What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Programming language
- Information retrieval
His primary areas of study are Information retrieval, Artificial intelligence, Natural language processing, Explicit semantic analysis and Semantic similarity.
His study in the fields of Concept search, Information extraction and Search engine under the domain of Information retrieval overlaps with other disciplines such as Dynamics.
The Information extraction study combines topics in areas such as Correctness, Knowledge graph, Web content and Knowledge-based systems.
His Artificial intelligence study frequently links to other fields, such as Machine learning.
Evgeniy Gabrilovich is involved in the study of Natural language processing that focuses on Natural language in particular.
His Semantic similarity research integrates issues from Semantics, SemEval and Semantic property.
His most cited work include:
- Computing semantic relatedness using Wikipedia-based explicit semantic analysis (1858 citations)
- Knowledge vault: a web-scale approach to probabilistic knowledge fusion (1051 citations)
- Placing search in context: the concept revisited (852 citations)
What are the main themes of his work throughout his whole career to date?
His main research concerns Information retrieval, World Wide Web, Artificial intelligence, Web search query and Natural language processing.
In his research on the topic of Information retrieval, Data mining is strongly related with Web page.
Within one scientific family, Evgeniy Gabrilovich focuses on topics pertaining to Machine learning under Artificial intelligence, and may sometimes address concerns connected to Knowledge graph.
His study in the field of Semantic similarity and Natural language is also linked to topics like Explicit semantic analysis and Encyclopedia.
His studies examine the connections between Search engine and genetics, as well as such issues in Search engine indexing, with regards to Concept search.
The study incorporates disciplines such as Probabilistic logic and Correctness in addition to Information extraction.
He most often published in these fields:
- Information retrieval (58.87%)
- World Wide Web (30.50%)
- Artificial intelligence (21.28%)
What were the highlights of his more recent work (between 2013-2020)?
- Information retrieval (58.87%)
- World Wide Web (30.50%)
- Knowledge base (6.38%)
In recent papers he was focusing on the following fields of study:
Evgeniy Gabrilovich focuses on Information retrieval, World Wide Web, Knowledge base, Information extraction and Crowdsourcing.
His Information retrieval study integrates concerns from other disciplines, such as Annotation and Web page.
The Web service and Web application research Evgeniy Gabrilovich does as part of his general World Wide Web study is frequently linked to other disciplines of science, such as Corporate governance and Quality, therefore creating a link between diverse domains of science.
His studies in Knowledge base integrate themes in fields like Field, Data science and Knowledge graph.
His Information extraction study is focused on Artificial intelligence in general.
The various areas that he examines in his Artificial intelligence study include Machine learning and Natural language processing.
Between 2013 and 2020, his most popular works were:
- Knowledge vault: a web-scale approach to probabilistic knowledge fusion (1051 citations)
- A Review of Relational Machine Learning for Knowledge Graphs (729 citations)
- Knowledge base completion via search-based question answering (195 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Programming language
- World Wide Web
Evgeniy Gabrilovich mainly investigates Information retrieval, Information extraction, Artificial intelligence, World Wide Web and Knowledge base.
His Information retrieval study incorporates themes from Probabilistic logic, Web page and Data mining.
As part of one scientific family, he deals mainly with the area of Probabilistic logic, narrowing it down to issues related to the Correctness, and often Knowledge extraction and Web content.
His work in Artificial intelligence covers topics such as Machine learning which are related to areas like Knowledge graph and Knowledge-based systems.
Evgeniy Gabrilovich has researched World Wide Web in several fields, including Annotation and Cover.
His Knowledge base study combines topics from a wide range of disciplines, such as Field, Relation, Open research and State.
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