01 · Structured Entity and Relation Extraction from Sequential Multimodal Audio-Video Content Using Large Language Models

基于大规模语言模型的序列化多模态音视频内容的结构化实体与关系抽取

Research record	Details
Authors	Timothy Dillan, Sani Muhamad Isa, Abba Suganda Girsang, Derwin Suhartono
Published	2026-10-02
Sources	openalex
Focus	multimodal entity and relation extraction, LLM-guided information extraction, cross-chunk entity resolution, schema-guided verification 多模态实体与关系抽取、基于大规模语言模型的信息抽取、跨片段实体解析、基于模式的验证

English

Relevant methods (temporal decomposition, schema verification, cross-chunk linking) could inform the thesis’s triple-extraction and entity-resolution components, but the paper is not central to agentic RAG, persistent graph memory, or multi-hop retrieval evaluation.

中文

其方法（时间分解、基于模式的验证、跨片段链接）可为论文的三元组抽取与实体解析提供参考，但该文并非关于智能体驱动的RAG、持久化图记忆或多跳检索评估的核心文献。

Research synopsis

English

The paper addresses structured information extraction from sequential multimodal audio-video content where entities and relations are distributed across synchronized text, images, audio, and video. It proposes an LLM-guided framework that combines temporal decomposition, context injection, schema-guided verification, and cross-chunk entity resolution to extract entities and relations without supervised fine-tuning. Evaluation on 200 annotated multimodal documents reports an overall entity extraction F1 of 82.4% with modality-specific F1s and shows temporal decomposition improves cross-chunk linking by 9.7% while schema-guided verification raises overall F1 by 5.5%. The authors report an estimated processing cost of $0.64 per item for their deployment.

中文

本文研究如何从序列化的多模态音视频内容中进行结构化信息抽取，针对实体与关系分布在文本、图像、音频与视频之间的情况。文章提出一个由大规模语言模型（LLM）驱动的框架，结合时间分解、上下文注入、基于模式的验证和跨片段实体解析，以在无需监督微调的前提下抽取实体与关系。在200个标注的多模态文档上评估，整体实体抽取F1为82.4%，给出按模态的F1值；时间分解使跨片段实体链接准确率提高9.7%，基于模式的验证使整体F1提高5.5%。作者估算每条处理成本约为0.64美元。

Thesis relevance

English

Overlap: the paper presents LLM-based triple/entity extraction and cross-chunk entity resolution methods that are directly relevant to building a Knowledge Graph (KG) from multimodal inputs for a persistent graph memory. Differences and limitations: it focuses on batch extraction from audiovisual documents rather than runtime, agentic RAG workflows, persistent graph memory construction from agent interactions, or multi-hop retrieval and bridge-entity tasks emphasized in the thesis. Complementarity: schema-guided verification and temporal decomposition techniques could be adapted to the thesis’s triple-extraction pipeline and to improve entity-linking quality when assembling incremental graph memory.

中文

重合点：该论文提出的基于LLM的三元组/实体抽取和跨片段实体解析方法，与从多模态输入构建用于持久化图记忆的知识图谱（KG）直接相关。差异与局限：论文侧重于对视听文档的批量离线抽取，而非论文关注的运行时智能体驱动的RAG流程、由智能体交互逐步构建的持久化图记忆，或论文强调的多跳检索与桥接实体任务。互补性：基于模式的验证与时间分解技术可移植到本论文的三元组抽取流水线，从而在增量组装图记忆时提升实体关联质量。

Writing and related work

English

Position this paper in related work as a multimodal triple-extraction study that emphasizes practical prompt-based LLM use and lightweight verification. Note the limited dataset scale and the paper’s focus on offline documents when comparing to agentic, online graph-memory work.

中文

将此论文归入多模态三元组抽取相关工作，强调其实用的基于提示的LLM用法与轻量级验证。与智能体驱动的在线图记忆工作比较时，应指出其数据集规模有限且侧重离线文档处理。

Method and evaluation

English

Consider adopting their temporal decomposition and context injection patterns to segment long interaction traces before triple extraction for the thesis’s KG construction. Reuse schema-guided verification as a lightweight filter to reduce unsupported triples prior to insertion into the graph. Compare the paper’s cross-chunk entity-linking gains against the thesis’s embedding-similarity vs LLM-judge entity-resolution experiments and measure impact on multi-hop path continuity and latency.

中文

可考虑将他们的时间分解与上下文注入模式用于在对话或检索轨迹中分段后再进行三元组抽取，以利于论文中的KG构建。可把基于模式的验证作为轻量筛选器，在将三元组写入图之前减少不受支持的三元组。将论文报告的跨片段实体链接改进与论文中嵌入相似度和LLM-裁判的实体解析对照，评估其对多跳路径连贯性和延迟的影响。

Future directions

English

Integrate the multimodal extraction pipeline into an agentic, online graph-memory system and measure effects on multi-hop retrieval, bridge-entity identification, and end-to-end latency. Expand evaluation to larger, heterogeneous datasets and test online incremental extraction under the latency constraints of local LLMs.

中文

将该多模态抽取流水线整合到一个智能体驱动的在线图记忆系统中，度量其对多跳检索、桥接实体识别和端到端延迟的影响。将评估扩展到更大且异质的数据集，并在本地LLM延迟约束下测试在线增量抽取。

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02 · ddkg.skill: A Compositional Agent Skill for Translating Biomedical and Bioinformatics Questions into Cypher for the Data Distillery Knowledge Graph

ddkg.skill：一种用于将生物医学与生物信息学问题翻译为 Data Distillery 知识图谱（DDKG） Cypher 查询的可组合智能体技能

Research record	Details
Authors	Deanne M. Taylor, Aditya M. Lahiri, Taha Mohseni Ahooyi, Benjamin Stear, Yuanchao Zhang, Asif Chinwalla, Shiping Zhang, Christopher Nemarich, J. Alan Simmons, Jonathan C. Silverstein
Published	2026-10-02
Sources	openalex
Focus	biomedical knowledge graph querying, compositional agent skill, Cypher generation, entity resolution, reproducible KG interfaces 生物医学 知识图谱（KG）查询, 可组合 智能体技能, Cypher 生成, 实体解析, 可复现的 KG 接口

English

The paper contains practical, reproducible techniques for entity resolution, query validation, and versioned KG interfaces that could materially inform the thesis system’s design and evaluation; its engineering choices and failure analysis merit careful study.

中文

该文提供了关于实体解析、查询验证和版本化 KG 接口的可复现工程技术，可能对论文系统的设计与评估产生实质性启发；其工程选择与失败分析值得深入研读。

Research synopsis

English

The paper presents ddkg.skill, a compositional Agent Skill designed to help large language models generate correct Cypher queries against the Data Distillery Knowledge Graph (DDKG). The skill bundles a controller, DDKG-specific reference tables, validated query patterns, a routing table, and a script that checks internal links; builds are identified by checksum so users can reference an exact skill version. An orthogonal nine-test evaluation on an earlier build (38 files, 227 routing entries) exercised live queries: five of seven tests that targeted sources absent from examples returned correct executed results, while the evaluation also revealed a failed cross-source comparison and errors in the skill’s references. The design aims to guide AI through entity resolution, query construction, inspection, and validation while keeping the DDKG as the authoritative result source.

中文

本文介绍了 ddkg.skill —— 一个可组合的 智能体技能，用于帮助大型语言模型针对 Data Distillery 知识图谱（DDKG）生成正确的 Cypher 查询。该技能打包了一个控制器、DDKG 专用的参考表与结构化材料、经过验证的查询模式、路由表以及用于检查内部链接的脚本；每个构建通过校验和标识以便精确复现。在对早期构建（38 个文件，227 条路由条目）进行的九项独立测试中：针对示例中缺失来源的七项测试中有五项返回了正确的执行结果，但评估也暴露出一次跨源比较失败和技能参考资料中的错误。该设计旨在在实体解析、查询构建、图检查与验证过程中为 AI 提供指导，同时将 DDKG 保持为权威的结果来源。

Thesis relevance

English

Overlap: ddkg.skill directly addresses entity resolution, validated query construction, and keeping an external KG as the authoritative source—topics relevant to graph memory verifiability and query grounding. Differences: ddkg.skill is a domain-targeted, pre-authored skill bundle for a specific biomedical KG rather than an incrementally assembled persistent graph memory built from the agent’s own retrieval and inference. Limitations: the paper reports a small, domain-specific evaluation (nine tests on one KG build) and does not compare structured graph memory against vector RAG or agentic RAG variants. Complementarity: its compositional controller, routing-table patterns, and checksumed builds offer practical techniques for verifiable query generation that could be incorporated into a graph-memory-enabled agentic search system.

中文

重合点：ddkg.skill 直接处理实体解析、经过验证的查询构建，以及将外部知识图谱（KG）作为权威结果来源——这些主题与图记忆的可验证性和查询落地相关。差异：ddkg.skill 是针对特定生物医学 KG 的领域化预置技能包，而非由智能体自身检索与推理活动增量组装的持久图记忆。局限：文章仅对单一 KG 构建进行了小规模、领域相关的评估（九项测试），且未将结构化图记忆与向量 RAG 或其他智能体 RAG 变体进行比较。互补性：其可组合控制器、路由表模式和校验和构建方法是可用于实现可验证查询生成的实用技术，可被纳入具有图记忆的智能体搜索系统。

Writing and related work

English

Position this paper as an engineering-focused, reproducibility-oriented example of giving an AI practical, versioned knowledge of a complex KG. Emphasize its value for query validation and for exposing failure modes through explicit reference materials and build checksums.

中文

将此文定位为面向工程与可复现性的案例，展示如何为 AI 提供复杂 KG 的可版本化实践知识。强调其在查询验证和通过显式参考材料及构建校验和揭示失败模式方面的价值。

Method and evaluation

English

Consider reusing ddkg.skill patterns: pack a controller, domain-specific reference tables, validated query templates, and an internal link checker with versioned builds to increase reproducibility and verifiability of Cypher (or graph) queries. For evaluation, run targeted tests that probe sources absent from worked examples and record executed query correctness and failure modes; compare these outcomes against the thesis system’s incremental graph memory to measure gains in multi-hop retrieval, verifiability, and error types.

中文

考虑借鉴 ddkg.skill 的模式：将控制器、领域专用参考表、经过验证的查询模板和内部链接检查脚本打包并对构建进行版本化，以提高 Cypher（或图）查询的可复现性与可验证性。评估上，可设计针对示例中缺失来源的测试，记录已执行查询的正确性与失败模式；并将这些结果与论文提出的增量图记忆系统进行对比，以衡量在多跳检索、可验证性和错误类型方面的改进。

Future directions

English

Generalize the compositional-skill approach beyond a single KG and automate extraction of validated query patterns from interaction logs. Explore integrating a versioned skill-controller with an agent that incrementally assembles graph memory to combine skill-driven query correctness with long-term structured memory.

中文

将该可组合技能方法推广到多种 KG，并自动从交互日志中提取经过验证的查询模式。探索将版本化技能控制器与增量构建图记忆的智能体集成，以将技能驱动的查询正确性与长期结构化记忆相结合。

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