An Automatic Pipeline for Biomedical Research Hotspot Mining: Keyphrase Mining in Specific Disease Fields (Preprint)

BACKGROUND With the increase in the number of biomedical scientific publications, it is of great value to characterize the research status of subtopics in this field, especially in the specific field of diseases. However, there has not been a fully automated pipeline for mining and analysing research hotspots in this field. OBJECTIVE We propose a completely automatic method based on natural language processing technology to analyize scientific innovations in a specific disease area. METHODS The whole pipeline consists of three steps, i.e. keyphrase extraction, clustering and cluster naming. The pipeline expands the existing literature analysis methods (including keyphrase extraction, document clustering, and paper ranking), adds advanced semantic mining technology (contextualized embeddings from pre-trained language models), and designs a document cluster naming strategy based on core document mining and topic-related phrase mining. With this pipeline, a full picture of the field of a specific disease is established. Distinct document clusters are generated to describe various subfields in disease-related research. Core documents and topic-related phrases are used to name clusters to interpret the concerns that researchers care about. Besides, the relations between clusters are analysed. Finally, several important clusters are analysed, whose core citation paths illustrate the research roadmap for a certain subfield and whose phrases directly describe the hotspots in each subfield. RESULTS We applied the method in the field of cataracts. From the 35117 cataract publications, the proposed method has extracted phrases with a high frequency like cataract extraction, cataract formation, intraocular pressure, etc. The method also found the most important documents in this field, which reveal the flow of research hotspots over time. 23 communities are generated and the top 10 topic-related phrases and core documents are extracted to name the communities. The cluster with the most paper is mainly about cataract formation. The cluster with the most high-impact papers focuses on common cataract diseases related to cataract epidemiology surveys. The cluster with the highest novelty and the highest progressiveness is related to the femtosecond laser technique. CONCLUSIONS This fully automated method can achieve the full picture of the research status of the field of a specific disease, without expert annotation.

Multilingualism in Kosovo and Switzerland; a Comparable Viewpoint of Language Policy

The main aim of the paper is to show the differences and similarities of the language use and language policy in The Republic of Kosovo and The Federation of Switzerland. It has a look in to the core documents of language including the constitution of both countries.

Knowledge Governance: Building a Conceptual Framework

This exploratory systematic literature review is a starting point for a deep literature review on “Knowledge Governance” (KGOV) topic. The aim is to have a quick picture about KGOV; specifically trying to identify the seminal, core and relevant documents. We also seek to know the contexts of these studies, as well as on what ontological levels and activities they refer to. The principal results are: a) the identification of the structure of the topic, by retrieving the main seminal articles and the most cited (core documents) and b) the building of a structured analysis framework. This framework will be used to perform a deep literature review that aim to develop an integrated and holist conceptual model on Knowledge Governance. Major conclusions are related to clues for future research on this topic.