@inproceedings{bfa73507f5de47b69bf9e98efa47bb59,
title = "User experience-based information retrieval from semistar data ontologies",
abstract = "The time necessary for the doubling of medical knowledge is rapidly decreasing. In such circumstances, it is of utmost importance for the information retrieval process to be rapid, convenient and straightforward. However, it often lacks at least one of these properties. Several obstacles prohibit domain experts extracting knowledge from their databases without involving the third party in the form of IT professionals. The main limitation is usually the complexity of querying languages and tools. This paper proposes the approach of using a keywords-containing natural language for querying the database and exploiting the system that could automatically translate such queries to already existing target language that has an efficient implementation upon the database. The querying process is based on data conforming to a Semistar data ontology that has proven to be a very easily perceptible data structure for domain experts. Over time, the system can learn from the user actions, thus making the translation more accurate and the querying – more straightforward.",
keywords = "Information Retrieval, Query Language, Query Translation, Semistar Ontologies",
author = "Edgars Rencis",
note = "Publisher Copyright: Copyright {\textcopyright} 2019 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved; 11th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management, IC3K 2019 ; Conference date: 17-09-2019 Through 19-09-2019",
year = "2019",
doi = "10.5220/0008345004190426",
language = "English",
series = "IC3K 2019 - Proceedings of the 11th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management",
publisher = "SciTePress",
pages = "419--426",
editor = "Ana Fred and Joaquim Filipe",
booktitle = "IC3K 2019 - Proceedings of the 11th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management",
}