ACM Transactions on Data Science
E61729
ACM Transactions on Data Science is a peer-reviewed scholarly journal published by the Association for Computing Machinery that focuses on research in data science, including theory, methods, and applications.
All labels observed (1)
| Label | Occurrences |
|---|---|
| ACM Transactions on Data Science canonical | 1 |
How this entity was disambiguated
This entity first appeared as the object of triple T488010 — 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 Data Science Context triple: [ACM Transactions series, hasMemberJournal, ACM Transactions on Data Science]
-
A.
ACM Transactions on Knowledge Discovery from Data
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.
-
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.
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.
-
D.
ACM Transactions on Intelligent Systems and Technology
ACM Transactions on Intelligent Systems and Technology is a peer-reviewed scholarly journal published by the Association for Computing Machinery that focuses on research in artificial intelligence, machine learning, and intelligent systems.
-
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 Data Science Target entity description: ACM Transactions on Data Science is a peer-reviewed scholarly journal published by the Association for Computing Machinery that focuses on research in data science, including theory, methods, and applications.
-
A.
ACM Transactions on Knowledge Discovery from Data
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.
-
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.
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.
-
D.
ACM Transactions on Intelligent Systems and Technology
ACM Transactions on Intelligent Systems and Technology is a peer-reviewed scholarly journal published by the Association for Computing Machinery that focuses on research in artificial intelligence, machine learning, and intelligent systems.
-
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 (43)
| Predicate | Object |
|---|---|
| instanceOf |
academic journal
ⓘ
peer-reviewed journal ⓘ scientific journal ⓘ |
| academicField |
artificial intelligence
ⓘ
data science ⓘ information systems ⓘ |
| associatedOrganization | Association for Computing Machinery ⓘ |
| contentType |
applied research papers
ⓘ
research articles ⓘ survey papers ⓘ theoretical papers ⓘ |
| countryOfPublication |
United States
ⓘ
linked to:
United States of America
|
| discipline |
computer science
ⓘ
data science ⓘ |
| editorialPolicy | peer review ⓘ |
| focusesOn |
big data analytics
ⓘ
data management for data science ⓘ data mining ⓘ data science applications ⓘ data science methods ⓘ data science theory ⓘ data-driven applications ⓘ machine learning ⓘ statistical learning ⓘ |
| format | online journal ⓘ |
| hasAbbreviation |
ACM TDS
ⓘ
linked to:
ACM
|
| hasPublisherType | scientific society ⓘ |
| hasWebsite | https://dl.acm.org/journal/tds ⓘ |
| isPartOf | ACM Digital Library ⓘ |
| language | English ⓘ |
| publisher |
ACM
ⓘ
Association for Computing Machinery ⓘ |
| publishingModel |
hybrid open access
ⓘ
subscription-based ⓘ |
| reviewProcess | peer-reviewed ⓘ |
| subjectArea |
applied data science
ⓘ
computational methods ⓘ data science ⓘ data-intensive computing ⓘ theoretical data science ⓘ |
| targetAudience |
academics
ⓘ
data science practitioners ⓘ researchers ⓘ |
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 Data Science Description of subject: ACM Transactions on Data Science is a peer-reviewed scholarly journal published by the Association for Computing Machinery that focuses on research in data science, including theory, methods, and applications.
Referenced by (1)
Full triples — surface form annotated when it differs from this entity's canonical label.