Changes

On January 12, 2023 at 1:14:21 PM UTC, admin:

Set author of NLPContributionGraph Trial Dataset to Jennifer D’Souza, Soeren Auer (previously Jennifer D’Souza; Soeren Auer)

Updated description of NLPContributionGraph Trial Dataset from
##An Annotation Scheme for Machine Reading of Scholarly Contributions in Natural Language Processing Literature This dataset is the result of a pilot annotation exercise to capture the scholarly contributions in natural language processing (NLP) articles, particularly, for the articles that discuss machine learning (ML) approaches for various information extraction tasks. The pilot annotation exercise was performed on 50 NLP-ML scholarly articles presenting contributions to the five information extraction tasks 1. machine translation, 2. named entity recognition, 3. question answering, 4. relation classification, and 5. text classification. The outcome of this pilot annotation exercise was two-fold: 1) a preliminary annotation methodology, and 2) the dataset released in this repository. The resulting annotation scheme is called NLPContributions. ###Supporting Publications D’Souza, J., & Auer, S. (2020). NLPContributions: An Annotation Scheme for Machine Reading of Scholarly Contributions in Natural Language Processing Literature. In C. Zhang, P. Mayr, W. Lu, & Y. Zhang (Eds.), Proceedings of the 1st Workshop on Extraction and Evaluation of Knowledge Entities from Scientific Documents co-located with the ACM/IEEE Joint Conference on Digital Libraries in 2020, EEKE@JCDL 2020, Virtual Event, China, August 1st, 2020 (Vol. 2658, pp. 16–27).
to
##An Annotation Scheme for Machine Reading of Scholarly Contributions in Natural Language Processing Literature This dataset is the result of a pilot annotation exercise to capture the scholarly contributions in natural language processing (NLP) articles, particularly, for the articles that discuss machine learning (ML) approaches for various information extraction tasks. The pilot annotation exercise was performed on 50 NLP-ML scholarly articles presenting contributions to the five information extraction tasks 1. machine translation, 2. named entity recognition, 3. question answering, 4. relation classification, and 5. text classification. The outcome of this pilot annotation exercise was two-fold: 1) a preliminary annotation methodology, and 2) the dataset released in this repository. The resulting annotation scheme is called NLPContributions. ###Supporting Publications D’Souza, J., & Auer, S. (2020). NLPContributions: An Annotation Scheme for Machine Reading of Scholarly Contributions in Natural Language Processing Literature. In C. Zhang, P. Mayr, W. Lu, & Y. Zhang (Eds.), Proceedings of the 1st Workshop on Extraction and Evaluation of Knowledge Entities from Scientific Documents co-located with the ACM/IEEE Joint Conference on Digital Libraries in 2020, EEKE@JCDL 2020, Virtual Event, China, August 1st, 2020 (Vol. 2658, pp. 16–27). D'Souza, Jennifer, and Sören Auer. "Sentence, Phrase, and Triple Annotations to Build a Knowledge Graph of Natural Language Processing Contributions—A Trial Dataset." Journal of Data and Information Science, vol.6, no.3, 2021, pp.6-34. DOI: 10.2478/jdis-2021-0023

Changed value of field source_metadata_modified to 2022-02-21T13:09:17.617156 in NLPContributionGraph Trial Dataset

Deleted resource All resource data from NLPContributionGraph Trial Dataset

Removed field datastore_active from resource Trial data from the NLPContributionGraph scheme in NLPContributionGraph Trial Dataset

              
    
          
          
        
        
            f 1 { f 1 {
            n 2   "author": "Jennifer D\u2019Souza; Soeren Auer", n 2   "author": "Jennifer D\u2019Souza, Soeren Auer",
            3   "author_email": "jennifer.dsouza@tib.eu", 3   "author_email": "jennifer.dsouza@tib.eu",
            4   "creator_user_id": "17755db4-395a-4b3b-ac09-e8e3484ca700", 4   "creator_user_id": "17755db4-395a-4b3b-ac09-e8e3484ca700",
            5   "doi": "10.25835/0019761", 5   "doi": "10.25835/0019761",
            6   "doi_date_published": "2020-07-03", 6   "doi_date_published": "2020-07-03",
            7   "doi_publisher": "LUIS", 7   "doi_publisher": "LUIS",
            8   "doi_status": "true", 8   "doi_status": "true",
            9   "domain": "https://data.uni-hannover.de", 9   "domain": "https://data.uni-hannover.de",
            10   "groups": [], 10   "groups": [],
            11   "have_copyright": "Yes", 11   "have_copyright": "Yes",
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            15   "license_title": "CC-BY-SA-3.0", 15   "license_title": "CC-BY-SA-3.0",
            16   "maintainer": "Jennifer D'Souza", 16   "maintainer": "Jennifer D'Souza",
            17   "maintainer_email": "jennifer.dsouza@tib.eu", 17   "maintainer_email": "jennifer.dsouza@tib.eu",
            18   "metadata_created": "2021-10-14T10:16:03.354056", 18   "metadata_created": "2021-10-14T10:16:03.354056",
            n 19   "metadata_modified": "2021-10-14T10:16:03.354061", n 19   "metadata_modified": "2023-01-12T13:14:21.766915",
            20   "name": "luh-nlpcontributions-pilot-dataset", 20   "name": "luh-nlpcontributions-pilot-dataset",
            21   "notes": "##An Annotation Scheme for Machine Reading of Scholarly  21   "notes": "##An Annotation Scheme for Machine Reading of Scholarly 
            22 Contributions in Natural Language Processing Literature\r\n\r\nThis  22 Contributions in Natural Language Processing Literature\r\n\r\nThis 
            23 dataset is the result of a pilot annotation exercise to capture the  23 dataset is the result of a pilot annotation exercise to capture the 
            24 scholarly contributions in natural language processing (NLP) articles,  24 scholarly contributions in natural language processing (NLP) articles, 
            25 particularly, for the articles that discuss machine learning (ML)  25 particularly, for the articles that discuss machine learning (ML) 
            26 approaches for various information extraction tasks. The pilot  26 approaches for various information extraction tasks. The pilot 
            27 annotation exercise was performed on 50 NLP-ML scholarly articles  27 annotation exercise was performed on 50 NLP-ML scholarly articles 
            28 presenting contributions to the five information extraction tasks 1.  28 presenting contributions to the five information extraction tasks 1. 
            29 machine translation, 2. named entity recognition, 3. question  29 machine translation, 2. named entity recognition, 3. question 
            30 answering, 4. relation classification, and 5. text classification.  30 answering, 4. relation classification, and 5. text classification. 
            31 \r\n\r\nThe outcome of this pilot annotation exercise was two-fold: 1)  31 \r\n\r\nThe outcome of this pilot annotation exercise was two-fold: 1) 
            32 a preliminary annotation methodology, and 2) the dataset released in  32 a preliminary annotation methodology, and 2) the dataset released in 
            33 this repository.\r\n\r\nThe resulting annotation scheme is called  33 this repository.\r\n\r\nThe resulting annotation scheme is called 
            34 <b><i>NLPContributions</i></b>.\r\n\r\n###Supporting  34 <b><i>NLPContributions</i></b>.\r\n\r\n###Supporting 
            35 Publications\r\nD\u2019Souza, J., & Auer, S. (2020). NLPContributions:  35 Publications\r\nD\u2019Souza, J., & Auer, S. (2020). NLPContributions: 
            36 An Annotation Scheme for Machine Reading of Scholarly Contributions in  36 An Annotation Scheme for Machine Reading of Scholarly Contributions in 
            37 Natural Language Processing Literature. In C. Zhang, P. Mayr, W. Lu, &  37 Natural Language Processing Literature. In C. Zhang, P. Mayr, W. Lu, & 
            38 Y. Zhang (Eds.), Proceedings of the 1st Workshop on Extraction and  38 Y. Zhang (Eds.), Proceedings of the 1st Workshop on Extraction and 
            39 Evaluation of Knowledge Entities from Scientific Documents co-located  39 Evaluation of Knowledge Entities from Scientific Documents co-located 
            40 with the ACM/IEEE Joint Conference on Digital Libraries in 2020,  40 with the ACM/IEEE Joint Conference on Digital Libraries in 2020, 
            41 EEKE@JCDL 2020, Virtual Event, China, August 1st, 2020 (Vol. 2658, pp.  41 EEKE@JCDL 2020, Virtual Event, China, August 1st, 2020 (Vol. 2658, pp. 
            n 42 16\u201327). \r\n", n 42 16\u201327). \r\n\r\nD'Souza, Jennifer, and S\u00f6ren Auer. 
            43 \"Sentence, Phrase, and Triple Annotations to Build a Knowledge Graph 
            44 of Natural Language Processing Contributions\u2014A Trial Dataset.\" 
            45 Journal of Data and Information Science, vol.6, no.3, 2021, pp.6-34. 
            46 DOI: 10.2478/jdis-2021-0023 ",
            43   "num_resources": 2, 47   "num_resources": 1,
            44   "num_tags": 8, 48   "num_tags": 8,
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            46     "approval_status": "approved", 50     "approval_status": "approved",
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            48     "description": "The German National Library of Science and  52     "description": "The German National Library of Science and 
            49 Technology, abbreviated TIB, is the national library of the Federal  53 Technology, abbreviated TIB, is the national library of the Federal 
            50 Republic of Germany for all fields of engineering, technology, and the  54 Republic of Germany for all fields of engineering, technology, and the 
            51 natural sciences.", 55 natural sciences.",
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            54 3conf/ext/tib_tmpl_bootstrap/Resources/Public/images/TIB_Logo_en.png", 58 3conf/ext/tib_tmpl_bootstrap/Resources/Public/images/TIB_Logo_en.png",
            55     "is_organization": true, 59     "is_organization": true,
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            57     "state": "active", 61     "state": "active",
            58     "title": "TIB", 62     "title": "TIB",
            59     "type": "organization" 63     "type": "organization"
            60   }, 64   },
            61   "owner_org": "0c5362f5-b99e-41db-8256-3d0d7549bf4d", 65   "owner_org": "0c5362f5-b99e-41db-8256-3d0d7549bf4d",
            62   "private": false, 66   "private": false,
            63   "relationships_as_object": [], 67   "relationships_as_object": [],
            64   "relationships_as_subject": [], 68   "relationships_as_subject": [],
            65   "repository_name": "Leibniz University Hannover", 69   "repository_name": "Leibniz University Hannover",
            66   "resources": [ 70   "resources": [
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            81       "mimetype": "application/zip", 
            82       "mimetype_inner": null, 
            83       "name": "All resource data", 
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            90       "url":  
            91 9f0d-e6c31d367555/download/nlpcontributions-pilot-dataset-jh70ne.zip", 
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            93     }, 
            94     { 
            95       "cache_last_updated": null, 
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            97       "created": "2020-07-03T12:32:07.815445", 74       "created": "2020-07-03T12:32:07.815445",
            n 98       "datastore_active": false, n 
            99       "description": "Research contributions in NLP annotated as  75       "description": "Research contributions in NLP annotated as 
            100 structured data using the NLPContributionGraph scheme for structuring  76 structured data using the NLPContributionGraph scheme for structuring 
            101 scholarly contributions in the  77 scholarly contributions in the 
            102 [ORKG](https://www.orkg.org/orkg/).\r\n\r\nThe repository is organized  78 [ORKG](https://www.orkg.org/orkg/).\r\n\r\nThe repository is organized 
            103 as follows:\r\n\r\n    [task-name-folder]/                                 79 as follows:\r\n\r\n    [task-name-folder]/                                
            104 # machine-translation, named-entity-recognition, question-answering,  80 # machine-translation, named-entity-recognition, question-answering, 
            105 relation-classification, text-classification\r\n         81 relation-classification, text-classification\r\n        
            106 \u251c\u2500\u2500 [article-counter-folder]/                  # ranges  82 \u251c\u2500\u2500 [article-counter-folder]/                  # ranges 
            107 from 0 to 9 since we annotated 10 articles per task\r\n        \u2502    83 from 0 to 9 since we annotated 10 articles per task\r\n        \u2502   
            108 \u2514\u2500\u2500 research-problem.json              # `research  84 \u2514\u2500\u2500 research-problem.json              # `research 
            109 problem` mandatory information unit in json format\r\n        \u2502    85 problem` mandatory information unit in json format\r\n        \u2502   
            110 \u2514\u2500\u2500 model.json                         # `model`  86 \u2514\u2500\u2500 model.json                         # `model` 
            111 information unit in json format; in some articles it is called  87 information unit in json format; in some articles it is called 
            112 `approach`\r\n        \u2502   \u2514\u2500\u2500 ...                                 88 `approach`\r\n        \u2502   \u2514\u2500\u2500 ...                                
            113 # there are 8 main information units in all and each article may be  89 # there are 8 main information units in all and each article may be 
            114 annotated by 3 or 6\r\n        \u2502   \u2514\u2500\u2500 triples/                                90 annotated by 3 or 6\r\n        \u2502   \u2514\u2500\u2500 triples/                               
            115 # the folder containing information unit triples one per line\r\n         91 # the folder containing information unit triples one per line\r\n        
            116 \u2502   \u2502   \u2514\u2500\u2500 research-problem.txt                92 \u2502   \u2502   \u2514\u2500\u2500 research-problem.txt               
            117 # `research problem` triples (one research problem statement per  93 # `research problem` triples (one research problem statement per 
            118 line)\r\n        \u2502   \u2502   \u2514\u2500\u2500 model.txt                           94 line)\r\n        \u2502   \u2502   \u2514\u2500\u2500 model.txt                          
            119 # `model` triples (one statement per line)\r\n        \u2502   \u2502    95 # `model` triples (one statement per line)\r\n        \u2502   \u2502   
            120 \u2514\u2500\u2500 ...                                # there are 8  96 \u2514\u2500\u2500 ...                                # there are 8 
            121 main information units in all and each article may be annotated by 3  97 main information units in all and each article may be annotated by 3 
            122 or 6\r\n        \u2502   \u2514\u2500\u2500 ...                                     98 or 6\r\n        \u2502   \u2514\u2500\u2500 ...                                    
            123 # there are ten articles annotated for each task, so this repeats nine  99 # there are ten articles annotated for each task, so this repeats nine 
            124 more times\r\n        \u2514\u2500\u2500 ...                                         100 more times\r\n        \u2514\u2500\u2500 ...                                        
            125 # there are five tasks selected overall, so this repeats four more  101 # there are five tasks selected overall, so this repeats four more 
            126 times", 102 times",
            127       "format": "JSON", 103       "format": "JSON",
            128       "hash": "", 104       "hash": "",
            129       "id": "29c065cf-7087-49e0-89aa-4901f90a528c", 105       "id": "29c065cf-7087-49e0-89aa-4901f90a528c",
            130       "last_modified": "2020-07-24T12:03:36.054419", 106       "last_modified": "2020-07-24T12:03:36.054419",
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            132       "mimetype": "application/zip", 108       "mimetype": "application/zip",
            133       "mimetype_inner": null, 109       "mimetype_inner": null,
            134       "name": "Trial data from the NLPContributionGraph scheme", 110       "name": "Trial data from the NLPContributionGraph scheme",
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            139       "size": 29426833, 114       "size": 29426833,
            140       "state": "active", 115       "state": "active",
            141       "url": "https://github.com/ncg-task/trial-data", 116       "url": "https://github.com/ncg-task/trial-data",
            142       "url_type": "" 117       "url_type": ""
            143     } 118     }
            144   ], 119   ],
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            149     { 124     {
            150       "display_name": "corpus", 125       "display_name": "corpus",
            151       "id": "95b6b4a3-2816-47b4-a9da-50ab21116c20", 126       "id": "95b6b4a3-2816-47b4-a9da-50ab21116c20",
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            153       "state": "active", 128       "state": "active",
            154       "vocabulary_id": null 129       "vocabulary_id": null
            155     }, 130     },
            156     { 131     {
            157       "display_name": "machine reading", 132       "display_name": "machine reading",
            158       "id": "d34df1b1-5ebe-415e-8b43-80a15ef1e215", 133       "id": "d34df1b1-5ebe-415e-8b43-80a15ef1e215",
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            160       "state": "active", 135       "state": "active",
            161       "vocabulary_id": null 136       "vocabulary_id": null
            162     }, 137     },
            163     { 138     {
            164       "display_name": "natural language processing", 139       "display_name": "natural language processing",
            165       "id": "8af9c93a-1d87-41e0-83d9-f5d01a2bbd0c", 140       "id": "8af9c93a-1d87-41e0-83d9-f5d01a2bbd0c",
            166       "name": "natural language processing", 141       "name": "natural language processing",
            167       "state": "active", 142       "state": "active",
            168       "vocabulary_id": null 143       "vocabulary_id": null
            169     }, 144     },
            170     { 145     {
            171       "display_name": "open research knowledge graph", 146       "display_name": "open research knowledge graph",
            172       "id": "c9fb26fb-f92f-4740-899e-290c1a384971", 147       "id": "c9fb26fb-f92f-4740-899e-290c1a384971",
            173       "name": "open research knowledge graph", 148       "name": "open research knowledge graph",
            174       "state": "active", 149       "state": "active",
            175       "vocabulary_id": null 150       "vocabulary_id": null
            176     }, 151     },
            177     { 152     {
            178       "display_name": "orkg", 153       "display_name": "orkg",
            179       "id": "a029b5df-5c95-4e99-94f3-1d9e2fbf1fd0", 154       "id": "a029b5df-5c95-4e99-94f3-1d9e2fbf1fd0",
            180       "name": "orkg", 155       "name": "orkg",
            181       "state": "active", 156       "state": "active",
            182       "vocabulary_id": null 157       "vocabulary_id": null
            183     }, 158     },
            184     { 159     {
            185       "display_name": "pilot annotation", 160       "display_name": "pilot annotation",
            186       "id": "33d81870-19c7-4aea-9ba6-76c17255b905", 161       "id": "33d81870-19c7-4aea-9ba6-76c17255b905",
            187       "name": "pilot annotation", 162       "name": "pilot annotation",
            188       "state": "active", 163       "state": "active",
            189       "vocabulary_id": null 164       "vocabulary_id": null
            190     }, 165     },
            191     { 166     {
            192       "display_name": "scholarly knowledge graph", 167       "display_name": "scholarly knowledge graph",
            193       "id": "cad5b92d-5dd7-4e88-ab94-cf3dc6cb6594", 168       "id": "cad5b92d-5dd7-4e88-ab94-cf3dc6cb6594",
            194       "name": "scholarly knowledge graph", 169       "name": "scholarly knowledge graph",
            195       "state": "active", 170       "state": "active",
            196       "vocabulary_id": null 171       "vocabulary_id": null
            197     }, 172     },
            198     { 173     {
            199       "display_name": "semantic web", 174       "display_name": "semantic web",
            200       "id": "cc98f198-dc4f-47f2-87b4-9ee665b8dfd8", 175       "id": "cc98f198-dc4f-47f2-87b4-9ee665b8dfd8",
            201       "name": "semantic web", 176       "name": "semantic web",
            202       "state": "active", 177       "state": "active",
            203       "vocabulary_id": null 178       "vocabulary_id": null
            204     } 179     }
            205   ], 180   ],
            206   "terms_of_usage": "Yes", 181   "terms_of_usage": "Yes",
            207   "title": "NLPContributionGraph Trial Dataset", 182   "title": "NLPContributionGraph Trial Dataset",
            208   "type": "vdataset", 183   "type": "vdataset",
            209   "url":  184   "url": 
            210 "https://data.uni-hannover.de/dataset/nlpcontributions-pilot-dataset", 185 "https://data.uni-hannover.de/dataset/nlpcontributions-pilot-dataset",
            211   "version": "2.0" 186   "version": "2.0"
            212 } 187 }