Ontology development to support the Open Public data - The Greek case

Theocharis, Stamatios; Tsihrintzis, George A.

doi:10.1109/iisa.2014.6878820

Cited by 10 publications

(3 citation statements)

References 4 publications

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“…Besides this, AI/ML can be used to select and generate diverse and related game content, even from sources of Open and Big Data (cf. [70] for a Monopoly clone populated with Open Data [65] to teach Big Data rankings and associations in the context of a primary school geography course or [13] for an approach that uses Open Data in a card game for environmental education), making games more relevant to everyday life. In this chapter, we outlined some of the more prominent approaches which combine AI/ML with game design concepts and player behaviour to provide information about player experience and generate content that's predicted to maximise engagement.…”

Section: Discussionmentioning

confidence: 99%

AI in (and for) Games

Karpouzis

Tsatiris

2021

Learning and Analytics in Intelligent Systems

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This chapter outlines the relation between artificial intelligence (AI)/mac hine learning (ML) algorithms and digital games. This relation is two-fold: on one hand, AI/ML researchers can generate large, in-the-wild datasets of human affective activity, player behaviour (i.e. actions within the game world), commercial behaviour, interaction with graphical user interface elements or messaging with other players, while games can utilise intelligent algorithms to automate testing of game levels, generate content, develop intelligent and responsive non-player characters (NPCs) or predict and respond to player behaviour across a wide variety of player cultures. In this work, we discuss some of the most common and widely accepted uses of AI/ML in games and how intelligent systems can benefit from those, elaborating on estimating player experience based on expressivity and performance, and on generating proper and interesting content for a language learning game.

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Section: Discussionmentioning

confidence: 99%

AI in (and for) Games

Karpouzis

Tsatiris

2021

Learning and Analytics in Intelligent Systems

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show abstract

Section: Discussionmentioning

confidence: 99%

AI in (and for) Games

Karpouzis,

Tsatiris

2021

Preprint

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This chapter outlines the relation between artificial intelligence (AI) / machine learning (ML) algorithms and digital games. This relation is two-fold: on one hand, AI/ML researchers can generate large, in-the-wild datasets of human affective activity, player behaviour (i.e. actions within the game world), commercial behaviour, interaction with graphical user interface elements or messaging with other players, while games can utilise intelligent algorithms to automate testing of game levels, generate content, develop intelligent and responsive non-player characters (NPCs) or predict and respond player behaviour across a wide variety of player cultures. In this work, we discuss some of the most common and widely accepted uses of AI/ML in games and how intelligent systems can benefit from those, elaborating on estimating player experience based on expressivity and performance, and on generating proper and interesting content for a language learning game.

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“…However, the alignment of vocabularies remains imperative to deal with vocabulary heterogeneity when aggregating and independently provided data. As highlighted by Theocharis [75], the diversity of terms employed across public sector bodies has already led to confusion during data searches and interconnections. Distinct vocabularies depict data at varying levels of detail, covering overlapping yet not identical facets of the domain, bringing to a granularity mismatch.…”

Section: Heterogeneitymentioning

confidence: 99%

Linked Open Government Data: Still a Viable Option for Sharing and Integrating Public Data?

Quarati,

Albertoni

2024

Future Internet

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Linked Data (LD) principles, when applied to Open Government Data (OGD), aim to make government data accessible and interconnected, unlocking its full potential and facilitating widespread reuse. As a modular and scalable solution to fragmented government data, Linked Open Government Data (LOGD) improve citizens’ understanding of government functions while promoting greater data interoperability, ultimately leading to more efficient government processes. However, despite promising developments in the early 2010s, including the release of LOGD datasets by some government agencies, and studies and methodological proposals by numerous scholars, a cursory examination of government websites and portals suggests that interest in this technology has gradually waned. Given the initial expectations surrounding LOGD, this paper goes beyond a superficial analysis and provides a deeper insight into the evolution of interest in LOGD by raising questions about the extent to which the dream of LD has influenced the reality of OGD and whether it remains sustainable.

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Ontology development to support the Open Public data - The Greek case

Cited by 10 publications

References 4 publications

AI in (and for) Games

AI in (and for) Games

AI in (and for) Games

Linked Open Government Data: Still a Viable Option for Sharing and Integrating Public Data?

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