microsoft/MS-KeBAB

microsoft/MS-KeBAB

Description: Repository for Microsoft KnowledgE Base construction and Access Benchmark (MS KeBAB)

Language: Python

License: MIT

Stars: 5

Forks: 0

Open issues: 10

Created: 2024-04-11T19:35:55Z

Pushed: 2026-06-20T04:35:59Z

Default branch: main

Fork: no

Archived: no

README:

Knowledge Base Construction and Access Benchmark (KeBAB)

This repository contains the backend implementation for the Knowledge Base construction and Access Benchmark (KeBAB🍢).

The recommended way to work with the code is to clone the repository and install it via uv:

uv sync --locked

Overview

This repository enables benchmarking of:

• Different approaches for knowledge access (e.g., text-retrieval-augmentation vs. KB-augmentation),
• Different approaches for surfacing knowledge from KBs, and
• Different approaches for KB construction,
• Different approaches for individual steps in KB construction (e.g., for entity extraction and clustering)

Glossary

• Entity: A set of properties with provenance information.
• Entity fragment: A partial entity extracted from a single piece of text.
• Knowledge base (KB): A set of entities.

Tasks

The benchmark will support several related tasks depicted by red rectangles in the figure below. For each task, the benchmark may provide multiple benchmarking datasets and corresponding evaluation metrics, each of which we refer to as a task instance. For example, the KB construction task may have one instance that benchmarks based on a corpus of emails and another based on a corpus of scholarly articles. We define consistent interfaces for each task that all corresponding task instances will comply with. This is to ensure that any given model for a task can be easily run on all corresponding task instances without requiring instance-specific code changes. We describe the supported tasks and task instances below.


Knowledge access task

Input: A text corpus + a user input. Expected output: A generated response. Task instance(s): Currently, one task instance comprising of document completion given initial few tokens of the document.

KB surfacing task

Input instance: A KB + a user input. Expected output: A generated response. Task instance(s): Currently, one task instance comprising of document completion given initial few tokens of the document.

KB construction task

Input: A text corpus. Expected output: A KB. Task instance(s): We have not implemented any instances for this task yet.

Entity extraction task

Input instance: Text. Expected output instance: A set of entity fragments. Task instance(s): Currently, one task instance based on ReDocRed.

Entity linking task

Input instance: A pair of entity fragments. Expected output instance: Boolean prediction on whether they correspond to the same entity or not. Task instance(s): Currently, one task instance based on REBEL.

Clustering task

Input: A set of entity fragments. Expected output: A KB. Task instance(s): Currently, one task instance based on REBEL.

Repository organization

The repository is organized to ensure clarity and ease of navigation. Below is a brief overview of the main directories and their purposes:

• build/: Contains configuration files for automated builds.
• docs/: Documentation resources, including experiment results.
• kebab/: Contains the core implementation of the project, including all modules, utilities, and primary logic.
• configs/: Configuration files for the benchmark.
• contracts/: Core interfaces and abstractions that define the project's key contracts and APIs for document, entity, task, etc.
• tasks/: Task-specific implementations for various task types, such as extraction, linking, and more.
• utils/: Utility functions for common operations across the project, including I/O handling, logging, and data processing.
• mskebab.py: Contains the entry point class Benchmark.
• scripts/: Includes scripts for specific tasks such as data downloading, processing, or running experiments.
• tests/: Contains tests to ensure the robustness and reliability of the codebase.

Contributing

This project welcomes contributions and suggestions. Most contributions require you to agree to a Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us the rights to use your contribution. For details, visit https://cla.opensource.microsoft.com.

When you submit a pull request, a CLA bot will automatically determine whether you need to provide a CLA and decorate the PR appropriately (e.g., status check, comment). Simply follow the instructions provided by the bot. You will only need to do this once across all repos using our CLA.

This project has adopted the Microsoft Open Source Code of Conduct. For more information see the Code of Conduct FAQ or contact [opencode@microsoft.com](mailto:opencode@microsoft.com) with any additional questions or comments.

Trademarks

This project may contain trademarks or logos for projects, products, or services. Authorized use of Microsoft trademarks or logos is subject to and must follow Microsoft's Trademark & Brand Guidelines. Use of Microsoft trademarks or logos in modified versions of this project must not cause confusion or imply Microsoft sponsorship. Any use of third-party trademarks or logos are subject to those third-party's policies.