A Semantic Web of Know-How: Linked Data for Community-Centric Tasks

TL;DR

This paper introduces a new semantic framework for representing community-generated procedural knowledge on the Semantic Web, enabling automatic discovery and integration of unstructured community know-how.

Contribution

It presents a novel semantic representation framework tailored for community know-how and demonstrates an implementation with automatic procedural knowledge acquisition.

Findings

Feasibility of representing community know-how semantically

Successful automatic acquisition of procedural knowledge

Enhanced potential for application integration and discovery

Abstract

This paper proposes a novel framework for representing community know-how on the Semantic Web. Procedural knowledge generated by web communities typically takes the form of natural language instructions or videos and is largely unstructured. The absence of semantic structure impedes the deployment of many useful applications, in particular the ability to discover and integrate know-how automatically. We discuss the characteristics of community know-how and argue that existing knowledge representation frameworks fail to represent it adequately. We present a novel framework for representing the semantic structure of community know-how and demonstrate the feasibility of our approach by providing a concrete implementation which includes a method for automatically acquiring procedural knowledge for real-world tasks.