WIGeoGIS: Softwareerstellungs- und Handelsgesellschaft MGH

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WIGEOGIS was founded by Zoltán Daróczi and Georg Magenschab as an offshoot of a research group at the University of Economics of Vienna (Wirtschaftsuniversität Wien) in 1993 and is one of the leading European companies in the fields of geomarketing and Web GIS today. The company currently has a turnover of 6M Euros and employs 40 engineers and staff (2015, Austria, Germany and Poland / 15 engineers Austrian company). WIGEOGIS has branch offices in Vienna, Munich, and Warszawa. WIGEOGIS offers consulting services in the following areas: location planning, sales and operations planning, media planning as well as target group analysis and penetration analysis. Apart from its standard products WIGEOGIS also develops and customizes sotware based on geographic information systems. All products and services of WIGEOGIS evolve around the concept of Geo Business Intelligence, i.e., services that derive their value from superior geospatial datasets. WIGEOGIS products are used by over 300 companies in fields such as company management, marketing, sales and services. Our customers include clients such as Allianz, Unicredit, T-Mobile, Mediaprint, OMV, Spar, UPC, Volkswagen and many more.

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Project Collaboration

SLIPO

POIs are the content of any application, service, and product even remotely related to our physical surroundings. From navigation applications, to social networks, to tourism, and logistics, we use POIs to search, communicate, decide, and plan our actions. The Big Data assets for POIs and the evolved POI value chain introduced opportunities for growth, but also complexity, intensifying the challenges relating to their quality-assured integration, enrichment, and data sharing. POI data are by nature semantically diverse and spatiotemporally evolving, representing different entities and associations depending on their geographical, temporal, and thematic context. Pioneered by the FP7 project GeoKnow, linked data technologies have been applied to effectively extract the maximum possible value from open, crowdsourced and proprietary Big Data sources. Validated in the domains of tourism and logistics, these technologies have proven their benefit as a cost-effective and scalable foundation for the quality-assured integration, enrichment, and sharing of generic-purpose geospatial data In SLIPO, we argue that linked data technologies can address the limitations, gaps and challenges of the current landscape in integrating, enriching, and sharing POI data. Our goal is to transfer the research output generated by our work in project GeoKnow, to the specific challenge of POI data, introducing validated and cost-effective innovations across their value chain. Read more about SLIPO

News

Assessing Language Identification Over DBpedia ( 2021-05-04T23:27:40+02:00 EdgardMarx)

2021-05-04T23:27:40+02:00 EdgardMarx

Large-scale multilingual knowledge bases (KBs) are the key for cross-lingual and multilingual applications such as Question Answering, Machine  Translation,  and  Search. Read more about "Assessing Language Identification Over DBpedia"

DBpedia Tutorial @ Knowledge Graph Conference 2021 ( 2021-04-09T13:20:50+02:00 by Julia Holze)

2021-04-09T13:20:50+02:00 by Julia Holze

On May 4, 2021 we will organize a tutorial at the Knowledge Graph Conference (KGC) 2021. Read more about "DBpedia Tutorial @ Knowledge Graph Conference 2021"

DBpedia @ Google Summer of Code program 2021 ( 2021-03-15T09:41:22+01:00 by Julia Holze)

2021-03-15T09:41:22+01:00 by Julia Holze

DBpedia, one of InfAI’s community projects, will participate in the Google Summer of Code (GSoC) program for the 10th time. The GsoC program has the goal to bring students from all over the globe into open source software development. Read more about "DBpedia @ Google Summer of Code program 2021"

DBpedia’s New Website ( 2021-01-28T12:42:40+01:00 by Julia Holze)

2021-01-28T12:42:40+01:00 by Julia Holze

We are proud to announce the completion of the new DBpedia website. Read more about "DBpedia’s New Website"

SANSA 0.7.1 (Semantic Analytics Stack) Released ( 2020-01-17T09:52:41+01:00 by Prof. Dr. Jens Lehmann)

2020-01-17T09:52:41+01:00 by Prof. Dr. Jens Lehmann

We are happy to announce SANSA 0.7.1 – the seventh release of the Scalable Semantic Analytics Stack. SANSA employs distributed computing via Apache Spark and Flink in order to allow scalable machine learning, inference and querying capabilities for large knowledge graphs. Read more about "SANSA 0.7.1 (Semantic Analytics Stack) Released"