kapipe 0.1.3

A modular pipeline for knowledge acquisition

[!NOTE] This repository is currently under active development. Major updates are planned soon, including changes to APIs, component interfaces, model loading, and datasets. The current implementation should be considered unstable until the update is complete.

KAPipe

KAPipe is a modular framework for building Knowledge Acquisition Systems from unstructured data.

KAPipe decomposes knowledge acquisition into four main stages:

1. Extraction: extracting knowledge units from unstructured data.
2. Organization: organizing extracted knowledge units into structured representations such as knowledge graph.
3. Retrieval: retrieving relevant knowledge for a given query or task.
4. Utilization: using retrieved structured knowledge for downstream tasks such as question answering.

KAPipe is used in the following papers:

• Nishida et al., TACL 2026, Dissecting GraphRAG: A Modular Analysis of Knowledge Structuring for Factoid Question Answering.
• Oumaima and Nishida et al., BioNLP 2024, Mention-Agnostic Information Extraction for Ontological Annotation of Biomedical Articles.

Installation

python -m pip install -U kapipe

For local development:

git clone https://github.com/norikinishida/kapipe.git
cd kapipe
python -m pip install -e .

Some pretrained models and configuration files are distributed separately.

mkdir -p ~/.kapipe
mv release.YYYYMMDD.tar.gz ~/.kapipe
cd ~/.kapipe
tar -zxvf release.YYYYMMDD.tar.gz

Release files are available here:

KAPipe Release Files

Components

In KAPipe, a component is a modular processing unit that implements a specific approach within one of the four stages: extraction, organization, retrieval, or utilization.

The following table summarizes the components currently supported by KAPipe.

Stage	Component	Module	Docs	Example
Extraction	Named Entity Recognition	kapipe.ner	Docs	Example
Extraction	Entity Disambiguation (Retrieval)	kapipe.ed_retrieval	Docs	Example
Extraction	Entity Disambiguation (Reranking)	kapipe.ed_reranking	Docs	Example
Extraction	Document-level Relation Extraction	kapipe.docre	Docs	Example
Organization	Entity Graph Construction	kapipe.entity_graph_construction	Docs	Example
Organization	Community Clustering	kapipe.community_clustering	Docs	Example
Organization	Report Generation	kapipe.report_generation	Docs	Example
Organization	Chunking	kapipe.chunking	Docs	Example
Retrieval	Passage Retrieval	kapipe.passage_retrieval	Docs	Example
Utilization	Question Answering	kapipe.qa	Docs	Example

Quickstart

TBA.

Citation / Publication

If KAPipe is helpful for your work, please consider citing the following paper:

Dissecting GraphRAG: A Modular Analysis of Knowledge Structuring for Factoid Question Answering. Noriki Nishida, Rumana Ferdous Munne, Shanshan Liu, Narumi Tokunaga, Yuki Yamagata, Fei Cheng, Kouji Kozaki, and Yuji Matsumoto. Transactions of the Association for Computational Linguistics (TACL), vol. 14, pp. 627-655. 2026. (Presented at ACL 2026)

@article{nishida-etal-2026-dissecting,
    title = "Dissecting {G}raph{RAG}: A Modular Analysis of Knowledge Structuring for Factoid Question Answering",
    author = "Nishida, Noriki  and
      Munne, Rumana Ferdous  and
      Liu, Shanshan  and
      Tokunaga, Narumi  and
      Yamagata, Yuki  and
      Cheng, Fei  and
      Kozaki, Kouji  and
      Matsumoto, Yuji",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "14",
    year = "2026",
    address = "Cambridge, MA",
    publisher = "MIT Press",
    url = "https://aclanthology.org/2026.tacl-1.29/",
    doi = "10.1162/tacl.a.615",
    pages = "627--655"
}