# Towards a Question Answering System over the Semantic Web

arXiv: https://arxiv.org/abs/1803.00832 · HTML: https://mlsanity.com/paper/1803.00832/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Mar 2018.

## Built on by (most cited first, 14 of 14)

- [QALD-9-plus: A Multilingual Dataset for Question Answering over DBpedia and Wikidata Translated by Native Speakers](https://mlsanity.com/paper/2202.00120/) (2022)
- [UNIQORN: Unified Question Answering over RDF Knowledge Graphs and Natural Language Text](https://mlsanity.com/paper/2108.08614/) (2021)
- [Knowledge Graph Question Answering Leaderboard: A Community Resource to Prevent a Replication Crisis](https://mlsanity.com/paper/2201.08174/) (2022)
- [Knowledge Graph Question Answering using Graph-Pattern Isomorphism](https://mlsanity.com/paper/2103.06752/) (2021)
- [GETT-QA: Graph Embedding based T2T Transformer for Knowledge Graph Question Answering](https://mlsanity.com/paper/2303.13284/) (2023)
- [Knowledge Graph - Deep Learning: A Case Study in Question Answering in Aviation Safety Domain](https://mlsanity.com/paper/2205.15952/) (2022)
- [MST5 -- Multilingual Question Answering over Knowledge Graphs](https://mlsanity.com/paper/2407.06041/) (2024)
- [Learning to Transpile AMR into SPARQL](https://mlsanity.com/paper/2112.07877/) (2021)
- [Konstruktor: A Strong Baseline for Simple Knowledge Graph Question Answering](https://mlsanity.com/paper/2409.15902/) (2024)
- [Answer Candidate Type Selection: Text-to-Text Language Model for Closed Book Question Answering Meets Knowledge Graphs](https://mlsanity.com/paper/2310.07008/) (2023)
- [Integrating Multi-Head Convolutional Encoders with Cross-Attention for Improved SPARQL Query Translation](https://mlsanity.com/paper/2408.13432/) (2024)
- [Knowledge Graph Question Answering via SPARQL Silhouette Generation](https://mlsanity.com/paper/2109.09475/) (2021)
- [Lexicalization Is All You Need: Examining the Impact of Lexical Knowledge in a Compositional QALD System](https://mlsanity.com/paper/2411.03906/) (2024)
- [Improving the Question Answering Quality using Answer Candidate Filtering based on Natural-Language Features](https://mlsanity.com/paper/2112.05452/) (2021)

## Abstract (arXiv)

Thanks to the development of the Semantic Web, a lot of new structured data has become available on the Web in the form of knowledge bases (KBs). Making this valuable data accessible and usable for end-users is one of the main goals of Question Answering (QA) over KBs. Most current QA systems query one KB, in one language (namely English). The existing approaches are not designed to be easily adaptable to new KBs and languages. We first introduce a new approach for translating natural language questions to SPARQL queries. It is able to query several KBs simultaneously, in different languages, and can easily be ported to other KBs and languages. In our evaluation, the impact of our approach is proven using 5 different well-known and large KBs: Wikidata, DBpedia, MusicBrainz, DBLP and Freebase as well as 5 different languages namely English, German, French, Italian and Spanish. Second, we show how we integrated our approach, to make it easily accessible by the research community and by end-users. To summarize, we provided a conceptional solution for multilingual, KB-agnostic Question Answering over the Semantic Web. The provided first approximation validates this concept.