mycoreobjectrosdok_document_0000007968rosdok_document_0000007968rosdokdocumentrosdok_derivate_0000029083rosdok_derivate_00000290842023-10-17T09:41:13.289Z2015-08-14T13:45:23.517ZeditorRSapipublished2rosdok_derivate_0000029083rosdok_derivate_0000029084truetruederivate_types:documentationDokumentationdocumentationwf_edit_data wf_register_dataderivate_types:dataDatendatawf_edit_data wf_register_datastate:publishedstate:publishedveröffentlichtpublishedrosdok/id000001171677718749Oau2019-09-252023-08-05T15:20:43ZrdaConverted from PICA to MODS using Pica2Mods XSLT Transformer 2.7 [SCM: "0c0e7a3c226a4a0cbcbec39b493c3c5257339ab8" "v2.7" "2023-08-04T00:00:00+0200"] with mode 'DEFAULT'.DatenpublikationForschungsdatenTime series from textual instructions for causal relations discovery (Causal relations dataset)[research data]One aspect of ontology learning methods is the discovery of relations in textual data. One kind of such relations are causal relations. Our aim is to discover causations described in texts such as recipes and manuals. There is a lot of research on causal relations discovery that is based on grammatical patterns. These patterns are, however, rarely discovered in textual instructions (such as recipes) with short and simple sentence structure. Therefore we use time series to discover causal relations. To do that, each word of interest in the text is converted into time series that represent how often and in which time stamp this word appears in the text. Then a time series analysis can be applied to discover causal relations.KristinaYordanova1985 -VerfasserInaut10568927730000-0002-6428-1062University of Rostock, Institute of Computer Science, Mobile Multimedia Information Systems Grouphttp://purl.uni-rostock.de/rosdok/id00000117urn:nbn:de:gbv:28-rosdok_id00000117-810.18453/rosdok_id00000117004 InformatikFakultät für Informatik und ElektrotechnikCC BY 4.0Nutzungsrechte erteiltLizenz Metadaten: CC0frei zugänglich (Open Access)en2015University of RostockRostockmonographic20152015University Library of RostockRostock2015Universitätsbibliothek Rostockhttp://purl.uni-rostock.de/rosdok/id00000117[{"name":"Yordanova, Kristina","affil":"University of Rostock, Institute of Computer Science, Mobile Multimedia Information Systems Group","orcid":"0000-0002-6428-1062"}]Kristina YordanovaFrank Krüger, Martin Nyolt, Kristina Yordanova, Albert Hein, and Thomas Kirste. Computational state space models for activity and intention recognition. a feasibility study. PLoS ONE, 9(11):e109381, 11 2014.https://doi.org/10.1371/journal.pone.0109381Forschungsdaten zuKristina Yordanova. Discovering causal relations in textual instructions. In Recent Advances in Natural Language Processing, Hissar, Bulgaria, September 2015.Forschungsdaten zuYordanova, KristinaUniversity of Rostock, Institute of Computer Science, Mobile Multimedia Information Systems Group0000-0002-6428-1062doctype:epubdoctype:epubDokumenttypDocument typedoctype:datadoctype:dataDatenpublikationdata publicationdiniPublType:ResearchData diniPublType2022:ResearchDatainfo:eu-repo/semantics/otherdocumentnatureOfContent:ppn_857755366natureOfContent:ppn_857755366ForschungsdatendiniPublType2022:ResearchDatadiniPublType2022:ResearchDataForschungsdatenResearch dataKDSFSDNB:004SDNB:004004 Informatik004 Data processing Computer sciencesinstitution:unirostockinstitution:unirostockUniversität RostockUniversity of RostockUniversität RostockUniversität RostockUni.Rostockhttp://d-nb.info/gnd/38329-6institution:unirostock.iefinstitution:unirostock.iefFakultät für Informatik und ElektrotechnikFaculty of Computer Science and Electrical EngineeringUniversität Rostock. Fakultät für Informatik und ElektrotechnikFakultät für Informatik<br />und ElektrotechnikUni.Rostock.Fakultaet.IEFhttp://d-nb.info/gnd/10085032-7licenseinfo:worklicenseinfo:workWerkworklicenseinfo:work.cclicenselicenseinfo:work.cclicenseCC-LizenzCC-licenselicenseinfo:work.cclicense.cc-bylicenseinfo:work.cclicense.cc-byCC BYCC BYlicenseinfo:work.cclicense.cc-by.v40licenseinfo:work.cclicense.cc-by.v40CC BY 4.0CC BY 4.0/creativecommons/l/by/4.0/88x31.png[DE-28]Namensnennung 4.0 International$cCC BY 4.0$gCreative Commons$uhttps://creativecommons.org/licenses/by/4.0/https://creativecommons.org/licenses/by/4.0/https://creativecommons.org/licenses/by/4.0/licenseinfo:depositlicenseinfo:depositVeröffentlichungsgenehmigungpermission to storelicenseinfo:deposit.rightsgrantedlicenseinfo:deposit.rightsgrantedNutzungsrechte erteiltrights grantedlicenseinfo:metadatalicenseinfo:metadataLizenzen für Metadatenlicenseinfo:metadata.cc0licenseinfo:metadata.cc0Lizenz Metadaten: CC0license metadata: CC0/creativecommons/p/zero/1.0/88x31.pnghttps://creativecommons.org/publicdomain/zero/1.0/accesscondition:openaccessaccesscondition:openaccessfrei zugänglich (Open Access)open accesshttp://purl.org/coar/access_right/c_abf2OAfreeinfo:eu-repo/semantics/openAccess[DE-28]Open Access$gControlled Vocabulary for Access Rights$uhttp://purl.org/coar/access_right/c_abf2rfc5646:enrfc5646:enEnglischEnglishengengTime series from textual instructions for causal relations discovery (Causal relations dataset)[research data]Time series from textual instructions for causal relations discovery (Causal relations dataset)[research data]gnd:1056892773orcid:0000-0002-6428-1062gnd:1056892773orcid:0000-0002-6428-1062rosdok_document_0000007968-d510624e65gnd:1056892773orcid:0000-0002-6428-1062Kristina YordanovaKristina YordanovaKristina YordanovaKristina YordanovaKristina YordanovaRostockUniversity of Rostockdatahttp://purl.uni-rostock.de/rosdok/id00000117urn:nbn:de:gbv:28-rosdok_id00000117-810.18453/rosdok_id00000117One aspect of ontology learning methods is the discovery of relations in textual data. One kind of such relations are causal relations. Our aim is to discover causations described in texts such as recipes and manuals. There is a lot of research on causal relations discovery that is based on grammatical patterns. These patterns are, however, rarely discovered in textual instructions (such as recipes) with short and simple sentence structure. Therefore we use time series to discover causal relations. To do that, each word of interest in the text is converted into time series that represent how often and in which time stamp this word appears in the text. Then a time series analysis can be applied to discover causal relations.20152015Frank Krüger, Martin Nyolt, Kristina Yordanova, Albert Hein, and Thomas Kirste. Computational state space models for activity and intention recognition. a feasibility study. PLoS ONE, 9(11):e109381, 11 2014.Frank Krüger, Martin Nyolt, Kristina Yordanova, Albert Hein, and Thomas Kirste. Computational state space models for activity and intention recognition. a feasibility study. PLoS ONE, 9(11):e109381, 11 2014.https://doi.org/10.1371/journal.pone.0109381Forschungsdaten zuhttps://doi.org/10.1371/journal.pone.0109381Kristina Yordanova. Discovering causal relations in textual instructions. In Recent Advances in Natural Language Processing, Hissar, Bulgaria, September 2015.Kristina Yordanova. Discovering causal relations in textual instructions. In Recent Advances in Natural Language Processing, Hissar, Bulgaria, September 2015.Forschungsdaten zu[{"name":"Yordanova, Kristina","affil":"University of Rostock, Institute of Computer Science, Mobile Multimedia Information Systems Group","orcid":"0000-0002-6428-1062"}]Kristina Yordanovadata1 documentation.pdf 180705 b4818d59e4a6738c4f8ffc87601bcb2c 2 D20150330-Causal_Relations-KY.zip 178852 42a686e6e3e683894a4227c954787e03 rosdok/id000001171677718749Oau2019-09-252023-08-05T15:20:43ZrdaConverted from PICA to MODS using Pica2Mods XSLT Transformer 2.7 [SCM: "0c0e7a3c226a4a0cbcbec39b493c3c5257339ab8" "v2.7" "2023-08-04T00:00:00+0200"] with mode 'DEFAULT'.DatenpublikationForschungsdatenTime series from textual instructions for causal relations discovery (Causal relations dataset)[research data]One aspect of ontology learning methods is the discovery of relations in textual data. One kind of such relations are causal relations. Our aim is to discover causations described in texts such as recipes and manuals. There is a lot of research on causal relations discovery that is based on grammatical patterns. These patterns are, however, rarely discovered in textual instructions (such as recipes) with short and simple sentence structure. Therefore we use time series to discover causal relations. To do that, each word of interest in the text is converted into time series that represent how often and in which time stamp this word appears in the text. Then a time series analysis can be applied to discover causal relations.KristinaYordanova1985 -VerfasserInaut10568927730000-0002-6428-1062University of Rostock, Institute of Computer Science, Mobile Multimedia Information Systems Grouphttp://purl.uni-rostock.de/rosdok/id00000117urn:nbn:de:gbv:28-rosdok_id00000117-810.18453/rosdok_id00000117004 InformatikFakultät für Informatik und ElektrotechnikCC BY 4.0Nutzungsrechte erteiltLizenz Metadaten: CC0frei zugänglich (Open Access)en2015University of RostockRostockmonographic20152015University Library of RostockRostock2015Universitätsbibliothek Rostockhttp://purl.uni-rostock.de/rosdok/id00000117[{"name":"Yordanova, Kristina","affil":"University of Rostock, Institute of Computer Science, Mobile Multimedia Information Systems Group","orcid":"0000-0002-6428-1062"}]Kristina YordanovaFrank Krüger, Martin Nyolt, Kristina Yordanova, Albert Hein, and Thomas Kirste. Computational state space models for activity and intention recognition. a feasibility study. PLoS ONE, 9(11):e109381, 11 2014.https://doi.org/10.1371/journal.pone.0109381Forschungsdaten zuKristina Yordanova. Discovering causal relations in textual instructions. In Recent Advances in Natural Language Processing, Hissar, Bulgaria, September 2015.Forschungsdaten zu Yordanova, Kristina University of Rostock, Institute of Computer Science, Mobile Multimedia Information Systems Group 0000-0002-6428-1062 2015-08-14T13:45:23.517Z 2023-10-17T09:41:13.289Z 2023-10-27T09:41:13.297Z api {"identifier":"rosdok/id00000117","type":"local_id","additional":"","service":"MCRLocalID","created":"2018-06-30T20:09:52.190Z"} {"identifier":"http://purl.uni-rostock.de/rosdok/id00000117","type":"purl","additional":"","service":"RosDokPURL","created":"2018-06-30T20:09:52.279Z","registered":"2018-06-30T20:09:52.279Z"} {"identifier":"10.18453/rosdok_id00000117","type":"doi","additional":"","service":"RosDokDOI","created":"2021-08-01T15:24:34.553Z","registrationStarted":"2021-08-01T15:24:34.551Z","registered":"2021-08-10T17:02:47.041Z"} editorRSdocumentationdataTime series from textual instructions for causal relations discovery (Causal relations dataset)[research data]Time series from textual instructions for causal relations discovery (Causal relations dataset)[research data]gnd:1056892773orcid:0000-0002-6428-1062gnd:1056892773orcid:0000-0002-6428-1062rosdok_document_0000007968-d510624e65gnd:1056892773orcid:0000-0002-6428-1062Kristina YordanovaKristina YordanovaKristina YordanovaKristina YordanovaKristina YordanovaRostockUniversity of Rostockdatahttp://purl.uni-rostock.de/rosdok/id00000117urn:nbn:de:gbv:28-rosdok_id00000117-810.18453/rosdok_id00000117One aspect of ontology learning methods is the discovery of relations in textual data. One kind of such relations are causal relations. Our aim is to discover causations described in texts such as recipes and manuals. There is a lot of research on causal relations discovery that is based on grammatical patterns. These patterns are, however, rarely discovered in textual instructions (such as recipes) with short and simple sentence structure. Therefore we use time series to discover causal relations. To do that, each word of interest in the text is converted into time series that represent how often and in which time stamp this word appears in the text. Then a time series analysis can be applied to discover causal relations.20152015Frank Krüger, Martin Nyolt, Kristina Yordanova, Albert Hein, and Thomas Kirste. Computational state space models for activity and intention recognition. a feasibility study. PLoS ONE, 9(11):e109381, 11 2014.Frank Krüger, Martin Nyolt, Kristina Yordanova, Albert Hein, and Thomas Kirste. Computational state space models for activity and intention recognition. a feasibility study. PLoS ONE, 9(11):e109381, 11 2014.https://doi.org/10.1371/journal.pone.0109381Forschungsdaten zuhttps://doi.org/10.1371/journal.pone.0109381Kristina Yordanova. Discovering causal relations in textual instructions. In Recent Advances in Natural Language Processing, Hissar, Bulgaria, September 2015.Kristina Yordanova. Discovering causal relations in textual instructions. In Recent Advances in Natural Language Processing, Hissar, Bulgaria, September 2015.Forschungsdaten zu[{"name":"Yordanova, Kristina","affil":"University of Rostock, Institute of Computer Science, Mobile Multimedia Information Systems Group","orcid":"0000-0002-6428-1062"}]Kristina Yordanova[xslt]Saxonrosdok/id00000117https://purl.uni-rostock.de/rosdok/id00000117167771874910.18453/rosdok_id00000117urn:nbn:de:gbv:28-rosdok_id00000117-8Kristina YordanovaYordanova KristinaTime series from textual instructions for causal relations discovery (Causal relations dataset) : [research data]Datenpublikationdata publicationUniversity of Rostock, 2015One aspect of ontology learning methods is the discovery of relations in textual data. One kind of such relations are causal relations. Our aim is to discover causations described in texts such as recipes and manuals. There is a lot of research on causal relations discovery that is based on…Kristina YordanovaTime series from textual instructions for causal relations discovery (Causal relations dataset)[research data]Frank Krüger, Martin Nyolt, Kristina Yordanova, Albert Hein, and Thomas Kirste. Computational state space models for activity and intention recognition. a feasibility study. PLoS ONE, 9(11):e109381, 11 2014.Kristina Yordanova. Discovering causal relations in textual instructions. In Recent Advances in Natural Language Processing, Hissar, Bulgaria, September 2015.Universitätsbibliothek Rostockhttp://purl.uni-rostock.de/rosdok/id00000117KristinaYordanova1985 -VerfasserInaut10568927730000-0002-6428-1062University of Rostock, Institute of Computer Science, Mobile Multimedia Information Systems Group[purl]http://purl.uni-rostock.de/rosdok/id00000117[urn]urn:nbn:de:gbv:28-rosdok_id00000117-8[doi]10.18453/rosdok_id00000117open_access20152015epoch:21th_centuryrfc5646:endoctype:dataaccesscondition:openaccessSDNB:004institution:unirostock.iefstate:published