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  "access_rights": "",
            
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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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  "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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  "doi_publisher": "TIB",
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  "doi_publisher": "TIB",
            
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  "domain": "https://service.tib.eu/ldmservice",
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  "domain": "https://service.tib.eu/ldmservice",
            
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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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      "orcid": ""
            
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    }
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    }
            
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  "extras": [
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    {
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  "license_id": "notspecified",
            
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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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  "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 
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problem in statistical relational learning. SPI involves discovering 
            
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new concepts, properties, and relations within structured data, 
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new concepts, properties, and relations within structured data, 
            
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extending beyond mere discovery of hidden variables within statistical 
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extending beyond mere discovery of hidden variables within statistical 
            
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models and predicate invention within ILP. An initial model for SPI is 
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models and predicate invention within ILP. An initial model for SPI is 
            
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proposed, based on second-order Markov logic, wherein predicates as 
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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 
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well as arguments can be variables, and the domain of discourse is not 
            
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fully known in advance. The approach iteratively refines clusters of 
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fully known in advance. The approach iteratively refines clusters of 
            
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symbols based on the clusters of symbols they appear in atoms with 
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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 
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(e.g., it clusters relations by the clusters of the objects they 
            
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relate). Since different clusterings are better for predicting 
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relate). Since different clusterings are better for predicting 
            
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different subsets of the atoms, multiple cross-cutting clusterings are 
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different subsets of the atoms, multiple cross-cutting clusterings are 
            
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allowed. This approach is shown to outperform Markov logic structure 
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allowed. This approach is shown to outperform Markov logic structure 
            
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learning and the recently introduced infinite relational model on a 
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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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    "approval_status": "approved",
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    "created": "2017-11-23T17:30:37.757128",
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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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    "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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  "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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      "id": "70474eb4-f8bf-42f1-bf26-7511d4f3356c",
            
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  "temporal_resolution": "",
            
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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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