semantic Primitives

CSP-106 is a semantic primitive-based concept modeling framework that represents complex concepts as interconnected nodes using 106 indivisible basic semantic units, supporting cross-domain reasoning and visualization. Its core value lies in providing a highly interpretable structured knowledge representation method, which can collaborate with large models to enhance the capabilities of AI systems.

The concept of semantic primitives originates from the Natural Semantic Metalanguage (NSM) theory, which holds that there exists a set of core words in all human languages with three key characteristics: undefinable, universally present, and semantically primitive (e.g., "I", "you", "something", "do", etc.). CSP-106 is built based on the linguistic insight that "complex concepts can be traced back to basic semantic units."

The CSP-106 framework consists of three core components:

1. Primitive Dictionary: Defines the types, grammatical behaviors, and internal relationships of the 106 primitives;
2. Concept Graph: Decomposes complex concepts (e.g., "purchase") into combinations of primitive nodes;
3. Reasoning Engine: Performs semantic reasoning based on the graph, which is more interpretable than word vectors.

CSP-106 supports cross-domain concept migration, with potential applications including:

• Knowledge graph construction: Providing a standardized semantic foundation;
• Natural language understanding: Improving parsing accuracy and interpretability;
• Cross-language processing: Optimizing translation alignment using the universality of primitives;
• Concept visualization: Transforming abstract concepts into node network diagrams.

CSP-106 can complement large models in the following ways:

• Enhanced interpretability: Mapping model outputs to the primitive level;
• Semantic constraints: Reducing hallucinations and biases;
• Knowledge injection: Improving performance in specific domains.

CSP-106 faces three major challenges:

1. Ambiguous primitive boundaries: Academic disputes still exist;
2. Cultural specificity: Insufficient validation for non-Western languages;
3. Computational complexity: Performance bottlenecks in large-scale applications.

CSP-106 returns to the essence of semantics, providing interpretability and structural capabilities in the era of deep learning. Whether as an independent tool or in collaboration with large models, it deserves continuous attention.