What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Programming language
- Database
His main research concerns Information retrieval, Linked data, Semantic Web, SPARQL and Question answering.
RDF is the focus of his Information retrieval research.
The study incorporates disciplines such as Scalability and Unstructured data in addition to RDF.
His Semantic Web study combines topics in areas such as Machine learning, Link, Key and Data mining.
His SPARQL research is multidisciplinary, relying on both Selection, Database and Data Web.
His Question answering research integrates issues from Ontology, Information needs, Library science, Natural language and Hidden Markov model.
His most cited work include:
- Template-based question answering over RDF data (364 citations)
- LIMES: a time-efficient approach for large-scale link discovery on the web of data (261 citations)
- An overview of the BIOASQ large-scale biomedical semantic indexing and question answering competition (232 citations)
What are the main themes of his work throughout his whole career to date?
His primary areas of investigation include Linked data, Information retrieval, RDF, Artificial intelligence and Semantic Web.
His research in Linked data focuses on subjects like Natural language, which are connected to Key.
His work on SPARQL as part of his general Information retrieval study is frequently connected to Context, thereby bridging the divide between different branches of science.
In his research, Entity linking is intimately related to Set, which falls under the overarching field of RDF.
His Artificial intelligence research focuses on Natural language processing and how it connects with Graph.
His Semantic Web research includes themes of Link, Theoretical computer science, State and Machine translation.
He most often published in these fields:
- Linked data (38.20%)
- Information retrieval (35.40%)
- RDF (34.16%)
What were the highlights of his more recent work (between 2018-2021)?
- Artificial intelligence (26.71%)
- Information retrieval (35.40%)
- RDF (34.16%)
In recent papers he was focusing on the following fields of study:
His primary areas of study are Artificial intelligence, Information retrieval, RDF, Knowledge graph and Linked data.
His biological study spans a wide range of topics, including Machine learning and Natural language processing.
In general Information retrieval study, his work on SPARQL, Semantic Web and Question answering often relates to the realm of Benchmarking, thereby connecting several areas of interest.
Axel-Cyrille Ngonga Ngomo interconnects Graph and Knowledge base in the investigation of issues within Question answering.
His studies in RDF integrate themes in fields like Domain, Database, Web crawler, Open data and Search engine indexing.
The various areas that Axel-Cyrille Ngonga Ngomo examines in his Linked data study include Data science and Data Web.
Between 2018 and 2021, his most popular works were:
- Knowledge Graphs (27 citations)
- How Representative Is a SPARQL Benchmark? An Analysis of RDF Triplestore Benchmarks (13 citations)
- HOBBIT: A platform for benchmarking Big Linked Data (9 citations)
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.
