Refined Commonsense Knowledge from Large-Scale Web Contents

TL;DR

This paper introduces ASCENT++, a novel method for automatically constructing a large-scale, refined commonsense knowledge base from web content, improving expressiveness, precision, and recall over previous approaches.

Contribution

ASCENT++ advances prior work by capturing composite concepts, semantic facets, and integrating open information extraction with ranking, resulting in a more expressive and accurate commonsense KB.

Findings

Superior quality of the ASCENT++ KB demonstrated through human judgments.

Enhanced performance in QA-support tasks using the new knowledge base.

High coverage achieved by leveraging large-scale web crawl C4.

Abstract

Commonsense knowledge (CSK) about concepts and their properties is helpful for AI applications. Prior works, such as ConceptNet, have compiled large CSK collections. However, they are restricted in their expressiveness to subject-predicate-object (SPO) triples with simple concepts for S and strings for P and O. This paper presents a method called ASCENT++ to automatically build a large-scale knowledge base (KB) of CSK assertions, with refined expressiveness and both better precision and recall than prior works. ASCENT++ goes beyond SPO triples by capturing composite concepts with subgroups and aspects, and by refining assertions with semantic facets. The latter is essential to express the temporal and spatial validity of assertions and further qualifiers. Furthermore, ASCENT++ combines open information extraction (OpenIE) with judicious cleaning and ranking by typicality and saliency scores. For high coverage, our method taps into the large-scale crawl C4 with broad web contents. The evaluation with human judgments shows the superior quality of the ASCENT++ KB, and an extrinsic evaluation for QA-support tasks underlines the benefits of ASCENT++. A web interface, data, and code can be accessed at https://ascentpp.mpi-inf.mpg.de/.