Question answering over knowledge graphs (KGQA) has evolved from simple
single-fact questions to complex questions that require graph traversal and
aggregation. We propose a novel approach for complex KGQA that uses
unsupervised message passing, which propagates confidence scores obtained by
parsing an input question and matching terms in the knowledge graph to a set of
possible answers. First, we identify entity, relationship, and class names
mentioned in a natural language question, and map these to their counterparts
in the graph. Then, the confidence scores of these mappings propagate through
the graph structure to locate the answer entities. Finally, these are
aggregated depending on the identified question type. This approach can be
efficiently implemented as a series of sparse matrix multiplications mimicking
joins over small local subgraphs. Our evaluation results show that the proposed
approach outperforms the state-of-the-art on the LC-QuAD benchmark. Moreover,
we show that the performance of the approach depends only on the quality of the
question interpretation results, i.e., given a correct relevance score
distribution, our approach always produces a correct answer ranking. Our error
analysis reveals correct answers missing from the benchmark dataset and
inconsistencies in the DBpedia knowledge graph. Finally, we provide a
comprehensive evaluation of the proposed approach accompanied with an ablation
study and an error analysis, which showcase the pitfalls for each of the
question answering components in more detail.

本文提出了一种基于无监督消息传递的复杂知识图谱问答方法，通过识别问题类型，并在图结构中传递置信度分数来进行答案实体的定位和聚合，其在 LC-QuAD 基准测试中表现优于现有方法，具有较高的实现效率和通用性。