Controlling Search in Very large Commonsense Knowledge Bases: A Machine Learning Approach

03/14/2016
by   Abhishek Sharma, et al.
0

Very large commonsense knowledge bases (KBs) often have thousands to millions of axioms, of which relatively few are relevant for answering any given query. A large number of irrelevant axioms can easily overwhelm resolution-based theorem provers. Therefore, methods that help the reasoner identify useful inference paths form an essential part of large-scale reasoning systems. In this paper, we describe two ordering heuristics for optimization of reasoning in such systems. First, we discuss how decision trees can be used to select inference steps that are more likely to succeed. Second, we identify a small set of problem instance features that suffice to guide searches away from intractable regions of the search space. We show the efficacy of these techniques via experiments on thousands of queries from the Cyc KB. Results show that these methods lead to an order of magnitude reduction in inference time.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
03/29/2020

Extending Automated Deduction for Commonsense Reasoning

Commonsense reasoning has long been considered as one of the holy grails...
research
10/12/2022

CIKQA: Learning Commonsense Inference with a Unified Knowledge-in-the-loop QA Paradigm

Recently, the community has achieved substantial progress on many common...
research
05/10/2023

Decker: Double Check with Heterogeneous Knowledge for Commonsense Fact Verification

Commonsense fact verification, as a challenging branch of commonsense qu...
research
10/10/2020

Beyond Language: Learning Commonsense from Images for Reasoning

This paper proposes a novel approach to learn commonsense from images, i...
research
10/30/2019

Towards Generalizable Neuro-Symbolic Systems for Commonsense Question Answering

Non-extractive commonsense QA remains a challenging AI task, as it requi...
research
07/21/2018

Towards Neural Theorem Proving at Scale

Neural models combining representation learning and reasoning in an end-...
research
10/02/2022

Does Wikidata Support Analogical Reasoning?

Analogical reasoning methods have been built over various resources, inc...

Please sign up or login with your details

Forgot password? Click here to reset