What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Programming language
- Database
Frank van Harmelen mainly focuses on World Wide Web, Ontology, Semantic Web, Information retrieval and Artificial intelligence.
His work in the fields of World Wide Web, such as Semantic Web Stack, Web Ontology Language, Semantic Web Rule Language and OWL-S, intersects with other areas such as Information engineering.
His Ontology research includes elements of Data science, Information integration and Knowledge management.
His Semantic Web study also includes
- Ontology which is related to area like Ontology language,
- Ontology-based data integration which connect with Knowledge extraction.
The study of Information retrieval is intertwined with the study of Function in a number of ways.
His research integrates issues of Formal language, Software engineering and Natural language processing in his study of Artificial intelligence.
His most cited work include:
- From SHIQ and RDF to OWL: the making of a Web Ontology Language (1342 citations)
- A semantic Web primer (1213 citations)
- Sesame: A Generic Architecture for Storing and Querying RDF and RDF Schema (1195 citations)
What are the main themes of his work throughout his whole career to date?
His primary areas of investigation include Semantic Web, Information retrieval, World Wide Web, Ontology and Artificial intelligence.
Frank van Harmelen has researched Semantic Web in several fields, including Data science and Knowledge representation and reasoning.
His studies deal with areas such as Context, Metadata and Set as well as Information retrieval.
His study ties his expertise on Semantics together with the subject of World Wide Web.
His Ontology study frequently links to related topics such as Ontology.
His Social Semantic Web research incorporates themes from Web standards, Semantic computing, Semantic Web Stack and Data Web.
He most often published in these fields:
- Semantic Web (30.52%)
- Information retrieval (27.62%)
- World Wide Web (23.26%)
What were the highlights of his more recent work (between 2014-2021)?
- Information retrieval (27.62%)
- Semantic Web (30.52%)
- Linked data (7.85%)
In recent papers he was focusing on the following fields of study:
Frank van Harmelen spends much of his time researching Information retrieval, Semantic Web, Linked data, Artificial intelligence and World Wide Web.
His work on Semantic similarity, Ontology and SPARQL as part of general Information retrieval research is frequently linked to Evidence-based practice, bridging the gap between disciplines.
His Semantic Web research integrates issues from Metadata, Vocabulary and Data science.
His research on Linked data also deals with topics like
- Theoretical computer science that connect with fields like Set and Data structure,
- Knowledge graph most often made with reference to Conceptual framework.
His Artificial intelligence study incorporates themes from Machine learning, Differentiable function, Task and Natural language processing.
He mostly deals with Semantic Web Stack in his studies of World Wide Web.
Between 2014 and 2021, his most popular works were:
- Stream reasoning: A survey and outlook (63 citations)
- Why the Data Train Needs Semantic Rails (50 citations)
- Adaptive Linked Data-Driven Web Components: Building Flexible and Reusable Semantic Web Interfaces (23 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Programming language
- Database
The scientist’s investigation covers issues in Information retrieval, Semantic Web, Linked data, Data science and Artificial intelligence.
In the field of Information retrieval, his study on RDF overlaps with subjects such as Evidence-based practice.
Semantic Web is the subject of his research, which falls under World Wide Web.
His research in the fields of Social Semantic Web and Web modeling overlaps with other disciplines such as European community.
His research in Data science intersects with topics in Domain, Context, Stream reasoning and Usability.
His Artificial intelligence research is multidisciplinary, incorporating perspectives in Differentiable function, Semantics and Natural language processing.
This overview was generated by a machine learning system which analysed the scientist’s
body of work. If you have any feedback, you can
contact us
here.
