# Towards a Question Answering System over the Semantic Web

## TL;DR

This paper presents a novel multilingual, KB-agnostic question answering approach over the Semantic Web, capable of querying multiple knowledge bases in various languages, enhancing accessibility and adaptability.

## Contribution

Introduces a new method for translating natural language questions into SPARQL that supports multiple KBs and languages, and demonstrates its effectiveness across five major knowledge bases.

## Key findings

- Effective querying of 5 large KBs in 5 languages
- Supports multiple KBs simultaneously and easily adaptable
- Validated approach through experimental evaluation

## Abstract

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.