SIGKDD
E13737
SIGKDD is the ACM Special Interest Group on Knowledge Discovery and Data Mining, best known for its flagship KDD conference and contributions to data mining and machine learning research.
All labels observed (14)
How this entity was disambiguated
This entity first appeared as the object of triple T122394 — resolving that mention is where its identity was fixed. The disambiguator weighed these candidate entities and picked the highlighted one (or “None”, minting a new entity). This is how homonymy is resolved: the same surface form can point to different entities.
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: SIGKDD Context triple: [Special Interest Groups, hasExample, SIGKDD]
-
A.
Communications of the ACM
Communications of the ACM is a leading peer-reviewed magazine that publishes articles and research on computer science and information technology for the global computing community.
-
B.
ACM Computing Surveys
ACM Computing Surveys is a leading peer-reviewed journal that publishes comprehensive, in-depth survey articles covering major areas of computer science and computing research.
-
C.
ACM Digital Library
The ACM Digital Library is a comprehensive online research repository providing access to the Association for Computing Machinery’s journals, conference proceedings, technical magazines, and other computing-related publications.
-
D.
ACM Transactions series
The ACM Transactions series is a collection of peer-reviewed scholarly journals published by the Association for Computing Machinery, each focusing on a specific area of computer science and information technology research.
-
E.
IEEE Computer Society Conference on Computer Vision and Pattern Recognition
The IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR) is a premier annual international research conference showcasing cutting-edge advances in computer vision, machine learning, and pattern recognition.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: SIGKDD Target entity description: SIGKDD is the ACM Special Interest Group on Knowledge Discovery and Data Mining, best known for its flagship KDD conference and contributions to data mining and machine learning research.
-
A.
Communications of the ACM
Communications of the ACM is a leading peer-reviewed magazine that publishes articles and research on computer science and information technology for the global computing community.
-
B.
ACM Computing Surveys
ACM Computing Surveys is a leading peer-reviewed journal that publishes comprehensive, in-depth survey articles covering major areas of computer science and computing research.
-
C.
ACM Digital Library
The ACM Digital Library is a comprehensive online research repository providing access to the Association for Computing Machinery’s journals, conference proceedings, technical magazines, and other computing-related publications.
-
D.
ACM Transactions series
The ACM Transactions series is a collection of peer-reviewed scholarly journals published by the Association for Computing Machinery, each focusing on a specific area of computer science and information technology research.
-
E.
IEEE Computer Society Conference on Computer Vision and Pattern Recognition
The IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR) is a premier annual international research conference showcasing cutting-edge advances in computer vision, machine learning, and pattern recognition.
- F. None of above. chosen
Statements (47)
| Predicate | Object |
|---|---|
| instanceOf |
ACM special interest group
ⓘ
academic conference ⓘ professional association ⓘ |
| acronym | SIGKDD ⓘ |
| affiliationType |
ACM SIG
ⓘ
linked to:
ACM Special Interest Group
|
| community |
data mining researchers
ⓘ
data science practitioners ⓘ machine learning researchers ⓘ |
| field |
artificial intelligence
ⓘ
data mining ⓘ data mining ⓘ data science ⓘ knowledge discovery in databases ⓘ knowledge discovery in databases ⓘ machine learning ⓘ machine learning ⓘ |
| flagshipEvent |
KDD conference
ⓘ
linked to:
SIGKDD
|
| focus |
applications of data mining
ⓘ
research in knowledge discovery and data mining ⓘ theory and practice of machine learning ⓘ |
| fullName |
ACM Special Interest Group on Knowledge Discovery and Data Mining
ⓘ
linked to:
SIGKDD
|
| givesAward |
Best Research Paper Award
ⓘ
Best Student Paper Award ⓘ SIGKDD Innovation Award ⓘ SIGKDD Service Award ⓘ |
| hasWebsite | https://www.kdd.org/ ⓘ |
| hosts |
applied data science track at KDD conference
ⓘ
industrial track at KDD conference ⓘ research track at KDD conference ⓘ |
| language | English ⓘ |
| objective |
advance the state of the art in knowledge discovery and data mining
ⓘ
foster collaboration between academia and industry ⓘ promote the exchange of ideas in data mining and machine learning ⓘ |
| organizes |
KDD conference
ⓘ
linked to:
SIGKDD
|
| parentOrganization |
ACM
ⓘ
linked to:
Association for Computing Machinery
Association for Computing Machinery ⓘ |
| publishes | KDD conference proceedings ⓘ |
| regionServed | international ⓘ |
| sponsors |
data mining competitions
ⓘ
tutorials ⓘ workshops ⓘ |
| supports |
research awards
ⓘ
student travel awards ⓘ |
| topic |
knowledge extraction from data
ⓘ
large-scale data analysis ⓘ pattern discovery ⓘ predictive modeling ⓘ |
How these facts were elicited
The pipeline generated the facts above by prompting gpt-5.1 with this entity's name + description and the instruction below.
Instruction
You are a knowledge base construction expert. Given a subject entity and a description of it, return factual statements that you know for the subject as a JSON list of dictionaries(triples), where keys must be "subject", "predicate" and "object". The number of facts may be very high, between 25 to 50 or more, for very popular subjects. For less popular subjects, the number of facts can be very low, like 5 or 10. # Requirements - If you don't know the subject at all, return an empty list. - If the subject is not a named entity, return an empty list. - Include at least one triple where predicate is "instanceOf". - Do not get too wordy. - Separate several objects into multiple triples with one object.
Input
Subject: SIGKDD Description of subject: SIGKDD is the ACM Special Interest Group on Knowledge Discovery and Data Mining, best known for its flagship KDD conference and contributions to data mining and machine learning research.
Referenced by (32)
Full triples — surface form annotated when it differs from this entity's canonical label.
linked to: SIGKDD
linked to: SIGKDD
linked to: SIGKDD
Best Research Paper Award
→
presentedAt
→
ACM SIGKDD Conference on Knowledge Discovery and Data Mining
ⓘ
linked to: SIGKDD
linked to: SIGKDD
Best Research Paper Award
→
presentedBy
→
Special Interest Group on Knowledge Discovery and Data Mining
ⓘ
linked to: SIGKDD
linked to: SIGKDD
linked to: SIGKDD
SIGKDD Innovation Award
→
sponsor
→
ACM Special Interest Group on Knowledge Discovery and Data Mining
ⓘ
linked to: SIGKDD
linked to: SIGKDD
SIGKDD Innovation Award
→
presentedAt
→
ACM SIGKDD Conference on Knowledge Discovery and Data Mining
ⓘ
linked to: SIGKDD
linked to: SIGKDD
SIGKDD Service Award
→
presentedBy
→
ACM Special Interest Group on Knowledge Discovery and Data Mining
ⓘ
linked to: SIGKDD
linked to: SIGKDD
linked to: SIGKDD
linked to: SIGKDD
linked to: SIGKDD
linked to: SIGKDD
linked to: SIGKDD
linked to: SIGKDD
linked to: SIGKDD
Christos Faloutsos
→
hasPublishedIn
→
ACM SIGKDD Conference on Knowledge Discovery and Data Mining
ⓘ
linked to: SIGKDD
linked to: SIGKDD
linked to: SIGKDD
linked to: SIGKDD
linked to: SIGKDD
linked to: SIGKDD
linked to: SIGKDD