A scholarly recommendation system is an important tool for identifying prior and related
resources such as literature, datasets, grants, and collaborators. A well-designed scholarly …

We provide a comprehensive survey of the research literature that applies Information
Extraction techniques in a Semantic Web setting. Works in the intersection of these two …

With the increasing popularity of large scale Knowledge Graph (KG) s, many applications
such as semantic analysis, search and question answering need to link entity mentions in …

Semantic relatedness and disambiguation are fundamental problems for linking text
documents to the Web of Data. There are many approaches dealing with both problems but …

It is a growing trend among researchers to make their data publicly available for
experimental reproducibility and data reusability. Sharing data with fellow researchers helps …

The importance of query performance prediction has been widely acknowledged in the
literature, especially for query expansion, refinement, and interpolating different retrieval …

With the growing quantity and diversity of publicly available web datasets, most notably
Linked Open Data, recommending datasets, which meet specific criteria, has become an …

The increasing adoption of Linked Data principles has led to an abundance of datasets on
the Web. However, take-up and reuse is hindered by the lack of descriptive information …

Wikipedia, rich in entities and events, is an invaluable resource for various knowledge
harvesting, extraction and mining tasks. Numerous resources like DBpedia, YAGO and other …

The constantly growing amount of Web content and the success of the Social Web lead to
increasing needs for Web archiving. These needs go beyond the pure preservation of Web …