What is he best known for?
The fields of study he is best known for:
- Programming language
- Artificial intelligence
- World Wide Web
His primary areas of study are World Wide Web, Semantic Web, Information retrieval, Linked data and RDF.
His study in the field of Interoperability and SPARQL is also linked to topics like Action and Order by.
His Semantic Web research focuses on Data Web and how it relates to Web modeling and Semantic computing.
His work on Search engine indexing and Semantic search as part of general Information retrieval research is often related to Blank and Skolem normal form, thus linking different fields of science.
His Linked data research is multidisciplinary, incorporating elements of XML and Data science.
Aidan Hogan combines topics linked to Open data with his work on RDF.
His most cited work include:
- YARS2: a federated repository for querying graph structured data from the web (245 citations)
- SPARQL Web-Querying Infrastructure: Ready for Action? (176 citations)
- Searching and browsing Linked Data with SWSE: The Semantic Web Search Engine (175 citations)
What are the main themes of his work throughout his whole career to date?
His scientific interests lie mostly in Information retrieval, RDF, World Wide Web, Linked data and Semantic Web.
His studies in Information retrieval integrate themes in fields like Scalability and Data analysis.
His research integrates issues of Set, Data model, Benchmark, Semantics and Algorithm in his study of RDF.
His work on Web modeling and Semantic search as part of general World Wide Web study is frequently linked to Publishing and USable, bridging the gap between disciplines.
His research in Linked data intersects with topics in RDF Schema, Raw data, Data science and Web Ontology Language.
His work deals with themes such as Web crawler and Knowledge graph, which intersect with Semantic Web.
He most often published in these fields:
- Information retrieval (46.28%)
- RDF (40.50%)
- World Wide Web (33.06%)
What were the highlights of his more recent work (between 2017-2021)?
- Information retrieval (46.28%)
- SPARQL (34.71%)
- RDF (40.50%)
In recent papers he was focusing on the following fields of study:
Information retrieval, SPARQL, RDF, Knowledge graph and Semantic Web are his primary areas of study.
The study incorporates disciplines such as User interface and Data model in addition to Information retrieval.
His studies in SPARQL integrate themes in fields like Query language, Data mining, Theoretical computer science and Semantics.
The various areas that Aidan Hogan examines in his RDF study include Vocabulary, Set and Benchmark.
His Knowledge graph study combines topics from a wide range of disciplines, such as Visual query, Faceted search, Data science and Graph.
His primary area of study in Semantic Web is in the field of Linked data.
Between 2017 and 2021, his most popular works were:
- Information extraction meets the Semantic Web: A survey (37 citations)
- VoxEL: A Benchmark Dataset for Multilingual Entity Linking (16 citations)
- GraFa: Scalable Faceted Browsing for RDF Graphs (15 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Programming language
- Database
Aidan Hogan spends much of his time researching Entity linking, RDF, Semantic Web, Information retrieval and SPARQL.
He is researching Entity linking as part of the investigation of Knowledge base and World Wide Web.
In his study, Algorithm, Graph database and Set is strongly linked to Benchmark, which falls under the umbrella field of RDF.
The concepts of his Semantic Web study are interwoven with issues in Cardinality and Knowledge graph.
His Information retrieval research is multidisciplinary, relying on both Semantics, Vocabulary and Protocol.
His work on Blank node as part of general SPARQL study is frequently connected to Blank, therefore bridging the gap between diverse disciplines of science and establishing a new relationship between them.
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