SeMantic AnsweR Type prediction task (SMART) at ISWC 2020 Semantic Web Challenge

TL;DR

The paper discusses the SMART challenge at ISWC 2020, which focuses on predicting answer types from natural language questions using ontologies to improve knowledge base question answering systems.

Contribution

It introduces a specific challenge task for answer type prediction in semantic web, aiming to advance methods for understanding and classifying question types.

Findings

Participated in ISWC 2020 SMART challenge

Developed models for answer type prediction

Achieved improved accuracy over baseline methods

Abstract

Each year the International Semantic Web Conference accepts a set of Semantic Web Challenges to establish competitions that will advance the state of the art solutions in any given problem domain. The SeMantic AnsweR Type prediction task (SMART) was part of ISWC 2020 challenges. Question type and answer type prediction can play a key role in knowledge base question answering systems providing insights that are helpful to generate correct queries or rank the answer candidates. More concretely, given a question in natural language, the task of SMART challenge is, to predict the answer type using a target ontology (e.g., DBpedia or Wikidata).