Apache Mahout
E185681
Apache Mahout is an open-source machine learning library designed to build scalable algorithms for clustering, classification, and recommendation on large datasets, often leveraging big data platforms.
All labels observed (1)
| Label | Occurrences |
|---|---|
| Apache Mahout canonical | 1 |
How this entity was disambiguated
This entity first appeared as the object of triple T1647860 — 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: Apache Mahout Context triple: [Hadoop, ecosystemIncludes, Apache Mahout]
-
A.
scikit-learn
scikit-learn is a widely used open-source Python library that provides efficient tools for data mining, data analysis, and implementing a broad range of machine learning algorithms.
-
B.
Hadoop
Hadoop is an open-source framework that enables distributed storage and parallel processing of large data sets across clusters of commodity hardware.
-
C.
KMeans
KMeans is a popular unsupervised machine learning algorithm used for partitioning data into a specified number of clusters based on feature similarity.
-
D.
ML
ML is the postcode area in central Scotland that covers Motherwell and surrounding towns.
-
E.
ML
ML is a statically typed functional programming language developed at the University of Edinburgh, known for pioneering features like type inference, pattern matching, and modules that strongly influenced later languages such as Elm, Haskell, and OCaml.
- 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: Apache Mahout Target entity description: Apache Mahout is an open-source machine learning library designed to build scalable algorithms for clustering, classification, and recommendation on large datasets, often leveraging big data platforms.
-
A.
scikit-learn
scikit-learn is a widely used open-source Python library that provides efficient tools for data mining, data analysis, and implementing a broad range of machine learning algorithms.
-
B.
Hadoop
Hadoop is an open-source framework that enables distributed storage and parallel processing of large data sets across clusters of commodity hardware.
-
C.
KMeans
KMeans is a popular unsupervised machine learning algorithm used for partitioning data into a specified number of clusters based on feature similarity.
-
D.
ML
ML is the postcode area in central Scotland that covers Motherwell and surrounding towns.
-
E.
ML
ML is a statically typed functional programming language developed at the University of Edinburgh, known for pioneering features like type inference, pattern matching, and modules that strongly influenced later languages such as Elm, Haskell, and OCaml.
- F. None of above. chosen
Statements (46)
| Predicate | Object |
|---|---|
| instanceOf |
Apache Software Foundation project
ⓘ
machine learning library ⓘ open-source software ⓘ |
| category |
big data tool
ⓘ
data mining software ⓘ |
| designedFor |
big data analytics
ⓘ
large-scale datasets ⓘ |
| developer | Apache Software Foundation ⓘ |
| feature |
Scala DSL for linear algebra
ⓘ
distributed linear algebra framework ⓘ map-reduce based implementations (historical) ⓘ |
| focus |
distributed linear algebra
ⓘ
scalable machine learning algorithms ⓘ |
| integratesWith |
Apache Flink
ⓘ
Hadoop ⓘ
surface form:
Apache Hadoop
Apache Spark ⓘ |
| license | Apache License 2.0 ⓘ |
| partOf | Apache ecosystem ⓘ |
| programmingLanguage |
Java
ⓘ
Scala ⓘ |
| repository | https://github.com/apache/mahout ⓘ |
| runsOn |
Java Virtual Machine
ⓘ
surface form:
JVM
|
| status | active open-source project ⓘ |
| supportsAlgorithmType |
supervised learning
ⓘ
unsupervised learning ⓘ |
| supportsDataSource |
HDFS
ⓘ
distributed file systems ⓘ |
| supportsDeployment |
cloud environments
ⓘ
cluster environments ⓘ |
| supportsPlatform |
Linux
ⓘ
Windows ⓘ macOS ⓘ |
| supportsProgrammingModel |
distributed computing
ⓘ
scalable machine learning ⓘ |
| supportsTask |
classification
ⓘ
clustering ⓘ collaborative filtering ⓘ dimensionality reduction ⓘ recommendation ⓘ |
| useCase |
behavioral targeting
ⓘ
content personalization ⓘ customer segmentation ⓘ recommender systems ⓘ |
| website | https://mahout.apache.org ⓘ |
| writtenIn |
Java
ⓘ
Scala ⓘ |
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: Apache Mahout Description of subject: Apache Mahout is an open-source machine learning library designed to build scalable algorithms for clustering, classification, and recommendation on large datasets, often leveraging big data platforms.
Referenced by (1)
Full triples — surface form annotated when it differs from this entity's canonical label.