Towards a Semantic Search Engine for Scientific Articles
This work targets researchers and readers struggling with information overload in scientific publications, but it is incremental as it provides initial ideas rather than a fully developed solution.
The paper addresses the challenge of finding relevant scientific articles due to the data deluge by proposing a semantic search engine that extracts relations between keywords to classify articles, aiming to improve browsing and search processes.
Because of the data deluge in scientific publication, finding relevant information is getting harder and harder for researchers and readers. Building an enhanced scientific search engine by taking semantic relations into account poses a great challenge. As a starting point, semantic relations between keywords from scientific articles could be extracted in order to classify articles. This might help later in the process of browsing and searching for content in a meaningful scientific way. Indeed, by connecting keywords, the context of the article can be extracted. This paper aims to provide ideas to build such a smart search engine and describes the initial contributions towards achieving such an ambitious goal.