SeMantic AnsweR Type prediction task (SMART) at ISWC 2020 Semantic Web Challenge
This task addresses the problem of improving knowledge base question answering systems for users by enabling more accurate query generation and answer ranking.
The SeMantic AnsweR Type prediction task (SMART) at ISWC 2020 focused on predicting the answer type for natural language questions using target ontologies like DBpedia or Wikidata. This task aims to improve knowledge base question answering systems by providing insights for query generation and answer ranking.
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).