This paper presents our solution for the Micro-Expression Visual Question Answering (ME-VQA) task in the 2025 Facial Micro-Expression Grand Challenge (MEGC). To address the limitations of traditional micro-expression recognition (MER) methods in dynamic modeling, semantic interpretation, and natural language interaction, we propose Emotion-Qwen-VL, a fully fine-tuned multimodal large language model tailored for micro-expression understanding. Specifically, we construct a structured, instruction-based QA dataset that reformulates emotion categories and action unit (AU) annotations into natural language QA pairs, covering classification, AU detection, and causal reasoning. We then adopt a full-parameter fine-tuning strategy to guide Qwen2.5-VL in learning fine-grained temporal facial dynamics and their emotional semantics. Experimental results on the MEGC 2025 test set demonstrate that Emotion-Qwen-VL outperforms strong baselines such as Qwen2.5-VL and QVQ across multiple dimensions, including coarse-grained macro-expression classification, fine-grained micro-expression classification, and language generation. Our results highlight the effectiveness, interpretability, and adaptation potential of large models in micro-expression understanding. The code is available at: https://github.com/2308623956/MEGC2025.

Keyword searches based on the keywords-to-SPARQL translation is attracting more attention because of a growing number of excellent SPARQL search engines. Current approaches for keyword search based on the keywords-to-SPARQL translation suffer from returning incomplete answers or wrong answers due to a lack of underlying schema information. To overcome these difficulties, in this article, we propose a new keyword search paradigm by translating keyword queries into SPARQL queries for exploring RDF data. An inter-entity relationship summary with complete schema information is distilled from the RDF data graph for composing SPARQL queries. To avoid potentially wasteful summary graph expansion, we develop a new search prioritization scheme by combining the degree of a vertex with the distance from the original keyword element. Starting from the ordered priority list that is built in advance, we apply the forward path index to faster find the top-k subgraphs, which are relevant to the conjunction of the entering keywords. The experimental results show that our approach is efficient and scalable.