arXiv：cs.AI· Janik Lesage, Khadim L\^o, and Jean-S\'ebastien Dessureault·· 4 小时前
EneMonIE-Backend：模块化能源信息抽取与循证问答架构
From Conceptual EneMonIE to an Executable Backend: A Modular Architecture for Energy Information Extractionand Grounded Question Answering
AI 导读
EneMonIE-Backend 将原 EneMonIE 概念流水线重构为模块化、可执行且与应用无关的后端，分离数据源、采集与规范化、语义处理、知识组织、持久化五层关注点。
正文
Abstract:The original EneMonIE proposal describes a Web-based semantic system for continuously monitoring energy trends through targeted information extraction from heterogeneous online media. This article presents EneMonIE-Backend, an implementation-derived redesign that converts that conceptual pipeline into a modular, executable, and application-independent backend. The architecture separates five concerns: data sources, acquisition and normalization, semantic processing, knowledge organization, and persistent storage. It operationalizes configurable connectors for RSS feeds, URL collections, arXiv, social-media JSON files, and open-data CSV files; it also introduces question-driven Web search and scraping. Relevant documents pass through energy categorization, named entity recognition, temporal and event extraction, source sensitive sentiment analysis, and extractive summarization. The resulting semantic representations support entity co-occurrence graphs, cross-document interlinking, cluster-level information fusion, and relational persistence in SQLite. Three grounded question-answering paths are exposed at the backend level: live Web evidence, the stored corpus, and selected configured resources. Answer generation uses a language model when configured and otherwise falls back to deterministic extractive output. Unlike the original proposal, the presented architecture deliberately excludes the user-interface and server-side application layers, allowing the semantic core to be embedded in different applications. A deterministic smoke validation confirms relevance filtering, persistence, clustering, evidence retrieval, and document deletion. The work provides a reproducible architectural basis for future evaluation on larger multilingual energy corpora.
| Subjects: | Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Information Retrieval (cs.IR) |
| ACM classes: | I.2.7; H.3.1; H.3.3; H.3.4; D.2.11 |
| Cite as: | arXiv:2610.17243 [cs.CL] |
| (or arXiv:2610.17243v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2610.17243 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Janik Lesage [view email]
[v1]
Mon, 12 Oct 2026 18:09:35 UTC (645 KB)
来源：arXiv：cs.AI · arxiv.org