SAKA: An Intelligent Platform for Semi-automated Knowledge Graph Construction and Application

TL;DR

SAKA is an innovative platform that simplifies semi-automated knowledge graph construction from structured and audio data, enabling easier management and application of KGs for users without extensive expertise.

Contribution

The paper introduces SAKA, a user-friendly platform that integrates semi-automatic KG construction, audio data processing, and a semantic KBQA system, addressing manual effort and audio data challenges.

Findings

Feasibility demonstrated for semi-automatic KG construction on SAKA.

Effective extraction of knowledge from audio data using AGIE.

Enhanced user interaction for KG management and updates.

Abstract

Knowledge graph (KG) technology is extensively utilized in many areas, and many companies offer applications based on KG. Nonetheless, most KG platforms necessitate expertise and tremendous time and effort from users to construct KG records manually, which poses great difficulties for ordinary people. Additionally, audio data is abundant and holds valuable information, but it is challenging to transform it into a KG. What's more, the platforms usually do not leverage the full potential of the KGs constructed by users. In this paper, we propose an intelligent and user-friendly platform for Semi-automated KG Construction and Application (SAKA) to address the aforementioned problems. Primarily, users can semi-automatically construct KGs from structured data of numerous areas by interacting with the platform, based on which multi-versions of KG can be stored, viewed, managed, and updated. Moreover, we propose an Audio-based KG Information Extraction (AGIE) method to establish KGs from audio data. Lastly, the platform creates a semantic parsing-based knowledge base question answering (KBQA) system based on the user-created KGs. We prove the feasibility of the semi-automatic KG construction method on the SAKA platform.