Question Answering over Linked Data (QALD-1)
While more and more semantic data is published on the Web, the question of how typical Web users can access this body of knowledge becomes of crucial importance. So far there are not many paradigms which would allow end users to profit from the expressive power of these standards while at the same time hiding the complexity behind an intuitive and easy-to-use interface. Important and promising research directed towards this goal is offered by search paradigms based on natural language question answering, which allow users to express arbitrarily complex information needs in an intuitive fashion. The essential problem lies in translating these information needs into a form such that they can be evaluated using standard Semantic Web query processing and inferencing techniques.
In recent years, there have been important advances in semantic search and question answering over RDF data. In parallel to these developments in the Semantic Web community, there has been substantial progress on question answering from textual data as well as in the area of natural language interfaces to databases. An important challenge for the Semantic Web, but also Natural Language Processing communities, is scaling question answering approaches to Linked Data. The main challenges involved herein are related to dealing with a heterogeneous, distributed and huge set of interlinked data.
Our goal is to bring together research and expertise in question answering from different communities, including NLP, HCI, Semantic Web and Databases. To facilitate the comparison between different approaches and systems, and to foster research in this area, the workshop is accompanied by an open challenge.
The following topics are of special interest:
- Question analysis
- Natural language processing approaches applied to semantic search engines
- Disambiguation and inferencing across multiple sources and domains
- Distributed evaluation of queries over Linked Data
- Lexical resources supporting question answering over Linked Data
- Discovery on the fly of relevant Linked Data sources
- Support for multiple languages
- Methods for scaling up question answering to Linked Data
- Efficiency and performance aspects
- Dealing with data and schema heterogeneity
- Answer merging and ranking
- Providing justifications of answers and conveying trust
- User feedback and interaction
- Habitability and usability aspects
- Evaluation of question answering approaches for Linked Data