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  "author": "Stanley Kok",
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  "author": "Stanley Kok",
            
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  "author_email": "koks@cs.washington.edu",
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  "author_email": "koks@cs.washington.edu",
            
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  "defined_in": "https://doi.org/10.1145/1273496.1273551",
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  "defined_in": "https://doi.org/10.1145/1273496.1273551",
            
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  "doi": "10.57702/vu5ceumt",
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  "doi": "10.57702/vu5ceumt",
            
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  "doi_date_published": "2024-04-18",
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  "doi_date_published": "2024-04-18",
            
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  "extra_authors": [
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  "extra_authors": [
            
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    {
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    {
            
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      "extra_author": "Pedro Domingos",
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      "extra_author": "Pedro Domingos",
            
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      "orcid": ""
            
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    }
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  "license_title": "License not specified",
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  "license_title": "License not specified",
            
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  "link_orkg": "https://www.orkg.org/orkg/paper/R705681",
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  "link_orkg": "https://www.orkg.org/orkg/paper/R705681",
            
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  "name": "the-family-kg",
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  "name": "the-family-kg",
            
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  "notes": "Statistical predicate invention is considered a key 
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  "notes": "Statistical predicate invention is considered a key 
            
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problem in statistical relational learning. SPI involves discovering 
47
problem in statistical relational learning. SPI involves discovering 
            
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new concepts, properties, and relations within structured data, 
48
new concepts, properties, and relations within structured data, 
            
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extending beyond mere discovery of hidden variables within statistical 
49
extending beyond mere discovery of hidden variables within statistical 
            
50
models and predicate invention within ILP. An initial model for SPI is 
50
models and predicate invention within ILP. An initial model for SPI is 
            
51
proposed, based on second-order Markov logic, wherein predicates as 
51
proposed, based on second-order Markov logic, wherein predicates as 
            
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well as arguments can be variables, and the domain of discourse is not 
52
well as arguments can be variables, and the domain of discourse is not 
            
53
fully known in advance. The approach iteratively refines clusters of 
53
fully known in advance. The approach iteratively refines clusters of 
            
54
symbols based on the clusters of symbols they appear in atoms with 
54
symbols based on the clusters of symbols they appear in atoms with 
            
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(e.g., it clusters relations by the clusters of the objects they 
55
(e.g., it clusters relations by the clusters of the objects they 
            
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relate). Since different clusterings are better for predicting 
56
relate). Since different clusterings are better for predicting 
            
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different subsets of the atoms, multiple cross-cutting clusterings are 
57
different subsets of the atoms, multiple cross-cutting clusterings are 
            
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allowed. This approach is shown to outperform Markov logic structure 
58
allowed. This approach is shown to outperform Markov logic structure 
            
59
learning and the recently introduced infinite relational model on a 
59
learning and the recently introduced infinite relational model on a 
            
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number of relational datasets.",
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number of relational datasets.",
            
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    "created": "2017-11-23T17:30:37.757128",
            
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    "description": "The German National Library of Science and 
            
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Technology, abbreviated TIB, is the national library of the Federal 
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Technology, abbreviated TIB, is the national library of the Federal 
            
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Republic of Germany for all fields of engineering, technology, and the 
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Republic of Germany for all fields of engineering, technology, and the 
            
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natural sciences.",
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natural sciences.",
            
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3conf/ext/tib_tmpl_bootstrap/Resources/Public/images/TIB_Logo_en.png",
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  "tags": [
            
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      "display_name": "Benchmark",
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      "display_name": "Benchmark",
            
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      "id": "70474eb4-f8bf-42f1-bf26-7511d4f3356c",
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  "title": "The Family KG",
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  "title": "The Family KG",
            
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  "url": "https://dl.acm.org/doi/pdf/10.1145/1273496.1273551",
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