16 research outputs found
GESIS Knowledge Graph (GESIS KG)
No full textThe GESIS Knowledge Graph (GESIS KG) represents metadata of all scientific resources available in the GESIS Search (https://search.gesis.org/) and its semantic relationships in an integrated and consistent form and makes them accessible for integration and reuse. Understanding relations and dependencies between scientific resources is crucial to capture provenance, ensure reproducibility of research and facilitate informed search across resources. Hence, the GESIS KG captures links between different scientific resources, e.g., links between data, publications, survey instruments, survey variables, and links between entities like authors and social science concepts. The GESIS KG is geared towards interoperability and uses established W3C standards and widely accepted vocabularies, such as schema.org, DDI, the NFDIcore Ontology among others to increase interoperability and reusability of data on the Web for both humans and machines, e.g., through APIs. On instance-level, we address interoperability by reusing PIDs from commonly used PID systems, interlinking the GESIS KG with other KG provided by GESIS as well within the NFDI.
Find more information at https://data.gesis.org/gesiskg/
Detailed description of the files can be found in GESISKG_readme.txt
Keywords: knowledge graph, semantic web, scholarly resource metadata, social sciencesThe GESIS Knowledge Graph (GESIS KG) represents metadata of all scientific resources available in the GESIS Search (https://search.gesis.org/) and its semantic relationships in an integrated and consistent form and makes them accessible for integration and reuse. Understanding relations and dependencies between scientific resources is crucial to capture provenance, ensure reproducibility of research and facilitate informed search across resources. Hence, the GESIS KG captures links between different scientific resources, e.g., links between data, publications, survey instruments, survey variables, and links between entities like authors and social science concepts. The GESIS KG is geared towards interoperability and uses established W3C standards and widely accepted vocabularies, such as schema.org, DDI, the NFDIcore Ontology among others to increase interoperability and reusability of data on the Web for both humans and machines, e.g., through APIs. On instance-level, we address interoperability by reusing PIDs from commonly used PID systems, interlinking the GESIS KG with other KG provided by GESIS as well within the NFDI.
Find more information at https://data.gesis.org/gesiskg/
Detailed description of the files can be found in GESISKG_readme.txt
Keywords: knowledge graph, semantic web, scholarly resource metadata, social science
GESIS Knowledge Graph (GESIS KG)
No full textThe GESIS Knowledge Graph (GESIS KG) represents metadata of all scientific resources available in the GESIS Search (https://search.gesis.org/) and its semantic relationships in an integrated and consistent form and makes them accessible for integration and reuse. Understanding relations and dependencies between scientific resources is crucial to capture provenance, ensure reproducibility of research and facilitate informed search across resources. Hence, the GESIS KG captures links between different scientific resources, e.g., links between data, publications, survey instruments, survey variables, and links between entities like authors and social science concepts. The GESIS KG is geared towards interoperability and uses established W3C standards and widely accepted vocabularies, such as schema.org, DDI, the NFDIcore Ontology among others to increase interoperability and reusability of data on the Web for both humans and machines, e.g., through APIs. On instance-level, we address interoperability by reusing PIDs from commonly used PID systems, interlinking the GESIS KG with other KG provided by GESIS as well within the NFDI.
Find more information at Find more information at https://data.gesis.org/gesiskg/
Detailed description of the files can be found in GESISKG_readme.txt
Keywords: knowledge graph, semantic web, scholarly resource metadata, social sciencesThe GESIS Knowledge Graph (GESIS KG) represents metadata of all scientific resources available in the GESIS Search (https://search.gesis.org/) and its semantic relationships in an integrated and consistent form and makes them accessible for integration and reuse. Understanding relations and dependencies between scientific resources is crucial to capture provenance, ensure reproducibility of research and facilitate informed search across resources. Hence, the GESIS KG captures links between different scientific resources, e.g., links between data, publications, survey instruments, survey variables, and links between entities like authors and social science concepts. The GESIS KG is geared towards interoperability and uses established W3C standards and widely accepted vocabularies, such as schema.org, DDI, the NFDIcore Ontology among others to increase interoperability and reusability of data on the Web for both humans and machines, e.g., through APIs. On instance-level, we address interoperability by reusing PIDs from commonly used PID systems, interlinking the GESIS KG with other KG provided by GESIS as well within the NFDI.
Find more information at Find more information at https://data.gesis.org/gesiskg/
Detailed description of the files can be found in GESISKG_readme.txt
Keywords: knowledge graph, semantic web, scholarly resource metadata, social science
Thesaurus for the Social Sciences (SKOS version)
No full textThe Thesaurus for the Social Sciences (TheSoz) contains about 12,000 entries, of which more than 8,000 are descriptors (authorised keywords) and about 6,000 non-descriptors. Topics from all disciplines of the social sciences are covered. This SKOS version of the thesaurus contains descriptors and non-descriptors in four languages (German, English, French, and Russian) as well as links to the TheSoz Classification, the STW Thesaurus for Economics, the AGROVOC Multilingual Thesaurus, and to DBpedia.
The TheSoz Classification (TheSoz-CL) is a topic hierarchy complementing the Thesaurus for the Social Sciences (TheSoz). Within this SKOS version of the classification, individual classification items contain links to entries from the TheSoz.
The zip archive contains the following files:
- thesoz.ttl: the SKOS version of the TheSoz in Turtle format
- thesoz_classification.ttl: the SKOS version of the TheSoz Classification in Turtle format
- readme.txt: readme fileThe Thesaurus for the Social Sciences (TheSoz) contains about 12,000 entries, of which more than 8,000 are descriptors (authorised keywords) and about 6,000 non-descriptors. Topics from all disciplines of the social sciences are covered. This SKOS version of the thesaurus contains descriptors and non-descriptors in four languages (German, English, French, and Russian) as well as links to the TheSoz Classification, the STW Thesaurus for Economics, the AGROVOC Multilingual Thesaurus, and to DBpedia.
The TheSoz Classification (TheSoz-CL) is a topic hierarchy complementing the Thesaurus for the Social Sciences (TheSoz). Within this SKOS version of the classification, individual classification items contain links to entries from the TheSoz.
The zip archive contains the following files:
- thesoz.ttl: the SKOS version of the TheSoz in Turtle format
- thesoz_classification.ttl: the SKOS version of the TheSoz Classification in Turtle format
- readme.txt: readme fil
ClaimsKG - A Knowledge Graph of Fact-Checked Claims (August, 2022)
No full textClaimsKG is a knowledge graph of metadata information for 59580 fact-checked claims scraped from 13 fact-checking sites. In addition to providing a single dataset of claims and associated metadata, truth ratings are harmonised and additional information is provided for each claim, e.g., about mentioned entities. Please see (https://data.gesis.org/claimskg/) for further details about the data model and statistics.
The dataset facilitates structured queries about claims, their truth values, involved entities, authors, dates, and other kinds of metadata. ClaimsKG is generated through a (semi-)automated pipeline, which harvests claim-related data from popular fact-checking web sites, annotates them with related entities from DBpedia/Wikipedia, and lifts all data to RDF using established vocabularies (such as schema.org).
The latest release of ClaimsKG covers 59580 claims. The data was scraped till August, of 2022 containing claims published between the years 1996-2022 from 13 factchecking websites. The claim-review (fact checking) period for claims ranges between the year 1996 to 2022. Entity fishing python client (https://github.com/hirmeos/entity-fishing-client-python) has been used for entity linking and disambiguation in this release. The dataset contains a total of 1371271 entities detected and referenced with DBpedia. More information, such as detailed statistics, query examples and a user-friendly interface to explore the knowledge graph is available at: https://data.gesis.org/claimskg/ .
The first two releases of ClaimsKG are hosted at Zenodo (https://doi.org/10.5281/zenodo.3518960), ClaimsKGV1.0 (published on 04.04.2019), ClaimsKGV2.0 (published on 01.09.2019). This latest release of ClaimsKG supersedes the previous versions as it contains all the claims from the previous versions together with additional claims as well as improved entity annotations.ClaimsKG is a knowledge graph of metadata information for 59580 fact-checked claims scraped from 13 fact-checking sites. In addition to providing a single dataset of claims and associated metadata, truth ratings are harmonised and additional information is provided for each claim, e.g., about mentioned entities. Please see (https://data.gesis.org/claimskg/) for further details about the data model and statistics.
The dataset facilitates structured queries about claims, their truth values, involved entities, authors, dates, and other kinds of metadata. ClaimsKG is generated through a (semi-)automated pipeline, which harvests claim-related data from popular fact-checking web sites, annotates them with related entities from DBpedia/Wikipedia, and lifts all data to RDF using established vocabularies (such as schema.org).
The latest release of ClaimsKG covers 59580 claims. The data was scraped till August, of 2022 containing claims published between the years 1996-2022 from 13 factchecking websites. The claim-review (fact checking) period for claims ranges between the year 1996 to 2022. Entity fishing python client (https://github.com/hirmeos/entity-fishing-client-python) has been used for entity linking and disambiguation in this release. The dataset contains a total of 1371271 entities detected and referenced with DBpedia. More information, such as detailed statistics, query examples and a user-friendly interface to explore the knowledge graph is available at: https://data.gesis.org/claimskg/ .
The first two releases of ClaimsKG are hosted at Zenodo (https://doi.org/10.5281/zenodo.3518960), ClaimsKGV1.0 (published on 04.04.2019), ClaimsKGV2.0 (published on 01.09.2019). This latest release of ClaimsKG supersedes the previous versions as it contains all the claims from the previous versions together with additional claims as well as improved entity annotations
ClaimsKG - A Knowledge Graph of Fact-Checked Claims (January, 2023)
No full textClaimsKG is a knowledge graph of metadata information for fact-checked claims scraped from popular fact-checking sites. In addition to providing a single dataset of claims and associated metadata, truth ratings are harmonized and additional information is provided for each claim, e.g., about mentioned entities. Please see ( https://data.gesis.org/claimskg/ ) for further details about the data model, query examples and statistics.
The dataset facilitates structured queries about claims, their truth values, involved entities, authors, dates, and other kinds of metadata. ClaimsKG is generated through a (semi-)automated pipeline, which harvests claim-related data from popular fact-checking web sites, annotates them with related entities from DBpedia/Wikipedia, and lifts all data to RDF using established vocabularies (such as schema.org).
The latest release of ClaimsKG covers 74066 claims and 72127 Claim Reviews. This is the fourth release of the dataset where data was scraped till Jan 31, 2023 containing claims published between 1996 and 2023 from 13 fact-checking websites. The websites are Fullfact, Politifact, TruthOrFiction, Checkyourfact, Vishvanews, AFP (French), AFP, Polygraph, EU factcheck, Factograph, Fatabyyano, Snopes and Africacheck. The claim-review (fact-checking) period for claims ranges between the year 1996 to 2023. Similar to the previous release, the Entity fishing python client ( https://github.com/hirmeos/entity-fishing-client-python ) has been used for entity linking and disambiguation in this release. Improvements have been made in the web scraping and data preprocessing pipeline to extract more entities from both claims and claims reviews. Currently, ClaimsKG contains 3408386 entities detected and referenced with DBpedia.
This latest release of ClaimsKG supersedes the previous versions as it contained all the claims from the previous versions together in addition to the additional new claims as well as improved entity annotation resulting in a higher number of entities.ClaimsKG is a knowledge graph of metadata information for fact-checked claims scraped from popular fact-checking sites. In addition to providing a single dataset of claims and associated metadata, truth ratings are harmonized and additional information is provided for each claim, e.g., about mentioned entities. Please see ( https://data.gesis.org/claimskg/ ) for further details about the data model, query examples and statistics.
The dataset facilitates structured queries about claims, their truth values, involved entities, authors, dates, and other kinds of metadata. ClaimsKG is generated through a (semi-)automated pipeline, which harvests claim-related data from popular fact-checking web sites, annotates them with related entities from DBpedia/Wikipedia, and lifts all data to RDF using established vocabularies (such as schema.org).
The latest release of ClaimsKG covers 74066 claims and 72127 Claim Reviews. This is the fourth release of the dataset where data was scraped till Jan 31, 2023 containing claims published between 1996 and 2023 from 13 fact-checking websites. The websites are Fullfact, Politifact, TruthOrFiction, Checkyourfact, Vishvanews, AFP (French), AFP, Polygraph, EU factcheck, Factograph, Fatabyyano, Snopes and Africacheck. The claim-review (fact-checking) period for claims ranges between the year 1996 to 2023. Similar to the previous release, the Entity fishing python client ( https://github.com/hirmeos/entity-fishing-client-python ) has been used for entity linking and disambiguation in this release. Improvements have been made in the web scraping and data preprocessing pipeline to extract more entities from both claims and claims reviews. Currently, ClaimsKG contains 3408386 entities detected and referenced with DBpedia.
This latest release of ClaimsKG supersedes the previous versions as it contained all the claims from the previous versions together in addition to the additional new claims as well as improved entity annotation resulting in a higher number of entities
SoRa Survey - Befragung zu georeferenzierten Daten
No full textZwischen dem 15. Oktober 2018 und dem 13. Januar 2019 wurden innerhalb des von der Deutschen Forschungsgemeinschaft (DFG) geförderten Projekts "Sozial‐Raumwissenschaftliche Forschungsdateninfrastruktur" (SoRa) 39 Personen zum Thema
georeferenzierte Daten befragt. Ziel der Befragung war zum einen zu erfassen, inwiefern Vorkenntnisse zu Daten und Methoden unter Forschenden vorhanden sind und zum anderen,
welche Bedarfe an etwaigen Services und Datenangeboten bestehen. Dazu wurden unterschiedliche Fragen zu den Erfahrungen und Interessen der Befragten aus jenen Bereichen entwickelt und in einem Online‐Survey eingesetzt. Das vorliegende Papier beschreibt kurz die Motivation und den Hintergrund der Befragung, ihre Durchführung und in größeren Umfang die Ergebnisse. Wir starten mit einer kurzen Einführung zur Relevanz des Themas und seine Einbindung im Projektkontext sowie einer Stichprobenbeschreibung. Danach stellen wir die Ergebnisse aufgeschlüsselt nach Themenblöcken vor. In einem letzten Schritt ordnen wir die Ergebnisse angesichts ihrer Implikationen für die Ziele des Surveys ein. Im Anhang befindet sich zudem der Fragebogen der Erhebung
Ein Mehr-Thesauri-Szenario auf Basis von SKOS und Crosskonkordanzen
No full textThe paper describes a conceptual scenario with three thesauri which are available in SKOS and interconnected with terminology mappings (cross-concordances). The involved thesauri are a) TheSoz (Thesaurus Sozialwissenschaften, GESIS), b) STW (Standard-Thesaurus Wirtschaft, ZBW) und c) IBLK-Thesaurus (SWP). In our usecase we explain the usage of the mapping properties of SKOS and SKOS-XL
Establishing a multi-thesauri-scenario based on SKOS and cross-concordances
No full text"Im August 2009 wurde SKOS 'Simple Knowledge Organization System' als neuer Standard für web-basierte kontrollierte Vokabulare durch das W3C veröffentlicht. SKOS dient als Datenmodell, um kontrollierte Vokabulare über das Web anzubieten sowie technisch und semantisch interoperabel zu machen. Perspektivisch kann die heterogene Landschaft der Erschließungsvokabulare über SKOS vereinheitlicht und vor allem die Inhalte der klassischen Datenbanken (Bereich Fachinformation) für Anwendungen des Semantic Web, beispielsweise als Linked Open Data (LOD), zugänglich und stär-ker miteinander vernetzt werden. Vokabulare im SKOS-Format können dabei eine relevante Funktion einnehmen, indem sie als standardisiertes Brückenvokabular dienen und semantische Verlinkung zwischen erschlossenen, veröffentlichten Daten herstellen. Die folgende Fallstudie skizziert ein Szenario mit drei thematisch verwandten Thesauri, die ins SKOS-Format übertragen und inhaltlich über Crosskonkordanzen aus dem Projekt KoMoHe verbunden werden. Die Mapping Properties von SKOS bieten dazu standardisierte Relationen, die denen der Crosskonkordanzen entsprechen. Die beteiligten Thesauri der Fallstudie sind a) TheSoz (Thesaurus Sozialwissenschaften, GESIS), b) STW (Standard-Thesaurus Wirtschaft, ZBW) und c) IBLK-Thesaurus (SWP)." (Autorenreferat
Enabling Longitudinal Data Comparison Using DDI
No full textHansen SE, Iverson J, Jansen U, Orten H, Vompras J. Enabling Longitudinal Data Comparison Using DDI. DDI Working Paper Series - Longitudinal Best Practice. Vol No. 2. Dagstuhl, Germany: DDI Alliance; 2011.This paper is part of a series that focuses on DDI usage and how the metadata specification should be applied in a variety of settings by a variety of organizations and individuals. Support for this working paper series was provided by the authors’ home institutions; by GESIS - Leibniz Institute for the Social Sciences; by Schloss Dagstuhl - Leibniz Center for Informatics; and by the DDI Alliance
