发表机构
City University of Science and Information Technology (CUSIT); National University of Computer and Emerging Sciences (FAST-NUCES); University of Engineering and Technology (UET)（城市科学与信息技术大学（CUSIT）; 国立计算机与新兴科学大学（FAST-NUCES）; 工程技术大学（UET））
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
OntoKG-EQ构建基于溯源和能力问题的知识图谱，使新兴市场分析师的查询可复现、可审计，并验证其优于传统方法，提升信任与完整性。
AI 中文摘要
新兴股票市场的分析师们一直在回答相同的问题。基本面是否与市场反应相匹配？本币与收益率如何联动？哪些公司的表现优于行业和基准，哪些披露与异常交易同时发生？这些答案来自临时的电子表格，难以复现、审计或信任。我们提出了OntoKG-EQ，一个基于知识的系统，使此类查询可复现、可关联证据、时间明确、有效且可检查。它将一个有界、由能力问题驱动的核心本体与一个感知溯源的知识图谱相结合，其中每个类、属性、形状和度量都由五个冻结问题之一来证明其合理性。该系统将市场数据物化到图谱中，计算度量，根据声明性形状约束验证其结构，用图查询回答每个能力问题，推导类型化发现，并生成解释，将每个结果追溯到其观察、证据、来源和溯源。我们在来自三个新兴市场（巴基斯坦、马来西亚、印度尼西亚）的精选数据集上进行了评估。一旦每个市场的数据映射到通用模式，本体、形状、查询和规则将原样重用。一个关系数据库基线表明，图谱不改变任何分析。其价值在于治理、溯源和自解释结构。由于答案是从验证过的图谱中确定性生成的，因此它们与图谱的一致性由构造保证。作为参考，该系统衡量了八个开放语言模型转录相同证据的一致性（溯源覆盖率0.00至1.00）。一项包含17名参与者的便利样本研究发现，证据包显著提高了感知信任度和完整性。代码和数据已公开。
英文摘要
Analysts in emerging equity markets keep answering the same questions. Did fundamentals match the market's response? How does the local currency co-move with returns? Which firms outperform sector and benchmark, and which disclosures coincide with abnormal trading? These answers come from ad-hoc spreadsheets that are hard to reproduce, audit, or trust. We present OntoKG-EQ, a knowledge-based system that makes such queries reproducible, evidence-linked, temporally explicit, valid, and inspectable. It couples a bounded, competency-question-governed core ontology with a provenance-aware knowledge graph in which every class, property, shape, and metric is justified by one of five frozen questions. The system materialises market data into the graph, computes the metrics, validates its structure against declarative shape constraints, answers each competency question with a graph query, derives typed findings, and generates an explanation tracing each result to its observations, evidence, sources, and provenance. We evaluate on curated datasets from three emerging markets (Pakistan, Malaysia, Indonesia). Once each market's data is mapped into the common schema, the ontology, shapes, queries, and rules are reused unchanged. A relational-database baseline shows the graph changes no analytics. Its value is governance, provenance, and self-explaining structure. Because answers are rendered deterministically from the validated graph, their consistency with it is guaranteed by construction. Used as a reference, the system measures how consistently eight open language models transcribe the same evidence (provenance coverage 0.00 to 1.00). A study with a 17-participant convenience panel finds the evidence bundle significantly increased perceived trust and completeness. Code and data are openly released.
Comments36 pages, 2 figures
