Slot Filling for Extracting Reskilling and Upskilling Options from the Web

TL;DR

This paper presents a system that automatically extracts and integrates reskilling and upskilling options from various web sources into a comprehensive knowledge graph, aiding users in finding suitable education programs.

Contribution

It introduces a novel knowledge extraction system combining context, entity recognition, and linking to build a continuous education knowledge graph from diverse sources.

Findings

Collected data from 488 providers

Created a German gold standard with 169 documents

System achieves promising extraction and linking performance

Abstract

Disturbances in the job market such as advances in science and technology, crisis and increased competition have triggered a surge in reskilling and upskilling programs. Information on suitable continuing education options is distributed across many sites, rendering the search, comparison and selection of useful programs a cumbersome task. This paper, therefore, introduces a knowledge extraction system that integrates reskilling and upskilling options into a single knowledge graph. The system collects educational programs from 488 different providers and uses context extraction for identifying and contextualizing relevant content. Afterwards, entity recognition and entity linking methods draw upon a domain ontology to locate relevant entities such as skills, occupations and topics. Finally, slot filling integrates entities based on their context into the corresponding slots of the continuous education knowledge graph. We also introduce a German gold standard that comprises 169 documents and over 3800 annotations for benchmarking the necessary content extraction, entity linking, entity recognition and slot filling tasks, and provide an overview of the system's performance.