Towards a Semantic Search Engine for Scientific Articles

TL;DR

This paper discusses initial ideas and approaches for developing a semantic search engine for scientific articles by extracting and utilizing semantic relations between keywords to improve content classification and retrieval.

Contribution

It introduces initial concepts and methods for extracting semantic relations between keywords to enhance scientific article classification and search capabilities.

Findings

Semantic relations can be extracted from keywords in scientific articles.

Connecting keywords helps in understanding article context.

Initial framework for a semantic scientific search engine is proposed.

Abstract

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.