Declarative Modeling for Machine Learning and Data Mining View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2012

AUTHORS

Luc De Raedt

ABSTRACT

Despite the popularity of machine learning and data mining today, it remains challenging to develop applications and software that incorporates machine learning or data mining techniques. This is because machine learning and data mining have focussed on developing high-performance algorithms for solving particular tasks rather than on developing general principles and techniques. I propose to alleviate these problems by applying the constraint programming methodology to machine learning and data mining and to specify machine learning and data mining problems as constraint satisfaction and optimization problems. What is essential is that the user be provided with a way to declaratively specify what the machine learning or data mining problem is rather than having to outline how that solution needs to be computed. This corresponds to a model + solver-based approach to machine learning and data mining, in which the user specifies the problem in a high level modeling language and the system automatically transforms such models into a format that can be used by a solver to efficiently generate a solution. This should be much easier for the user than having to implement or adapt an algorithm that computes a particular solution to a specific problem. I shall illustrate this using our results on constraint programming for itemset mining [1] and probabilistic programming. Some further ideas along these lines are contained in [2]. More... »

PAGES

2-2

References to SciGraph publications

Book

TITLE

Formal Concept Analysis

ISBN

978-3-642-29891-2
978-3-642-29892-9

Author Affiliations

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-3-642-29892-9_2

DOI

http://dx.doi.org/10.1007/978-3-642-29892-9_2

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1009434467


Indexing Status Check whether this publication has been indexed by Scopus and Web Of Science using the SN Indexing Status Tool
Incoming Citations Browse incoming citations for this publication using opencitations.net

JSON-LD is the canonical representation for SciGraph data.

TIP: You can open this SciGraph record using an external JSON-LD service: JSON-LD Playground Google SDTT

[
  {
    "@context": "https://springernature.github.io/scigraph/jsonld/sgcontext.json", 
    "about": [
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/0801", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Artificial Intelligence and Image Processing", 
        "type": "DefinedTerm"
      }, 
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/08", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Information and Computing Sciences", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "KU Leuven", 
          "id": "https://www.grid.ac/institutes/grid.5596.f", 
          "name": [
            "Department of Computer Science, Katholieke Universiteit Leuven, Celestijnenlaan 200A, 3001\u00a0Heverlee, Belgium"
          ], 
          "type": "Organization"
        }, 
        "familyName": "De Raedt", 
        "givenName": "Luc", 
        "id": "sg:person.015333627665.77", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.015333627665.77"
        ], 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "sg:pub.10.1007/978-3-642-21916-0_3", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1002233268", 
          "https://doi.org/10.1007/978-3-642-21916-0_3"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/978-3-642-21916-0_3", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1002233268", 
          "https://doi.org/10.1007/978-3-642-21916-0_3"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.artint.2011.05.002", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1013588077"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2012", 
    "datePublishedReg": "2012-01-01", 
    "description": "Despite the popularity of machine learning and data mining today, it remains challenging to develop applications and software that incorporates machine learning or data mining techniques. This is because machine learning and data mining have focussed on developing high-performance algorithms for solving particular tasks rather than on developing general principles and techniques. I propose to alleviate these problems by applying the constraint programming methodology to machine learning and data mining and to specify machine learning and data mining problems as constraint satisfaction and optimization problems. What is essential is that the user be provided with a way to declaratively specify what the machine learning or data mining problem is rather than having to outline how that solution needs to be computed. This corresponds to a model + solver-based approach to machine learning and data mining, in which the user specifies the problem in a high level modeling language and the system automatically transforms such models into a format that can be used by a solver to efficiently generate a solution. This should be much easier for the user than having to implement or adapt an algorithm that computes a particular solution to a specific problem. I shall illustrate this using our results on constraint programming for itemset mining [1] and probabilistic programming. Some further ideas along these lines are contained in [2].", 
    "editor": [
      {
        "familyName": "Domenach", 
        "givenName": "Florent", 
        "type": "Person"
      }, 
      {
        "familyName": "Ignatov", 
        "givenName": "Dmitry I.", 
        "type": "Person"
      }, 
      {
        "familyName": "Poelmans", 
        "givenName": "Jonas", 
        "type": "Person"
      }
    ], 
    "genre": "chapter", 
    "id": "sg:pub.10.1007/978-3-642-29892-9_2", 
    "inLanguage": [
      "en"
    ], 
    "isAccessibleForFree": false, 
    "isPartOf": {
      "isbn": [
        "978-3-642-29891-2", 
        "978-3-642-29892-9"
      ], 
      "name": "Formal Concept Analysis", 
      "type": "Book"
    }, 
    "name": "Declarative Modeling for Machine Learning and Data Mining", 
    "pagination": "2-2", 
    "productId": [
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1007/978-3-642-29892-9_2"
        ]
      }, 
      {
        "name": "readcube_id", 
        "type": "PropertyValue", 
        "value": [
          "2cf16ced222cb76f9cdbb0f20d0b756af6784d31942d0a25badc9443cc1dbd65"
        ]
      }, 
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1009434467"
        ]
      }
    ], 
    "publisher": {
      "location": "Berlin, Heidelberg", 
      "name": "Springer Berlin Heidelberg", 
      "type": "Organisation"
    }, 
    "sameAs": [
      "https://doi.org/10.1007/978-3-642-29892-9_2", 
      "https://app.dimensions.ai/details/publication/pub.1009434467"
    ], 
    "sdDataset": "chapters", 
    "sdDatePublished": "2019-04-15T17:11", 
    "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
    "sdPublisher": {
      "name": "Springer Nature - SN SciGraph project", 
      "type": "Organization"
    }, 
    "sdSource": "s3://com-uberresearch-data-dimensions-target-20181106-alternative/cleanup/v134/2549eaecd7973599484d7c17b260dba0a4ecb94b/merge/v9/a6c9fde33151104705d4d7ff012ea9563521a3ce/jats-lookup/v90/0000000001_0000000264/records_8678_00000248.jsonl", 
    "type": "Chapter", 
    "url": "http://link.springer.com/10.1007/978-3-642-29892-9_2"
  }
]
 

Download the RDF metadata as:  json-ld nt turtle xml License info

HOW TO GET THIS DATA PROGRAMMATICALLY:

JSON-LD is a popular format for linked data which is fully compatible with JSON.

curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/pub.10.1007/978-3-642-29892-9_2'

N-Triples is a line-based linked data format ideal for batch operations.

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1007/978-3-642-29892-9_2'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/978-3-642-29892-9_2'

RDF/XML is a standard XML format for linked data.

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/978-3-642-29892-9_2'


 

This table displays all metadata directly associated to this object as RDF triples.

82 TRIPLES      23 PREDICATES      29 URIs      20 LITERALS      8 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1007/978-3-642-29892-9_2 schema:about anzsrc-for:08
2 anzsrc-for:0801
3 schema:author Naa04fc51e3e1439aac1dd1b65a0a98fe
4 schema:citation sg:pub.10.1007/978-3-642-21916-0_3
5 https://doi.org/10.1016/j.artint.2011.05.002
6 schema:datePublished 2012
7 schema:datePublishedReg 2012-01-01
8 schema:description Despite the popularity of machine learning and data mining today, it remains challenging to develop applications and software that incorporates machine learning or data mining techniques. This is because machine learning and data mining have focussed on developing high-performance algorithms for solving particular tasks rather than on developing general principles and techniques. I propose to alleviate these problems by applying the constraint programming methodology to machine learning and data mining and to specify machine learning and data mining problems as constraint satisfaction and optimization problems. What is essential is that the user be provided with a way to declaratively specify what the machine learning or data mining problem is rather than having to outline how that solution needs to be computed. This corresponds to a model + solver-based approach to machine learning and data mining, in which the user specifies the problem in a high level modeling language and the system automatically transforms such models into a format that can be used by a solver to efficiently generate a solution. This should be much easier for the user than having to implement or adapt an algorithm that computes a particular solution to a specific problem. I shall illustrate this using our results on constraint programming for itemset mining [1] and probabilistic programming. Some further ideas along these lines are contained in [2].
9 schema:editor N36d2641db93141b1a492c2db4d143f86
10 schema:genre chapter
11 schema:inLanguage en
12 schema:isAccessibleForFree false
13 schema:isPartOf N748af7f710da471fac18b2195f9e71e0
14 schema:name Declarative Modeling for Machine Learning and Data Mining
15 schema:pagination 2-2
16 schema:productId N3e4a3ce7716345238e3a2f12e5b8f9ca
17 N7a27fa497cb447eeb57d3a776acfe59f
18 N826c602cfc354a75ad7014164ba9023a
19 schema:publisher N10cee1c099334a95ad72642a62c7e97c
20 schema:sameAs https://app.dimensions.ai/details/publication/pub.1009434467
21 https://doi.org/10.1007/978-3-642-29892-9_2
22 schema:sdDatePublished 2019-04-15T17:11
23 schema:sdLicense https://scigraph.springernature.com/explorer/license/
24 schema:sdPublisher Ndf5332ef900440fd8431bf4e99f56a5d
25 schema:url http://link.springer.com/10.1007/978-3-642-29892-9_2
26 sgo:license sg:explorer/license/
27 sgo:sdDataset chapters
28 rdf:type schema:Chapter
29 N10cee1c099334a95ad72642a62c7e97c schema:location Berlin, Heidelberg
30 schema:name Springer Berlin Heidelberg
31 rdf:type schema:Organisation
32 N36d2641db93141b1a492c2db4d143f86 rdf:first Nd4d3a38c39dc4655bc154c2808f07cad
33 rdf:rest N97f96abb1b1b411e8033574493cd5363
34 N3e4a3ce7716345238e3a2f12e5b8f9ca schema:name doi
35 schema:value 10.1007/978-3-642-29892-9_2
36 rdf:type schema:PropertyValue
37 N748af7f710da471fac18b2195f9e71e0 schema:isbn 978-3-642-29891-2
38 978-3-642-29892-9
39 schema:name Formal Concept Analysis
40 rdf:type schema:Book
41 N7a27fa497cb447eeb57d3a776acfe59f schema:name readcube_id
42 schema:value 2cf16ced222cb76f9cdbb0f20d0b756af6784d31942d0a25badc9443cc1dbd65
43 rdf:type schema:PropertyValue
44 N826c602cfc354a75ad7014164ba9023a schema:name dimensions_id
45 schema:value pub.1009434467
46 rdf:type schema:PropertyValue
47 N97f96abb1b1b411e8033574493cd5363 rdf:first Nf01213b4afdc4b96bb516b9e3c417e5f
48 rdf:rest Nbaef7a9036894e1082fdbd06fcf35ac9
49 Naa04fc51e3e1439aac1dd1b65a0a98fe rdf:first sg:person.015333627665.77
50 rdf:rest rdf:nil
51 Nbaef7a9036894e1082fdbd06fcf35ac9 rdf:first Neadccce0c2bf4e6f8f3b00d9f89dd3c3
52 rdf:rest rdf:nil
53 Nd4d3a38c39dc4655bc154c2808f07cad schema:familyName Domenach
54 schema:givenName Florent
55 rdf:type schema:Person
56 Ndf5332ef900440fd8431bf4e99f56a5d schema:name Springer Nature - SN SciGraph project
57 rdf:type schema:Organization
58 Neadccce0c2bf4e6f8f3b00d9f89dd3c3 schema:familyName Poelmans
59 schema:givenName Jonas
60 rdf:type schema:Person
61 Nf01213b4afdc4b96bb516b9e3c417e5f schema:familyName Ignatov
62 schema:givenName Dmitry I.
63 rdf:type schema:Person
64 anzsrc-for:08 schema:inDefinedTermSet anzsrc-for:
65 schema:name Information and Computing Sciences
66 rdf:type schema:DefinedTerm
67 anzsrc-for:0801 schema:inDefinedTermSet anzsrc-for:
68 schema:name Artificial Intelligence and Image Processing
69 rdf:type schema:DefinedTerm
70 sg:person.015333627665.77 schema:affiliation https://www.grid.ac/institutes/grid.5596.f
71 schema:familyName De Raedt
72 schema:givenName Luc
73 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.015333627665.77
74 rdf:type schema:Person
75 sg:pub.10.1007/978-3-642-21916-0_3 schema:sameAs https://app.dimensions.ai/details/publication/pub.1002233268
76 https://doi.org/10.1007/978-3-642-21916-0_3
77 rdf:type schema:CreativeWork
78 https://doi.org/10.1016/j.artint.2011.05.002 schema:sameAs https://app.dimensions.ai/details/publication/pub.1013588077
79 rdf:type schema:CreativeWork
80 https://www.grid.ac/institutes/grid.5596.f schema:alternateName KU Leuven
81 schema:name Department of Computer Science, Katholieke Universiteit Leuven, Celestijnenlaan 200A, 3001 Heverlee, Belgium
82 rdf:type schema:Organization
 




Preview window. Press ESC to close (or click here)


...