Generating a linked soccer dataset

Bergmann, Tanja; Bunk, Stefan; Eschrig, Johannes; Hentschel, Christian; Knuth, Magnus; Sack, Harald; Schüler, Ricarda

doi:10.1145/2506182.2506203

Cited by 5 publications

(3 citation statements)

References 2 publications

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“…Beside several commercial providers and unstructured text websites, freely available and machine readable sources can be found online [24][25][26][27][28]. Bergmann et al [29] show how such data sources can be matched and stored.…”

Section: Descriptive (Statistical) Data/derived Datamentioning

confidence: 99%

How to Make Sense of Team Sport Data: From Acquisition to Data Modeling and Research Aspects

Stein

Janetzko

Seebacher

et al. 2017

Data

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Automatic and interactive data analysis is instrumental in making use of increasing amounts of complex data. Owing to novel sensor modalities, analysis of data generated in professional team sport leagues such as soccer, baseball, and basketball has recently become of concern, with potentially high commercial and research interest. The analysis of team ball games can serve many goals, e.g., in coaching to understand effects of strategies and tactics, or to derive insights improving performance. Also, it is often decisive to trainers and analysts to understand why a certain movement of a player or groups of players happened, and what the respective influencing factors are. We consider team sport as group movement including collaboration and competition of individuals following specific rule sets. Analyzing team sports is a challenging problem as it involves joint understanding of heterogeneous data perspectives, including high-dimensional, video, and movement data, as well as considering team behavior and rules (constraints) given in the particular team sport. We identify important components of team sport data, exemplified by the soccer case, and explain how to analyze team sport data in general. We identify challenges arising when facing these data sets and we propose a multi-facet view and analysis including pattern detection, context-aware analysis, and visual explanation. We also present applicable methods and technologies covering the heterogeneous aspects in team sport data.

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Section: Descriptive (Statistical) Data/derived Datamentioning

confidence: 99%

How to Make Sense of Team Sport Data: From Acquisition to Data Modeling and Research Aspects

Stein

Janetzko

Seebacher

et al. 2017

Data

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“…KGs consists of triples of the form (s,r,o) ∈ KG, where s and o denote the subject and object entities, respectively, and r denotes their relation. Following [6], we created a soccer knowledge graph from WikiData [30] which consists of information such as a team's coach, captain and also information such as home ground and its capacity for soccer clubs. For information about players, we have parsed individual wikipedia pages of the teams and mined goals scored, position, caps, height and age of players.…”

Section: Soccer Knowledge Graphmentioning

confidence: 99%

Using a KG-Copy Network for Non-goal Oriented Dialogues

Chaudhuri

Rony

Jordan

et al. 2019

Lecture Notes in Computer Science

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Non-goal oriented, generative dialogue systems lack the ability to generate answers with grounded facts. A knowledge graph can be considered an abstraction of the real world consisting of well-grounded facts. This paper addresses the problem of generating well-grounded responses by integrating knowledge graphs into the dialogue system's response generation process, in an end-to-end manner. A dataset for nongoal oriented dialogues is proposed in this paper in the domain of soccer, conversing on different clubs and national teams along with a knowledge graph for each of these teams. A novel neural network architecture is also proposed as a baseline on this dataset, which can integrate knowledge graphs into the response generation process, producing well articulated, knowledge grounded responses. Empirical evidence suggests that the proposed model performs better than other state-of-the-art models for knowledge graph integrated dialogue systems.

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“…Finding publicly available sports datasets which include soccer metadata is not an easy task. To overcome the locked nature restrictions of today's proprietary sports datasets, Bergmann et al (BERGMANN et al, 2013) presented soccer data (matches, teams and players information) as a Linked Open Data solution (LOD) collected from heterogeneous sources and linked to LOD datasets. Some alternatives to open soccer data exist, such as openLigaDB 1 and the Openfooty API 2 .…”

Section: 1 Dat Aset Smentioning

confidence: 99%

Multidimensional projections for the visual exploration of multimedia data

Coimbra

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Brazilian group, thanks for the relaxing and enjoyable time playing together. My research group in Brazil VICG (and ICMC in general), thank you for discussing useful (or not) ideas while drinking coffee, and moments of making fun of each others inside the office. Thanks go to the SVCG research group in Groningen, that treat me kindly and helped me to improve my research skills. Many thanks to the 5 th floor JBI friends, for exploring the city, countries and restaurants, and spending great moments together. Also, I would like to acknowledge the research funding provided by the Brazilian agencies FAPESP (grant 2011/17925-1), CNPq (grant 156580/2011-0), and the research project CAPES/NUFFIC 028/11. Carol, for believing in me more than I usually do, giving me support in hard times and sharing so many great moments together: Thanks a lot. God: No matter who, what or where are You. No matter if You are only one or many. No matter which religion You really belong (if You, in fact, belong to some). What matters is that you are providing me wonderful moments, in wonderful places, with wonderful people. Thanks! Palavr as-chave: visualização de informação, projeção multidimensional, visualização multimídia, visualização explicativa. ABST RACT COIMBRA, D. B.. M ultidimensional proj ections for the visual explor ation of multimedia data. 2016. 236 f. Thesis (Doctorate Candidate Program in Computer Science and Computational Mathematics (ICMC-USP) and of PhD (RUG), in accordance with the international academic agreement for PhD double degree agreement for PhD double degree signed between

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Generating a linked soccer dataset

Cited by 5 publications

References 2 publications

How to Make Sense of Team Sport Data: From Acquisition to Data Modeling and Research Aspects

How to Make Sense of Team Sport Data: From Acquisition to Data Modeling and Research Aspects

Using a KG-Copy Network for Non-goal Oriented Dialogues

Multidimensional projections for the visual exploration of multimedia data

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