# Semantic Answer Type and Relation Prediction Task (SMART 2021)

arXiv: https://arxiv.org/abs/2112.07606 · HTML: https://mlsanity.com/paper/2112.07606/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 30 references that are in the map.
- First posted on arXiv 7 Dec 2021.

## Builds on (most cited first, 9 of 9)

- [Complex Sequential Question Answering: Towards Learning to Converse Over Linked Question Answer Pairs with a Knowledge Graph](https://mlsanity.com/paper/1801.10314/) (2018)
- [A C-LSTM Neural Network for Text Classification](https://mlsanity.com/paper/1511.08630/) (2015)
- [Modeling Semantics with Gated Graph Neural Networks for Knowledge Base Question Answering](https://mlsanity.com/paper/1808.04126/) (2018)
- [Falcon 2.0: An Entity and Relation Linking Tool over Wikidata](https://mlsanity.com/paper/1912.11270/) (2019)
- [EARL: Joint Entity and Relation Linking for Question Answering over Knowledge Graphs](https://mlsanity.com/paper/1801.03825/) (2018)
- [Generative Relation Linking for Question Answering over Knowledge Bases](https://mlsanity.com/paper/2108.07337/) (2021)
- [Self-Adaptive Hierarchical Sentence Model](https://mlsanity.com/paper/1504.05070/) (2015)
- [Leveraging Semantic Parsing for Relation Linking over Knowledge Bases](https://mlsanity.com/paper/2009.07726/) (2020)
- [Semantic Answer Type Prediction using BERT: IAI at the ISWC SMART Task 2020](https://mlsanity.com/paper/2109.06714/) (2021)

## Built on by (most cited first, 1 of 1)

- [Konstruktor: A Strong Baseline for Simple Knowledge Graph Question Answering](https://mlsanity.com/paper/2409.15902/) (2024)

## Abstract (arXiv)

Each year the International Semantic Web Conference organizes a set of Semantic Web Challenges to establish competitions that will advance state-of-the-art solutions in some problem domains. The Semantic Answer Type and Relation Prediction Task (SMART) task is one of the ISWC 2021 Semantic Web challenges. This is the second year of the challenge after a successful SMART 2020 at ISWC 2020. This year's version focuses on two sub-tasks that are very important to Knowledge Base Question Answering (KBQA): Answer Type Prediction and Relation Prediction. Question type and answer type prediction can play a key role in knowledge base question answering systems providing insights about the expected answer that are helpful to generate correct queries or rank the answer candidates. More concretely, given a question in natural language, the first task is, to predict the answer type using a target ontology (e.g., DBpedia or Wikidata. Similarly, the second task is to identify relations in the natural language query and link them to the relations in a target ontology. This paper discusses the task descriptions, benchmark datasets, and evaluation metrics. For more information, please visit https://smart-task.github.io/2021/.