• Corpus ID: 233739727

Commonsense Knowledge Base Construction in the Age of Big Data

@article{Razniewski2021CommonsenseKB,
  title={Commonsense Knowledge Base Construction in the Age of Big Data},
  author={Simon Razniewski},
  journal={ArXiv},
  year={2021},
  volume={abs/2105.01925}
}
: Compiling commonsense knowledge is traditionally an AI topic approached by manual labor. Recent advances in web data processing have enabled automated ap- proaches. In this demonstration we will showcase three systems for automated commonsense knowledge base construction, highlighting each time one aspect of specific interest to the data management community. (i) We use Quasimodo to illustrate knowledge extraction systems engineering, (ii) Dice to illustrate the role that schema constraints… 

References

SHOWING 1-10 OF 15 REFERENCES

TransOMCS: From Linguistic Graphs to Commonsense Knowledge

Experimental results demonstrate the transferability of linguistic knowledge to commonsense knowledge and the effectiveness of the proposed approach in terms of quantity, novelty, and quality.

Inside Quasimodo: Exploring Construction and Usage of Commonsense Knowledge

A companion web portal is presented which allows to explore the data, to run and analyze the extraction pipeline live, and to inspect the usage of Quasimodo's knowledge in several downstream use cases.

Machine Knowledge: Creation and Curation of Comprehensive Knowledge Bases

Equipping machines with comprehensive knowledge of the world's entities and their relationships has been a long-standing goal of AI. Over the last decade, large-scale knowledge bases, also known as

Dice: A Joint Reasoning Framework for Multi-Faceted Commonsense Knowledge

A web prototype for the Dice framework for joint consolidation of noisy multi-faceted commonsense knowledge (CSK) and the opportunity to inspect grounded constraint systems and resulting inferences for two CSK collections, ConceptNet and Quasimodo is presented.

Commonsense Properties from Query Logs and Question Answering Forums

Quasimodo, a methodology and tool suite for distilling commonsense properties from non-standard web sources that focuses on salient properties that are typically associated with certain objects or concepts, is presented.

CSKG: The CommonSense Knowledge Graph

This paper shows the impact of CSKG as a source for reasoning evidence retrieval, and for pre-training language models for generalizable downstream reasoning, and applies five principles to combine seven key sources into a first integrated CommonSense Knowledge Graph (CSKG).

CauseNet: Towards a Causality Graph Extracted from the Web

CauseNet is compiled, a large-scale knowledge base of claimed causal relations between causal concepts and the first large- scale and open-domain causality graph is constructed, to gain insights about causal beliefs expressed on the web.

Extracting common sense knowledge via triple ranking using supervised and unsupervised distributional models

This paper presents information extraction models that support the extraction of common sense knowledge from a combination of unstructured and semi-structured datasets and compares the different approaches and parameterizations on the task of extracting two types of relations: locative and instrumental relations.

ATOMIC: An Atlas of Machine Commonsense for If-Then Reasoning

Experimental results demonstrate that multitask models that incorporate the hierarchical structure of if-then relation types lead to more accurate inference compared to models trained in isolation, as measured by both automatic and human evaluation.

CommonsenseQA: A Question Answering Challenge Targeting Commonsense Knowledge

This work presents CommonsenseQA: a challenging new dataset for commonsense question answering, which extracts from ConceptNet multiple target concepts that have the same semantic relation to a single source concept.