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Majigsuren Enkhsaikhan

dblp:14/4066 · DBLP profile ↗
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3ranked-venue papers
2as first author
1since 2021 · last 2021
0000-0003-4958-6762ORCID · corroborated

Domains — the database's venue-derived domains; a paper can count in several

Databases, data management, data science and information retrieval · 3 · 2 first-author · 1 since 2021Artificial intelligence and machine learning · 2 · 1 first-author

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Databases, data mining, and information retrieval
1 paper
Knowledge graphs · 100%
Artificial intelligence
1 paper
Information extraction and text analysis · 100%

Topics — the 3 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Knowledge graphs
knowledge graph construction		 0.412019
ICDM 2019 Knowledge Graph Contest: Team UWA · ICDM 2019
Knowledge graphs › knowledge graph construction
triple extraction		 0.412019
ICDM 2019 Knowledge Graph Contest: Team UWA · ICDM 2019
Natural language and speech › Information extraction and text analysis
named entity recognition		 0.112019
ICDM 2019 Knowledge Graph Contest: Team UWA · ICDM 2019

Methods — techniques the papers use, named apart from their topics

pipeline-based extraction · 0.8
YearPublicationVenuePosition
2021 Auto-labelling entities in low-resource text: a geological case study
Majigsuren Enkhsaikhan, Wei Liu 0006, Eun-Jung Holden, Paul Duuring
 Knowl. Inf. Syst.1
2019 ICDM 2019 Knowledge Graph Contest: Team UWA
abstract
We present an overview of our triple extraction system for the ICDM 2019 Knowledge Graph Contest. Our system uses a pipeline-based approach to extract a set of triples from a given document. It offers a simple and effective solution to the challenge of knowledge graph construction from domain-specific text. It also provides the facility to visualise useful information about each triple such as the degree, betweenness, structured relation type(s), and named entity types.
Michael Stewart 0006, Majigsuren Enkhsaikhan, Wei Liu 0006
 ICDM2
2018 Towards Geological Knowledge Discovery Using Vector-Based Semantic Similarity
Majigsuren Enkhsaikhan, Wei Liu 0006, Eun-Jung Holden, Paul Duuring
 ADMA1