GoAsQ
GoAsQ
Generating and Answering Ontological Queries
Research group
People
- Yue Ma
- Franz Baader
- Stefan Borgwardt
- Meghyn Bienvenu
- Romain Beaumont
- Philippe Chatalic
- Philippe Dague
- Brigitte Grau
- Sanjay Kamath
- Anne-Laure Ligozat
- Chantal Reynaud
- Pierre Zweigenbaum
- Contact Franz Baader
- Authors Stefan Borgwardt
- https://goasq.lri.fr/
- 2016 – 2019
- funded by ANR (France) and DFG (Germany)
This is a three-year (2016-2019) project co-funded by ANR (France) and DFG (Germany), under the challenges of human-machine interactions, connected objects, numerical contents, and large-scale data and knowledge. Object
More and more information on individuals (e.g., persons, events, biological objects) are available electronically in a structured or semi-structured form. However, selecting individuals satisfying certain constraints based on such data manually is a complex, error-prone, and time and personnel consuming effort. For this reason, tools that can automatically or semiautomatically answer questions based on the available data need to be developed. While simple questions can directly be expressed and answered using keywords in natural language, complex questions that can refer to type and relational information increase the precision of the retrieved results, and thus reduce the effort for posterior manual verification of the results.
In the GoAsq project, we will investigate, compare, and finally combine two different approaches for answering questions formulated in natural language over textual, semi-structured, and structured data. One approach is the text-based question answering that directly answers natural language questions using natural language processing and information extraction techniques. The other tries to translate the natural language questions into formal, database-like queries and then answer these formal queries w.r.t. a domain-dependent ontology using database techniques.
Proceedings Articles
Query Rewriting for DL-Lite with n-ary Concrete Domains
In Carles Sierra, eds., Proceedings of the 26th International Joint Conference on Artificial Intelligence (IJCAI'17), to appear
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Most Probable Explanations for Probabilistic Database Queries
In Carles Sierra, eds., Proceedings of the 26th International Joint Conference on Artificial Intelligence (IJCAI 2017), to appear
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