EMANTICALLY-GUIDED HYBRID TRAVERSAL FOR COMPLEX QUESTION ANSWERING OVER KNOWLEDGE GRAPHS

Knowledge Graph Question Answering (KG-QA) requires strong and effective navigation of complex graph architectures to obtain precise responses from natural language inquiries. Conventional traversal techniques such as breadth-first search (BFS), depth-first search (DFS) , random walks and transformer based approaches often face efficiency challenges like either computationally expensive, prone to combinatorial explosion, lacking semantic focus and failing to scale when applied to large, intricate RDF graphs. To resolve this, we propose an innovative hybrid traversal framework that combines semantic filtering, embedding-based prioritization, and parallel exploration within the RDFLib environment. Our proposed work gives an innovative hybrid traversal framework that effectively combines semantic filtering, embedding-based prioritization and simultaneous exploration within the RDFLib environment. (i) Semantic filtering provides ontology-based restrictions to methodically remove irrelevant pathways, clarity and concentration. (ii) Embedding guided ranking gives knowledge graph embedding to semantically elevate the prioritization of adjacent nodes improving the relevance of traversal choices and (iii) concurrent exploration speeds up the retrieval procedure by simultaneously examining several top ranked paths and scalability. Unlike previous methods, our framework explicitly balances semantic relevance, graph structure, and multi-hop reasoning, while maintaining interpretable paths for answer explanation. The suggested framework undergoes thorough evaluation using a FB15K-237 benchmark dataset of knowledge graph where results demonstrate that our method achieves better performance over baseline models. Initial results show that this combined method provides accuracy, decreases response time, and scalable answer to elevate the capabilities in KG-QA systems.