@inproceedings{comas-2026-continuous,
title = "Continuous Financial Knowledge Graph Construction with Citation-Grounded Agentic Question Answering",
author = "Comas, Arnau Rodon",
editor = "Huang, Jimin and
Lee, Yongjae and
Ananiadou, Sophia and
Chen, Hsin-Hsi and
Takamura, Hiroya and
Chen, Chung-Chi",
booktitle = "Proceedings of the 11th Workshop on Financial Technology and Natural Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.finnlp-1.31/",
pages = "383--393",
ISBN = "979-8-89176-466-8",
abstract = "Financial analysis requires crossing evidence modalities that arrive in incompatible forms {---} structured market data, reported text (news and regulatory filings), collective expectations from prediction markets, and private portfolio state {---} which existing systems either handle in isolation or collapse into a single index, destroying the distinction between observed measurements, attributed claims, probabilistic beliefs, and user-specific exposure. We present a continuously running financial NLP system that maintains these modalities natively, linked by canonical entities and time, through continuous knowledge graph construction with per-edge provenance and temporal decay, and answers questions via agentic tool use under a citation contract that is enforced structurally: every citation must resolve to a passage retrieved in the same session, and free-text figures must agree with provider-attributed tool outputs. A learned relevance gate formalises the extraction cost/quality tradeoff, reducing LLM extraction calls while retaining the majority of yield-positive documents. Over 284 benchmark questions executed three times under majority vote, 251 produce non-failing responses (205 strong, 20 pass, 26 weak), 23 fail, and 10 additional questions are data-absent under the evaluation protocol. A structural evaluation framework of eight deterministic pre-judge gates reaches {\ensuremath{\kappa}} = 0.860 with a human annotator. In a sample evaluation, grounding entity descriptions in retrieved passages cuts fabrication from 1.83 to 0.17 unverifiable claims per description."
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<abstract>Financial analysis requires crossing evidence modalities that arrive in incompatible forms — structured market data, reported text (news and regulatory filings), collective expectations from prediction markets, and private portfolio state — which existing systems either handle in isolation or collapse into a single index, destroying the distinction between observed measurements, attributed claims, probabilistic beliefs, and user-specific exposure. We present a continuously running financial NLP system that maintains these modalities natively, linked by canonical entities and time, through continuous knowledge graph construction with per-edge provenance and temporal decay, and answers questions via agentic tool use under a citation contract that is enforced structurally: every citation must resolve to a passage retrieved in the same session, and free-text figures must agree with provider-attributed tool outputs. A learned relevance gate formalises the extraction cost/quality tradeoff, reducing LLM extraction calls while retaining the majority of yield-positive documents. Over 284 benchmark questions executed three times under majority vote, 251 produce non-failing responses (205 strong, 20 pass, 26 weak), 23 fail, and 10 additional questions are data-absent under the evaluation protocol. A structural evaluation framework of eight deterministic pre-judge gates reaches \ensuremathąppa = 0.860 with a human annotator. In a sample evaluation, grounding entity descriptions in retrieved passages cuts fabrication from 1.83 to 0.17 unverifiable claims per description.</abstract>
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%0 Conference Proceedings
%T Continuous Financial Knowledge Graph Construction with Citation-Grounded Agentic Question Answering
%A Comas, Arnau Rodon
%Y Huang, Jimin
%Y Lee, Yongjae
%Y Ananiadou, Sophia
%Y Chen, Hsin-Hsi
%Y Takamura, Hiroya
%Y Chen, Chung-Chi
%S Proceedings of the 11th Workshop on Financial Technology and Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-466-8
%F comas-2026-continuous
%X Financial analysis requires crossing evidence modalities that arrive in incompatible forms — structured market data, reported text (news and regulatory filings), collective expectations from prediction markets, and private portfolio state — which existing systems either handle in isolation or collapse into a single index, destroying the distinction between observed measurements, attributed claims, probabilistic beliefs, and user-specific exposure. We present a continuously running financial NLP system that maintains these modalities natively, linked by canonical entities and time, through continuous knowledge graph construction with per-edge provenance and temporal decay, and answers questions via agentic tool use under a citation contract that is enforced structurally: every citation must resolve to a passage retrieved in the same session, and free-text figures must agree with provider-attributed tool outputs. A learned relevance gate formalises the extraction cost/quality tradeoff, reducing LLM extraction calls while retaining the majority of yield-positive documents. Over 284 benchmark questions executed three times under majority vote, 251 produce non-failing responses (205 strong, 20 pass, 26 weak), 23 fail, and 10 additional questions are data-absent under the evaluation protocol. A structural evaluation framework of eight deterministic pre-judge gates reaches \ensuremathąppa = 0.860 with a human annotator. In a sample evaluation, grounding entity descriptions in retrieved passages cuts fabrication from 1.83 to 0.17 unverifiable claims per description.
%U https://aclanthology.org/2026.finnlp-1.31/
%P 383-393
Markdown (Informal)
[Continuous Financial Knowledge Graph Construction with Citation-Grounded Agentic Question Answering](https://aclanthology.org/2026.finnlp-1.31/) (Comas, FinNLP 2026)
ACL