| 1 |
Querying knowledge graphs in natural language ...
|
|
|
|
Abstract:
Knowledge graphs are a powerful concept for querying large amounts of data. These knowledge graphs are typically enormous and are often not easily accessible to end-users because they require specialized knowledge in query languages such as SPARQL. Moreover, end-users need a deep understanding of the structure of the underlying data models often based on the Resource Description Framework (RDF). This drawback has led to the development of Question-Answering (QA) systems that enable end-users to express their information needs in natural language. While existing systems simplify user access, there is still room for improvement in the accuracy of these systems. In this paper we propose a new QA system for translating natural language questions into SPARQL queries. The key idea is to break up the translation process into 5 smaller, more manageable sub-tasks and use ensemble machine learning methods as well as Tree-LSTM-based neural network models to automatically learn and translate a natural language question ...
|
|
Keyword:
006 Spezielle Computerverfahren; 410.285 Computerlinguistik; Knowledge graphs; Natural language processing; Query processing; SPARQL
|
|
URL: https://dx.doi.org/10.21256/zhaw-21263
https://digitalcollection.zhaw.ch/handle/11475/21263
|
|
BASE
|
|
Hide details
|
|
|
|
|
