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This article introduces a hybrid architecture that combines large language models (LLMs) with an external ontology memory layer. By automatically constructing RDF/OWL knowledge graphs, it achieves persistent and verifiable semantic reasoning, significantly improving the performance of multi-step reasoning tasks.
Section 01
This article proposes a hybrid intelligent system architecture that combines large language models (LLMs) with an external ontology memory layer. Using RDF/OWL knowledge graphs, it achieves persistent and verifiable semantic reasoning, addressing the problem of LLMs' lack of structured long-term memory and significantly improving the performance of multi-step reasoning tasks.
Section 02
LLMs rely on static parameter knowledge (cannot be updated) and context retrieval (similarity matching, lacking structural understanding). They have three major flaws: inability to persist interaction history, difficulty maintaining structured relationships between concepts, and lack of explicit tracking and verification in multi-step reasoning—all of which limit the construction of reliable intelligent agents.
Section 03
The ontology memory layer uses RDF triples and OWL logical rules to represent knowledge, supporting semantic reasoning and consistency verification. In the hybrid reasoning architecture, LLMs access both vector-retrieved text fragments and structured knowledge from the ontology graph simultaneously, forming a closed loop of generation-verification-correction.
Section 04
The pipeline consists of four steps: 1. Entity recognition and linking (mapping to standard concepts); 2. Relation extraction and standardization (unified expression); 3. Triple verification (SHACL constraints + OWL semantic checks); 4. Continuous update (conflict resolution + knowledge completion).
Section 05
Experiments compared pure LLMs, RAG-enhanced LLMs, and ontology-enhanced LLMs. The ontology-enhanced approach showed more stable success rates in multi-disk Tower of Hanoi problems, with advantages including explicit rule maintenance, reliable state tracking, systematic planning, and improved interpretability.
Section 06
Applicable scenarios include enterprise knowledge management (integrating heterogeneous knowledge), intelligent customer service (long-term memory and cross-session reasoning), and robot systems (maintaining dynamic world models).
Section 07
It faces three major challenges: knowledge acquisition bottlenecks, scale-efficiency trade-offs, and multi-modal fusion. Future directions include integrating active learning, optimizing engineering implementations, and multi-modal technologies.
Section 08
The ontology-enhanced architecture combines the advantages of neural networks and symbolic systems, serving as a key path to building reliable intelligent systems. It will demonstrate value in more scenarios in the future.
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