Log In Sign Up

Identify, Align, and Integrate: Matching Knowledge Graphs to Commonsense Reasoning Tasks

by   Lisa Bauer, et al.

Integrating external knowledge into commonsense reasoning tasks has shown progress in resolving some, but not all, knowledge gaps in these tasks. For knowledge integration to yield peak performance, it is critical to select a knowledge graph (KG) that is well-aligned with the given task's objective. We present an approach to assess how well a candidate KG can correctly identify and accurately fill in gaps of reasoning for a task, which we call KG-to-task match. We show this KG-to-task match in 3 phases: knowledge-task identification, knowledge-task alignment, and knowledge-task integration. We also analyze our transformer-based KG-to-task models via commonsense probes to measure how much knowledge is captured in these models before and after KG integration. Empirically, we investigate KG matches for the SocialIQA (SIQA) (Sap et al., 2019b), Physical IQA (PIQA) (Bisk et al., 2020), and MCScript2.0 (Ostermann et al., 2019) datasets with 3 diverse KGs: ATOMIC (Sap et al., 2019a), ConceptNet (Speer et al., 2017), and an automatically constructed instructional KG based on WikiHow (Koupaee and Wang, 2018). With our methods we are able to demonstrate that ATOMIC, an event-inference focused KG, is the best match for SIQA and MCScript2.0, and that the taxonomic ConceptNet and WikiHow-based KGs are the best matches for PIQA across all 3 analysis phases. We verify our methods and findings with human evaluation.


page 1

page 2

page 3

page 4


COMET: Commonsense Transformers for Automatic Knowledge Graph Construction

We present the first comprehensive study on automatic knowledge base con...

Symbolic Knowledge Distillation: from General Language Models to Commonsense Models

The common practice for training commonsense models has gone from-human-...

CREAK: A Dataset for Commonsense Reasoning over Entity Knowledge

Most benchmark datasets targeting commonsense reasoning focus on everyda...

CSKG: The CommonSense Knowledge Graph

Sources of commonsense knowledge aim to support applications in natural ...

Dense-ATOMIC: Construction of Densely-connected and Multi-hop Commonsense Knowledge Graph upon ATOMIC

ATOMIC is a large-scale commonsense knowledge graph (CSKG) containing ev...

Implicit Premise Generation with Discourse-aware Commonsense Knowledge Models

Enthymemes are defined as arguments where a premise or conclusion is lef...

An exploration of the influence of path choice in game-theoretic attribution algorithms

We compare machine learning explainability methods based on the theory o...

Code Repositories