Name
Abstract | ||
|---|---|---|
We present a domain-agnostic system for Question Answering over multiple semi-structured and possibly linked datasets without the need of a training corpus. The system is motivated by an industry use-case where Enterprise Data needs to be combined with a large body of Open Data to fulfill information needs not satisfied by prescribed application data models. Our proposed Question Answering pipeline combines existing components with novel methods to perform, in turn, linguistic analysis of a query, named entity extraction, entity/graph search, fusion and ranking of possible answers. We evaluate QuerioDALI with two open-domain benchmarks and a biomedical one over Linked Open Data sources, and show that our system produces comparable results to systems that require training data and are domain-dependent. In addition, we analyze the current challenges and shortcomings. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1007/978-3-319-46547-0_32 | Lecture Notes in Computer Science |
Field | DocType | Volume |
Data modeling,Open data,Data mining,Knowledge graph,Computer science,Linked data,Natural language processing,Artificial intelligence,Enterprise data management,Question answering,Information needs,Information retrieval,Ranking | Conference | 9982 |
ISSN | Citations | PageRank |
0302-9743 | 5 | 0.42 |
References | Authors | |
12 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Vanessa Lopez | 1 | 725 | 48.98 |
| Pierpaolo Tommasi | 2 | 39 | 6.71 |
| Spyros Kotoulas | 3 | 590 | 46.46 |
| Jiewen Wu | 4 | 26 | 5.24 |