What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Operating system
- Database
His scientific interests lie mostly in Information retrieval, Artificial intelligence, World Wide Web, Natural language processing and Knowledge base.
His work on WordNet and Information extraction as part of general Information retrieval research is frequently linked to Ontology, thereby connecting diverse disciplines of science.
In his study, Variety is inextricably linked to Graph, which falls within the broad field of Artificial intelligence.
His study on Social Semantic Web is often connected to Credibility, Metaverse and Structure as part of broader study in World Wide Web.
His Natural language processing study incorporates themes from Entity linking, Pattern matching and Taxonomy.
The concepts of his Knowledge base study are interwoven with issues in Context, Knowledge extraction, Dimension and Semantic search.
His most cited work include:
- Yago: a core of semantic knowledge (2823 citations)
- YAGO2: A spatially and temporally enhanced knowledge base from Wikipedia (862 citations)
- The LRU-K page replacement algorithm for database disk buffering (840 citations)
What are the main themes of his work throughout his whole career to date?
His primary scientific interests are in Information retrieval, World Wide Web, Artificial intelligence, Natural language processing and Knowledge base.
His XML research extends to the thematically linked field of Information retrieval.
His study in Web page, Peer-to-peer, Data Web, Web standards and Social Semantic Web is carried out as part of his studies in World Wide Web.
His work deals with themes such as Context and Machine learning, which intersect with Artificial intelligence.
His Natural language processing study frequently links to related topics such as Entity linking.
In his work, Query optimization is strongly intertwined with Query expansion, which is a subfield of Web search query.
He most often published in these fields:
- Information retrieval (34.55%)
- World Wide Web (21.09%)
- Artificial intelligence (20.36%)
What were the highlights of his more recent work (between 2016-2021)?
- Artificial intelligence (20.36%)
- Information retrieval (34.55%)
- Natural language processing (14.42%)
In recent papers he was focusing on the following fields of study:
Gerhard Weikum mostly deals with Artificial intelligence, Information retrieval, Natural language processing, Question answering and Knowledge graph.
His work in Information extraction, Knowledge base, Ranking and Deep learning is related to Artificial intelligence.
His study on Information retrieval also encompasses disciplines like
- Commonsense knowledge, which have a strong connection to The Internet,
- Social media, which have a strong connection to Key.
His work in Natural language processing tackles topics such as Set which are related to areas like Conditional random field.
His study looks at the intersection of Question answering and topics like Context with Information needs, Benchmark and Rank.
His research integrates issues of Rule mining, Graph, Cluster analysis, Knowledge extraction and Asset in his study of Knowledge graph.
Between 2016 and 2021, his most popular works were:
- Where the Truth Lies: Explaining the Credibility of Emerging Claims on the Web and Social Media (141 citations)
- Equity of Attention: Amortizing Individual Fairness in Rankings (131 citations)
- Automated Template Generation for Question Answering over Knowledge Graphs (113 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Operating system
- Database
Gerhard Weikum focuses on Information retrieval, Artificial intelligence, Question answering, Natural language processing and Knowledge graph.
His Information retrieval study combines topics from a wide range of disciplines, such as Artificial neural network, Web page, Knowledge base, Fake news and Asset.
His Knowledge base research integrates issues from Ontology, Web Ontology Language, Description logic, Linked data and Cloud computing.
His Artificial intelligence study integrates concerns from other disciplines, such as Machine learning and Conversation.
He focuses mostly in the field of Natural language processing, narrowing it down to matters related to Set and, in some cases, Task, Relation and Conditional random field.
As part of one scientific family, Gerhard Weikum deals mainly with the area of Knowledge graph, narrowing it down to issues related to the Graph, and often Theoretical computer science, Leverage and Social group.
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