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ARXIV:2604.04359 · RAG SYSTEMS · SUBMITTED 07 APR · 20:12 UTC · FRESHNESS UNKNOWN
ARXIV:2604.04359RAG SYSTEMSSUBMITTED 07 APR · 20:12 UTCFRESHNESS UNKNOWNTianyi Zhang · Andreas Marfurt · arXiv
GroundedKG-RAG: A RAG system that explicitly extracts and grounds knowledge graphs from source documents for efficient and factually accurate long-document question answering.
Opportunity summary
Pain GroundedKG-RAG: A RAG system that explicitly extracts and grounds knowledge graphs from source documents for efficient and factually accurate long-document question answering.
Evidence 0 refs | 0 sources | 0% coverage
Blocker Evidence unverified
GroundedKG-RAG: A RAG system that explicitly extracts and grounds knowledge graphs from source documents for efficient and factually accurate long-document question answering. In this work, we focus on RAG systems for long-document question answering.
Retrieval-augmented generation (RAG) systems have been widely adopted in contemporary large language models (LLMs) due to their ability to improve generation quality while reducing the required input context length. In this work, we focus…
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Retrieval-augmented generation (RAG) systems have been widely adopted in contemporary large language models (LLMs) due to their ability to improve generation quality while reducing…
RAG Systems moved forward this cycle; last verified April 2026. Public score 7.0/10. Production flags indicate code availability.
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mobile layout uses overflow-hidden min-w-0 break-wordsOpportunity summary
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GroundedKG-RAG: A RAG system that explicitly extracts and grounds knowledge graphs from source documents for efficient and factually accurate long-document question answering.
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Paper Pack
10.48550/arXiv.2604.04359GroundedKG-RAG: A RAG system that explicitly extracts and grounds knowledge graphs from source documents for efficient and factually accurate long-document question answering.
Abstract
Retrieval-augmented generation (RAG) systems have been widely adopted in contemporary large language models (LLMs) due to their ability to improve generation quality while reducing the required input context length. In this work, we focus on RAG systems for long-document question answering. Current approaches suffer from a heavy reliance on LLM descriptions resulting in high resource consumption and latency, repetitive content across hierarchical levels, and hallucinations due to no or limited grounding in the source text. To improve both efficiency and factual accuracy through grounding, we propose GroundedKG-RAG, a RAG system in which the knowledge graph is explicitly extracted from and grounded in the source document. Specifically, we define nodes in GroundedKG as entities and actions, and edges as temporal or semantic relations, with each node and edge grounded in the original sentences. We construct GroundedKG from semantic role labeling (SRL) and abstract meaning representation (AMR) parses and then embed it for retrieval. During querying, we apply the same transformation to the query and retrieve the most relevant sentences from the grounded source text for question answering. We evaluate GroundedKG-RAG on examples from the NarrativeQA dataset and find that it performs on par with a state-of-the art proprietary long-context model at smaller cost and outperforms a competitive baseline. Additionally, our GroundedKG is interpretable and readable by humans, facilitating auditing of results and error analysis.
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What was readable
Derived fallback: Estimated from adjacent evidence; not verified from source.
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Dimensions overall score 7.0
PROBLEM
GroundedKG-RAG: A RAG system that explicitly extracts and grounds knowledge graphs from source documents for efficient and factually accurate long-document question answering. In this work, we focus on RAG systems for long-document question answering.
METHOD
Retrieval-augmented generation (RAG) systems have been widely adopted in contemporary large language models (LLMs) due to their ability to improve generation quality while reducing the required input context length. In this work, we focus on RAG systems for long-document questio...
RESULT
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Retrieval-augmented generation (RAG) systems have been widely adopted in contemporary large language models (LLMs) due to their ability to improve generation quality while reducing the required input cont...
WHY NOW
RAG Systems moved forward this cycle; last verified April 2026. Public score 7.0/10. Production flags indicate code availability.
Abstract-backed public claims while anchored extraction refreshes.
GroundedKG-RAG: A RAG system that explicitly extracts and grounds knowledge graphs from source documents for efficient and factually accurate long-document question answering. In this work, we focus on RAG systems for long-document question answering.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Retrieval-augmented generation (RAG) systems have been widely adopted in contemporary large language models (LLMs) due to their ability to improve generation quality while reducing the required input context length. In this work, we focus on RAG systems for long-document question answering.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Retrieval-augmented generation (RAG) systems have been widely adopted in contemporary large language models (LLMs) due to their ability to improve generation quality while reducing the required input context length. Code availability is flagged in the production record; the public repository link still needs proof alignment.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
RAG Systems moved forward this cycle; last verified April 2026. Public score 7.0/10. Production flags indicate code availability.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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Concepts
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GroundedKG-RAG: A RAG system that explicitly extracts and grounds knowledge graphs from source documents for efficient and factually accurate long-document question answering.
Segment
RAG Systems
Adoption evidence
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Commercial read
7.0/10 public viability
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CITED BY
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status
missing
reason
passport_row_missing
proof status
unverified
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No verified cost estimate
confidence low
next verification path
Build brief missing until Build Passport data exists.
Source missing: Build Passport payload.
Experiment plan missing until prototype path is available.
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Validation checklist missing until required assets, cost, and regulatory flags are verified.
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Evidence coverage
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Build readiness
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passport absent
unknown
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Artifact maturity
GitHub and Hugging Face maturity payloads
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unknown
Open source artifacts or mark the gap as missing. verified:false
Technical feasibility
partial
Current read
Runnable path is not fully verified.
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Gaps
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Run minimal reproduction from the Build Passport prototype path.
Market urgency
missing
Current read
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Refresh defensibility bars with source receipts.
Integration burden
missing
Current read
No public implementation surface observed.
Evidence
No GitHub or Hugging Face payload attached.
Gaps
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Write integration checklist from prototype path and target workflow.
Capital intensity
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Gaps
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Classify regulatory flags before commercialization planning.
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Paper authors are not treated as operators without consent.
People
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Prototype owner missing.
Build Passport does not name an implementer.
People
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People
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Regulatory need unclassified.
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ARTIFACTS
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DEFENSIBILITY
Defensibility and confidence evidence pending.
WATCHTOWER
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FORESIGHT
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OPPORTUNITYKERNEL CHANGES SINCE LAST VIEW
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BUZZ
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