ACM Transactions on Knowledge Discovery from Data
E61257
ACM Transactions on Knowledge Discovery from Data is a peer-reviewed scholarly journal published by the Association for Computing Machinery that focuses on research in data mining, knowledge discovery, and related areas of data science and machine learning.
All labels observed (2)
| Label | Occurrences |
|---|---|
| ACM Transactions on Knowledge Discovery from Data canonical | 2 |
| ACM TKDD | 1 |
How this entity was disambiguated
This entity first appeared as the object of triple T487991 — 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: ACM Transactions on Knowledge Discovery from Data Context triple: [ACM Transactions series, hasMemberJournal, ACM Transactions on Knowledge Discovery from Data]
-
A.
SIGKDD
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.
-
B.
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.
-
C.
ACM Guide to Computing Literature
The ACM Guide to Computing Literature is a comprehensive bibliographic database that indexes and abstracts worldwide research publications in computer science and related fields.
-
D.
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.
-
E.
SIGMOD
SIGMOD is a leading ACM special interest group focused on the research and development of data management and database systems.
- 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: ACM Transactions on Knowledge Discovery from Data Target entity description: ACM Transactions on Knowledge Discovery from Data is a peer-reviewed scholarly journal published by the Association for Computing Machinery that focuses on research in data mining, knowledge discovery, and related areas of data science and machine learning.
-
A.
SIGKDD
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.
-
B.
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.
-
C.
ACM Guide to Computing Literature
The ACM Guide to Computing Literature is a comprehensive bibliographic database that indexes and abstracts worldwide research publications in computer science and related fields.
-
D.
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.
-
E.
SIGMOD
SIGMOD is a leading ACM special interest group focused on the research and development of data management and database systems.
- F. None of above. chosen
Statements (47)
| Predicate | Object |
|---|---|
| instanceOf |
academic journal
ⓘ
peer-reviewed journal ⓘ scientific journal ⓘ |
| abbreviation |
ACM Transactions on Knowledge Discovery from Data
self-linksurface differs
ⓘ
surface form:
ACM TKDD
|
| academicDiscipline |
artificial intelligence
ⓘ
computer science ⓘ data mining ⓘ data science ⓘ knowledge discovery ⓘ machine learning ⓘ |
| contentType |
applied research
ⓘ
research articles ⓘ survey papers ⓘ technical papers ⓘ theoretical studies ⓘ |
| countryOfPublication |
United States of America
ⓘ
surface form:
United States
|
| field |
applied machine learning
ⓘ
big data analytics ⓘ data mining algorithms ⓘ information retrieval ⓘ knowledge discovery in databases ⓘ knowledge representation ⓘ pattern mining ⓘ predictive modeling ⓘ statistical learning ⓘ |
| format |
online journal
ⓘ
print journal ⓘ |
| language | English ⓘ |
| peerReviewed | true ⓘ |
| publisher | Association for Computing Machinery ⓘ |
| publisherAbbreviation | ACM ⓘ |
| publisherType | scientific society ⓘ |
| publishingOrganization | ACM ⓘ |
| reviewProcess | single-blind peer review ⓘ |
| subjectArea |
computing
ⓘ
information systems ⓘ |
| targetAudience |
academics
ⓘ
practitioners in data mining ⓘ researchers ⓘ |
| topic |
applications of data mining
ⓘ
data preprocessing and cleaning ⓘ evaluation of data mining algorithms ⓘ feature selection and extraction ⓘ knowledge discovery from large datasets ⓘ model interpretability in data mining ⓘ privacy and security in data mining ⓘ scalable data mining methods ⓘ |
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: ACM Transactions on Knowledge Discovery from Data Description of subject: ACM Transactions on Knowledge Discovery from Data is a peer-reviewed scholarly journal published by the Association for Computing Machinery that focuses on research in data mining, knowledge discovery, and related areas of data science and machine learning.
Referenced by (3)
Full triples — surface form annotated when it differs from this entity's canonical label.
ACM Transactions on Knowledge Discovery from Data
→
abbreviation
→
ACM Transactions on Knowledge Discovery from Data
self-linksurface differs
ⓘ
this entity surface form:
ACM TKDD