Towards a Flexible System Architecture for Automated Knowledge Base Construction Frameworks

TL;DR

This paper proposes a scalable and flexible architecture for automated knowledge base construction, addressing limitations of existing frameworks to facilitate more accurate, up-to-date, and adaptable knowledge bases.

Contribution

It introduces an extensible architecture for AKBC frameworks and demonstrates its application to improve existing systems in the domain.

Findings

Extended a specific AKBC framework to overcome design limitations

Showed the architecture's potential for computational knowledge base tasks

Provided insights for future development of AKBC frameworks

Abstract

Although knowledge bases play an important role in many domains (including in archives, where they are sometimes used for entity extraction and semantic annotation tasks), it is challenging to build knowledge bases by hand. This is owing to a number of factors: Knowledge bases must be accurate, up-to-date, comprehensive, and as flexible and as efficient as possible. These requirements mean a large undertaking, in the form of extensive work by subject matter experts (such as scientists, programmers, archivists, and other information professionals). Even when successfully engineered, manually built knowledge bases are typically one-off, use-case-specific, non-standardized, hard-to-maintain solutions. We present a scalable, flexible, and extensible architecture for knowledge base construction frameworks. As a work in progress, we extend a specific framework to address some of its design limitations. The contributions presented in this short paper can shed a light on the suitability of using AKBC frameworks for computational use cases in this domain and provide future directions for building improved AKBC frameworks.