A Zero-Shot Neuro-Symbolic Approach for Complex Knowledge Graph Question Answering (2025)

Abstract: Existing low-resource Knowledge Graph Question Answering (KGQA) methods rely heavily on Large Language Models (LLMs) for semantic parsing of natural language question to its corresponding logical form (LF) such as SPARQL, S-Expression, etc. However, LLMs becomes bottleneck for practical applications due to: (i) its high computational resource requirements; (2) limited knowledge of LLM about different LFs; (3) unavailability of low-resource annotated data for new KGs and settings. This motivates us to design a KGQA framework that can operate in a zero-shot setting without the need for additional resources. In this paper, we propose (NS-KGQA): a zero-shot neuro-symbolic approach based on neural KG embeddings that have demonstrated their ability to effectively model KG structure without the need of additional data. We extract a link-prediction based symbolic question subgraph. We then propose a Symbolic Resolver that uses Dual KG Embeddings combined with a symbolic approach to resolve the symbolic question subgraph. Our extensive experiments on Complex KGQA benchmarks such as KQA Pro demonstrate the effectiveness of our approach. NS-KGQA outperforms all other LLM-based zero-shot baselines by 26% (avg).

## introduction research has moved towards low-resource knowledge graph question answering (kgqa) @xcite due to the scarcity of annotated data. kgqa aims to answer a natural language question using the facts present in the knowledge graph (kg) in the form of triples (h, r, t) by producing a logical form (lf) such as sparql, s-expression, etc. that is executed on these kg facts to retrieve the answer(s) @xcite @xcite . due to the recent surge in the complexity of the questions users pose to the systems, complex kgqa has particularly gained attention @xcite @xcite . llm-based low-resource methods have shown promising results with various state-of-the-art llms such as @xcite , @xcite variants to generate the lf. however, for practical applications, these llms become bottleneck due to: (1) high costs and computational resource requirements associated to it; (2) limited pre-trained knowledge about the grammar and semantics of the lf (sparql, s-expression, etc.) and hence limited generative capability; (3) unavailability of low-resource annotated data for new kgs and settings at cold-start. moreover, these methods perform inferior for more complex kgqa benchmarks such as kqa pro @xcite as these benchmarks require explicit modeling of kg complexities such as concepts, attributes, qualifiers, etc. to perform joint compositional and numerical reasoning that may not be seen by the llm. this motivates us to design a kgqa framework that can operate in a zero-shot setting without additional data. as complex questions require joint compositional and numerical reasoning, this requires the decomposition of the question and the solution of them, providing transparency @xcite . therefore, in this paper, we propose ns-kgqa: a zero-shot neuro-symbolic approach based on neural kg embeddings that have demonstrated their ability to effectively model kg structure without the need of additional data. we decompose the natural language question into a link-prediction based symbolic question subgraph which is then resolved by our proposed symbolic resolver powered by dual kg embeddings. we illustrate the proposed ns-kgqa reasoning process in figure 1 with a complex real-world question where, former french region is a kg concept. the phrase not equal to 97000 and and are numerical operation phrases (nop) that will be identified and satisfied during answer retrieval. section 3.4 details the symbolic question subgraph generation process. subsequently, reasoning is conducted with a symbolic resolver (explained in section 3.6) in the following steps: 1. expand the kg concept former french region to extract entities such as alsace, champagne-ardenne and nord-pas-de-calais. ## ns-kgqa framework we introduce the details of ns-kgqa framework shown in figure 1 , that consists of three modules: (1) learn dual kg embeddings; (2) link-prediction based symbolic question subgraph generation; and (3) symbolic resolver. ## experiments our experiments answer the following research questions: 1. how does ns-kgqa compare against existing llm-based kgqa frameworks in zero-shot setting? ## results and analysis the results on each dataset are discussed below. qualitative analysis is provided in appendix a.1. ## conclusion we propose ns-kgqa, a novel zero-shot neurosymbolic approach for complex kgqa. to the best of our knowledge, it is the first one to fuse the neural kge approach with the symbolic approach without the need for any external data. the core of ns-kgqa is to preserve the query capability of the kg through the proposed link-prediction based reasoning (neural approximation). only the "symbolic" parts essential to answer the complex question are abstracted while leveraging dual kg embeddings for querying. our extensive experiments show that ns-kgqa outperforms llm-based zero-shot baselines by a significant margin, setting a new benchmark for various complex kgqa datasets. it discovers the weakness of current kgqa techniques in handling complex question types. ns-kgqa is independent of any logical form and, hence, applicable to any new kg and settings at cold-start while being interpretable.