# Question Answering on Linked Data: Challenges and Future Directions

arXiv: https://arxiv.org/abs/1601.03541 · HTML: https://mlsanity.com/paper/1601.03541/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Jan 2016.

## Built on by (most cited first, 3 of 3)

- [INODE: Building an End-to-End Data Exploration System in Practice [Extended Vision]](https://mlsanity.com/paper/2104.04194/) (2021)
- [Lexicalization Is All You Need: Examining the Impact of Lexical Knowledge in a Compositional QALD System](https://mlsanity.com/paper/2411.03906/) (2024)
- [CompoST: A Benchmark for Analyzing the Ability of LLMs To Compositionally Interpret Questions in a QALD Setting](https://mlsanity.com/paper/2507.21257/) (2025)

## Abstract (arXiv)

Question Answering (QA) systems are becoming the inspiring model for the future of search engines. While recently, underlying datasets for QA systems have been promoted from unstructured datasets to structured datasets with highly semantic-enriched metadata, but still question answering systems involve serious challenges which cause to be far beyond desired expectations. In this paper, we raise the challenges for building a Question Answering (QA) system especially with the focus of employing structured data (i.e. knowledge graph). This paper provide an exhaustive insight of the known challenges, so far. Thus, it helps researchers to easily spot open rooms for the future research agenda.