Suchergebnisse

Intuitively, most datasets found on governmental Open Data portals are organized by spatio-temporal criteria, that is, single datasets provide data for a certain region, valid for a certain time period. Likewise, for many use cases (such as, for instance, data journalism and fact checking) a pre-dominant need is to scope down the relevant datasets to a particular period or region. Rich spatio-temporal annotations are therefore a crucial need to enable semantic search for (and across) Open Data portals along those dimensions, yet -- to the best of our knowledge -- no working solution exists. To this end, in the present paper we (i) present a scalable approach to construct a spatio-temporal knowledge graph that hierarchically structures geographical as well as temporal entities, (ii) annotate a large corpus of tabular datasets from open data portals with entities from this knowledge graph, and (iii) enable structured, spatio-temporal search and querying over Open Data catalogs, both via a search interface as well as via a SPARQL endpoint, available at http://data.wu.ac.at/odgraphsearch/ ; Series: Working Papers on Information Systems, Information Business and Operations

In the past years Open Data has become a trend among governments to increase transparency and public engagement by opening up national, regional, and local datasets. However, while many of these datasets come in semi-structured file formats, they use di ff erent schemata and lack geo-references or semantically meaningful links and descriptions of the corresponding geo-entities. We aim to address this by detecting and establishing links to geo-entities in the datasets found in Open Data catalogs and their respective metadata descriptions and link them to a knowledge graph of geo-entities. This knowledge graph does not yet readily exist, though, or at least, not a single one: so, we integrate and interlink several datasets to construct our (extensible) base geo-entities knowledge graph: (i) the openly available geospatial data repository GeoNames, (ii) the map service OpenStreetMap, (iii) country-specific sets of postal codes, and (iv) the European Union's classification system NUTS. As a second step, this base knowledge graph is used to add semantic labels to the open datasets, i.e., we heuristically disambiguate the geo-entities in CSV columns using the context of the labels and the hierarchical graph structure of our base knowledge graph. Finally, in order to interact with and retrieve the content, we index the datasets and provide a demo user interface. Currently we indexed resources from four Open Data portals, and allow search queries for geo-entities as well as full-text matches at http://data.wu.ac.at/odgraph/ .

Intuitively, most datasets found on governmental Open Data portals are organized by spatio-temporal criteria, that is, single datasets provide data for a certain region, valid for a certain time period. Likewise, for many use cases (such as, for instance, data journalism and fact checking) a pre-dominant need is to scope down the relevant datasets to a particular period or region. Rich spatio-temporal annotations are therefore a crucial need to enable semantic search for (and across) Open Data portals along those dimensions, yet -- to the best of our knowledge -- no working solution exists. To this end, in the present paper we (i) present a scalable approach to construct a spatio-temporal knowledge graph that hierarchically structures geographical as well as temporal entities, (ii) annotate a large corpus of tabular datasets from open data portals with entities from this knowledge graph, and (iii) enable structured, spatio-temporal search and querying over Open Data catalogs, both via a search interface as well as via a SPARQL endpoint, available at http://data.wu.ac.at/odgraphsearch/ ; Series: Working Papers on Information Systems, Information Business and Operations

Intuitively, most datasets found on governmental Open Data portals are organized by spatio-temporal criteria, that is, single datasets provide data for a certain region, valid for a certain time period. Likewise, for many use cases (such as, for instance, data journalism and fact checking) a pre-dominant need is to scope down the relevant datasets to a particular period or region. Rich spatio-temporal annotations are therefore a crucial need to enable semantic search for (and across) Open Data portals along those dimensions, yet -- to the best of our knowledge -- no working solution exists. To this end, in the present paper we (i) present a scalable approach to construct a spatio-temporal knowledge graph that hierarchically structures geographical as well as temporal entities, (ii) annotate a large corpus of tabular datasets from open data portals with entities from this knowledge graph, and (iii) enable structured, spatio-temporal search and querying over Open Data catalogs, both via a search interface as well as via a SPARQL endpoint, available at http://data.wu.ac.at/odgraphsearch/ ; Series: Working Papers on Information Systems, Information Business and Operations

Intuitively, most datasets found on governmental Open Data portals are organized by spatio-temporal criteria, that is, single datasets provide data for a certain region, valid for a certain time period. Likewise, for many use cases (such as, for instance, data journalism and fact checking) a pre-dominant need is to scope down the relevant datasets to a particular period or region. Rich spatio-temporal annotations are therefore a crucial need to enable semantic search for (and across) Open Data portals along those dimensions, yet -- to the best of our knowledge -- no working solution exists. To this end, in the present paper we (i) present a scalable approach to construct a spatio-temporal knowledge graph that hierarchically structures geographical as well as temporal entities, (ii) annotate a large corpus of tabular datasets from open data portals with entities from this knowledge graph, and (iii) enable structured, spatio-temporal search and querying over Open Data catalogs, both via a search interface as well as via a SPARQL endpoint, available at http://data.wu.ac.at/odgraphsearch/ ; Series: Working Papers on Information Systems, Information Business and Operations

The European Union is working towards harmonizing legislation across Europe, in order to improve cross-border interchange of legal information. This goal is supported for instance via standards such as the European Law Identifier (ELI) and the European Case Law Identifier (ECLI), which provide technical specifications for Web identifiers and suggestions for vocabularies to be used to describe metadata pertaining to legal documents in a machine readable format. Notably, these ECLI and ELI metadata standards adhere to the RDF data format which forms the basis of Linked Data, and therefore have the potential to form a basis for a pan-European legal Knowledge Graph. Unfortunately, to date said specifications have only been partially adopted by EU member states. In this paper we describe a methodology to transform the existing legal information system used in Austria to such a legal knowledge graph covering different steps from modeling national specific aspects, to population, and finally the integration of legal data from other countries through linked data. We demonstrate the usefulness of this approach by exemplifying practical use cases from legal information search, which are not possible in an automated fashion so far.

In the past years Open Data has become a trend among governments to increase transparency and public engagement by opening up national, regional, and local datasets. However, while many of these datasets come in semi-structured file formats, they use different schemata and lack geo-references or semantically meaningful links and descriptions of the corresponding geo-entities. We aim to address this by detecting and establishing links to geo-entities in the datasets found in Open Data catalogs and their respective metadata descriptions and link them to a knowledge graph of geo-entities. This knowledge graph does not yet readily exist, though, or at least, not a single one: so, we integrate and interlink several datasets to construct our (extensible) base geo-entities knowledge graph: (i) the openly available geospatial data repository GeoNames, (ii) the map service OpenStreetMap, (iii) country-specific sets of postal codes, and (iv) the European Union¿s classification system NUTS. As a second step, this base knowledge graph is used to add semantic labels to the open datasets, i.e., we heuristically disambiguate the geo-entities in CSV columns using the context of the labels and the hierarchical graph structure of our base knowledge graph. Finally, in order to interact with and retrieve the content, we index the datasets and provide a demo user interface. Currently we indexed resources from four Open Data portals, and allow search queries for geo-entities as well as full-text matches at http://data.wu.ac.at/odgraph/.

Open Source Software (OSS) plays an important role in the digital economy. Yet although software production is amenable to remote collaboration and its outputs are digital, software development seems to cluster geographically in places like Silicon Valley, London, or Berlin. And while OSS activity creates positive externalities which accrue locally through knowledge spillovers and information effects, up-to-date data on the geographic distribution of open source developers is limited. This presents a significant blindspot for policymakers, who often promote OSS at the national level as a cost-saving tool for public sector institutions. We address this gap by geolocating more than half a million active contributors to GitHub in early 2021 at various spatial scales. Compared to results from 2010, we find a significant increase in the share of developers based in Asia, Latin America and Eastern Europe, suggesting a more even spread of OSS developers globally. Within countries, however, we find significant concentration in regions, exceeding the concentration of high-tech employment. Social and economic development indicators predict at most half of regional variation in OSS activity in the EU, suggesting that clusters have idiosyncratic roots. We argue for localized policies to support networks of OSS developers in cities and regions.

TThere is a growing body of literature recognizing the benefits of Open Data. However, many potential data providers are unwilling to publish their data and at the same time, data users are often faced with difficulties when attempting to use Open Data in practice. Despite various barriers in using and publishing Open Data still being present, studies which systematically collect and assess these barriers are rare. Based on this observation we present a review on prior literature on barriers and the results of an empirical study aimed at assessing both the users' and publishers' views on obstacles regarding Open Data adoption. We collected data with an online survey in Austria and internationally. Using a sample of 183 participants, we draw conclusions about the relative importance of the barriers reported in the literature. In comparison to a previous conference paper presented at the conference for E-Democracy and Open Government, this article includes new additional data from participants outside Austria, reports new analyses, and substantially extends the discussion of results and of possible strategies for the mitigation of Open Data barriers.

There is a growing body of literature recognizing the benefits of Open Data. However, many potential data providers are unwilling to publish their data and at the same time, data users are often faced with difficulties when attempting to use Open Data in practice. Despite various barriers in using and publishing Open Data still being present, studies which systematically collect and assess these barriers are rare. Based on this observation we present a review on prior literature on barriers and the results of an empirical study aimed at assessing both the users' and publishers' views on obstacles regarding Open Data adoption. We collected data with an online survey in Austria and internationally. Using a sample of 183 participants, we draw conclusions about the relative importance of the barriers reported in the literature. In comparison to a previous conference paper presented at the conference for E-Democracy and Open Government, this article includes new additional data from participants outside Austria, reports new analyses, and substantially extends the discussion of results and of possible strategies for the mitigation of Open Data barriers.

TThere is a growing body of literature recognizing the benefits of Open Data. However, many potential data providers are unwilling to publish their data and at the same time, data users are often faced with difficulties when attempting to use Open Data in practice. Despite various barriers in using and publishing Open Data still being present, studies which systematically collect and assess these barriers are rare. Based on this observation we present a review on prior literature on barriers and the results of an empirical study aimed at assessing both the users' and publishers' views on obstacles regarding Open Data adoption. We collected data with an online survey in Austria and internationally. Using a sample of 183 participants, we draw conclusions about the relative importance of the barriers reported in the literature. In comparison to a previous conference paper presented at the conference for E-Democracy and Open Government, this article includes new additional data from participants outside Austria, reports new analyses, and substantially extends the discussion of results and of possible strategies for the mitigation of Open Data barriers.

Managing privacy and understanding handling of personal data has turned into a fundamental right, at least within the European Union, with the General Data Protection Regulation (GDPR) being enforced since May 25th 2018. This has led to tools and services that promise compliance to GDPR in terms of consent management and keeping track of personal data being processed. The information recorded within such tools, as well as that for compliance itself, needs to be interoperable to provide sufficient transparency in its usage. Additionally, interoperability is also necessary towards addressing the right to data portability under GDPR as well as creation of user-configurable and manageable privacy policies. We argue that such interoperability can be enabled through agreement over vocabularies using linked data principles. The W3C Data Privacy Vocabulary and Controls Community Group (DPVCG) was set up to jointly develop such vocabularies towards interoperability in the context of data privacy. This paper presents the resulting Data Privacy Vocabulary (DPV), along with a discussion on its potential uses, and an invitation for feedback and participation.