Towards Semantic Recommendation of Biodiversity Datasets based on Linked Open Data

Conventional content-based filtering methods recommend documents based on extracted keywords. They calculate the similarity between keywords and user interests and return a list of matching documents. In the long run, this approach often leads to overspecialization and fewer new entries with respect to a user’s preferences. Here, we propose a semantic recommender system using Linked Open Data for the user profile and adding semantic annotations to the index. Linked Open Data allows recommendations beyond the content domain and supports the detection of new information. One research area with a strong need for the discovery of new information is biodiversity. Due to their heterogeneity, the exploration of biodiversity data requires interdisciplinary collaboration. Personalization, in particular in recommender systems, can help to link the individual disciplines in biodiversity research and to discover relevant documents and datasets from various sources. We developed a first prototype for our semantic recommender system in this field, where a multitude of existing vocabularies facilitate our approach.