What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Programming language
- Machine learning
His primary areas of study are Linked data, Information retrieval, Data mining, RDF and World Wide Web.
His study explores the link between Linked data and topics such as Rdf graph that cross with problems in Word.
His research in Information retrieval intersects with topics in Metadata, Knowledge extraction and Benchmark.
He has included themes like Estimator, Web mining and Cluster analysis in his Data mining study.
His work in RDF addresses issues such as Theoretical computer science, which are connected to fields such as Recommender system.
His World Wide Web research is multidisciplinary, incorporating elements of Word-sense disambiguation and Key.
His most cited work include:
- Knowledge graph refinement: A survey of approaches and evaluation methods (431 citations)
- Adoption of the Linked Data Best Practices in Different Topical Domains (247 citations)
- RDF2Vec: RDF Graph Embeddings for Data Mining (220 citations)
What are the main themes of his work throughout his whole career to date?
His scientific interests lie mostly in Information retrieval, Linked data, World Wide Web, Artificial intelligence and Knowledge graph.
His study in Information retrieval is interdisciplinary in nature, drawing from both Task and Set.
His Linked data research is multidisciplinary, relying on both Recommender system, Data mining and Data science.
His study focuses on the intersection of World Wide Web and fields such as Human–computer interaction with connections in the field of Semantic computing.
His Artificial intelligence research incorporates elements of Machine learning, Pattern recognition and Natural language processing.
The various areas that Heiko Paulheim examines in his Knowledge graph study include Graph and Theoretical computer science.
He most often published in these fields:
- Information retrieval (30.04%)
- Linked data (26.61%)
- World Wide Web (21.03%)
What were the highlights of his more recent work (between 2019-2021)?
- Knowledge graph (17.17%)
- Information retrieval (30.04%)
- Theoretical computer science (8.15%)
In recent papers he was focusing on the following fields of study:
Heiko Paulheim spends much of his time researching Knowledge graph, Information retrieval, Theoretical computer science, Matching and Ontology alignment.
His Knowledge graph research integrates issues from Graph, Knowledge representation and reasoning, Benchmark, Empirical research and Subject.
While working on this project, he studies both Information retrieval and Popularity.
His studies in Matching integrate themes in fields like Ontology, Artificial intelligence, Extension, Supervised learning and Machine learning.
His Ontology alignment study incorporates themes from Data mining and Presentation.
His Linked data study in the realm of Semantic Web connects with subjects such as Common sense.
Between 2019 and 2021, his most popular works were:
- On cognitive preferences and the plausibility of rule-based models (19 citations)
- The knowledge graph track at OAEI : Gold standards, baselines, and the golden hammer bias (11 citations)
- Knowledge Graphs on the Web -- an Overview (6 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Programming language
- Machine learning
Knowledge graph, Information retrieval, Graph, Theoretical computer science and Matching are his primary areas of study.
His research integrates issues of Business domain, World Wide Web and Knowledge representation and reasoning in his study of Knowledge graph.
He merges many fields, such as Information retrieval and Group evaluation, in his writings.
His Graph study spans across into fields like Graph, Web API, Embedding, Current and Focus.
Heiko Paulheim has researched Matching in several fields, including Ontology, Artificial intelligence, Ontology alignment, Extension and Supervised learning.
His Ontology alignment research is multidisciplinary, incorporating perspectives in User interface, Task and Confidence threshold.
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