What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Programming language
- Database
Jens Lehmann mainly focuses on Linked data, Information retrieval, RDF, Semantic Web and SPARQL.
His Linked data research incorporates themes from Crowdsourcing, Data quality, Set and Data science.
His research integrates issues of Semantics, World Wide Web and Data model in his study of Information retrieval.
Jens Lehmann has researched RDF in several fields, including Data integration and Data Web.
In his work, Description logic and Data structure is strongly intertwined with Web service, which is a subfield of Semantic Web.
The SPARQL study combines topics in areas such as Question answering and Knowledge base.
His most cited work include:
- DBpedia: a nucleus for a web of open data (3122 citations)
- DBpedia - A crystallization point for the Web of Data (1641 citations)
- DBpedia - A Large-scale, Multilingual Knowledge Base Extracted from Wikipedia (1606 citations)
What are the main themes of his work throughout his whole career to date?
Jens Lehmann mostly deals with Information retrieval, RDF, Linked data, Artificial intelligence and Semantic Web.
His research in the fields of Question answering overlaps with other disciplines such as Benchmarking.
His research investigates the connection between RDF and topics such as Scalability that intersect with problems in Big data.
His Linked data study is concerned with World Wide Web in general.
His Artificial intelligence research incorporates elements of Context, Machine learning and Natural language processing.
His biological study spans a wide range of topics, including Ontology and Description logic.
He most often published in these fields:
- Information retrieval (28.45%)
- RDF (27.32%)
- Linked data (24.23%)
What were the highlights of his more recent work (between 2019-2021)?
- Knowledge graph (19.44%)
- Artificial intelligence (25.07%)
- Embedding (10.99%)
In recent papers he was focusing on the following fields of study:
Jens Lehmann spends much of his time researching Knowledge graph, Artificial intelligence, Embedding, Question answering and Theoretical computer science.
His study looks at the intersection of Knowledge graph and topics like Data mining with Scalability.
His work deals with themes such as Machine learning and Natural language processing, which intersect with Artificial intelligence.
The study incorporates disciplines such as Parsing, Natural language and Pointer in addition to Question answering.
His Context research is multidisciplinary, incorporating elements of Quality, Information retrieval and Interface.
Information retrieval is a component of his SPARQL and RDF studies.
Between 2019 and 2021, his most popular works were:
- Encoding Knowledge Graph Entity Aliases in Attentive Neural Network for Wikidata Entity Linking. (9 citations)
- Evaluating the Impact of Knowledge Graph Context on Entity Disambiguation Models (6 citations)
- Bringing Light Into the Dark: A Large-scale Evaluation of Knowledge Graph Embedding Models Under a Unified Framework. (6 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Programming language
- Machine learning
Knowledge graph, Embedding, Theoretical computer science, Artificial intelligence and Entity linking are his primary areas of study.
His Knowledge graph research is under the purview of Information retrieval.
As part of his studies on Information retrieval, Jens Lehmann often connects relevant subjects like Structure.
His research in Artificial intelligence focuses on subjects like Natural language processing, which are connected to Component and Reinforcement learning.
His work carried out in the field of Entity linking brings together such families of science as Data science, Context, Transformer and Process.
Jens Lehmann focuses mostly in the field of Semantic Web, narrowing it down to matters related to Set and, in some cases, Ontology.
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