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| Content Provider | ACM Digital Library |
|---|---|
| Author | Ngonga Ngomo, Axel-Cyrille Auer, Sören Shekarpour, Saeedeh |
| Abstract | The Data Web contains a wealth of knowledge on a large number of domains. Question answering over interlinked data sources is challenging due to two inherent characteristics. First, different datasets employ heterogeneous schemas and each one may only contain a part of the answer for a certain question. Second, constructing a federated formal query across different datasets requires exploiting links between the different datasets on both the schema and instance levels. We present a question answering system, which transforms user supplied queries (i.e. natural language sentences or keywords) into conjunctive SPARQL queries over a set of interlinked data sources. The contribution of this paper is two-fold: Firstly, we introduce a novel approach for determining the most suitable resources for a user-supplied query from different datasets (disambiguation). We employ a hidden Markov model, whose parameters were bootstrapped with different distribution functions. Secondly, we present a novel method for constructing a federated formal queries using the disambiguated resources and leveraging the linking structure of the underlying datasets. This approach essentially relies on a combination of domain and range inference as well as a link traversal method for constructing a connected graph which ultimately renders a corresponding SPARQL query. The results of our evaluation with three life-science datasets and 25 benchmark queries demonstrate the effectiveness of our approach. |
| Starting Page | 1145 |
| Ending Page | 1156 |
| Page Count | 12 |
| File Format | |
| ISBN | 9781450320351 |
| DOI | 10.1145/2488388.2488488 |
| Language | English |
| Publisher | Association for Computing Machinery (ACM) |
| Publisher Date | 2013-05-13 |
| Publisher Place | New York |
| Access Restriction | Subscribed |
| Content Type | Text |
| Resource Type | Article |
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