EventKG+Click: A Dataset of Language-specific Event-centric User Interaction Traces

Abdollahi, Sara; Gottschalk, Simon; Demidova, Elena

doi:10.48550/arxiv.2010.12370

Cited by 2 publications

(4 citation statements)

References 12 publications

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“…Based on the proposed models, a number of populated ECKGs have been developed in various domains. Specifically, based on the SEM model [18], various cross-domain ECKGs [28][29][30] and domain-specific ECKGs, i.e., tourism [31] and news [3], have been developed. Most of the ECKGs [3,[28][29][30][31] capture information about the Events (e.g., Agent, Time, Place), while two of them capture temporal Event-Event relations [32,33].…”

Section: Event-centric Knowledge Graphsmentioning

confidence: 99%

“…Specifically, based on the SEM model [18], various cross-domain ECKGs [28][29][30] and domain-specific ECKGs, i.e., tourism [31] and news [3], have been developed. Most of the ECKGs [3,[28][29][30][31] capture information about the Events (e.g., Agent, Time, Place), while two of them capture temporal Event-Event relations [32,33]. Table 2 provides more information about the populated ECKGs that have been identified based on a non-exhaustive literature review by the authors.…”

Section: Event-centric Knowledge Graphsmentioning

confidence: 99%

“…EventKG [28] cross-domain SEM [18] Event-centric information extracted from Wikidata, DBpedia and YAGO EventKG+Click [29] cross-domain SEM [18] EventKG plus information on user interactions with events, entities, and their relations derived from Wikipedia clickstream OEKG [30] cross-domain SEM [18] A multi-domain open event knowledge graph (includes EventKG and EventKG+Click)…”

Section: Name Domain Base Model Descriptionmentioning

confidence: 99%

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An Event-Centric Knowledge Graph Approach for Public Administration as an Enabler for Data Analytics

Zeginis,

Tarabanis

2024

Computers

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In a continuously evolving environment, organizations, including public administrations, need to quickly adapt to change and make decisions in real-time. This requires having a real-time understanding of their context that can be achieved by adopting an event-native mindset in data management which focuses on the dynamics of change compared to the state-based traditional approaches. In this context, this paper proposes the adoption of an event-centric knowledge graph approach for the holistic data management of all data repositories in public administration. Towards this direction, the paper proposes an event-centric knowledge graph model for the domain of public administration that captures these dynamics considering events as first-class entities for knowledge representation. The development of the model is based on a state-of-the-art analysis of existing event-centric knowledge graph models that led to the identification of core concepts related to event representation, on a state-of-the-art analysis of existing public administration models that identified the core entities of the domain, and on a theoretical analysis of concepts related to events, public services, and effective public administration in order to outline the context and identify the domain-specific needs for event modeling. Further, the paper applies the model in the context of Greek public administration in order to validate it and showcase the possibilities that arise. The results show that the adoption of event-centric knowledge graph approaches for data management in public administration can facilitate data analytics, continuous integration, and the provision of a 360-degree-view of end-users. We anticipate that the proposed approach will also facilitate real-time decision-making, continuous intelligence, and ubiquitous AI.

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Section: Event-centric Knowledge Graphsmentioning

confidence: 99%

Section: Event-centric Knowledge Graphsmentioning

confidence: 99%

Section: Name Domain Base Model Descriptionmentioning

confidence: 99%

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An Event-Centric Knowledge Graph Approach for Public Administration as an Enabler for Data Analytics

Zeginis,

Tarabanis

2024

Computers

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“…The EventKG (Abdollahi et al 2020) is a multilingual1 resource incorporating event-centric information extracted from several large-scale knowledge graphs such as Wikidata, DBpedia, and YAGO, as well as less structured sources such as the Wikipedia Current Events Portal and Wikipedia event lists in 15 languages. It contains details of more than 1,200,000 events in nine languages.…”

Section: Event Detection Datasetsmentioning

confidence: 99%

Detection of dangerous events on social media: a critical review

Jamil

Pais

Cordeiro

2022

Soc. Netw. Anal. Min.

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The usability of the events information on social media has been widely studied recently. Several surveys have reviewed the specific type of events on social media using various techniques. Most of the existing methods for event detection are segregated as they approach certain situations that limit the overall details of events happening consecutively on social media while ignoring the crucial relationship between the evolution of these events. Numerous events that materialize on the social media sphere every day before our eyes jeopardize people’s safety and are referred to by using a high-level concept of dangerous events. The front of dangerous events is broad, yet no known work exists that fully addresses and approaches this issue. This work introduces the term dangerous events and defines its scope in terms of practicality to establish the origins of the events caused by the previous events and their respective relationship. Furthermore, it divides dangerous events into sentiment, scenario, and action-based dangerous events grouped on their similarities. The existing research and methods related to event detection are surveyed, including some available events datasets and knowledge-base to address the problem. Finally, the survey is concluded with suggestions for future work and possible related challenges.

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EventKG+Click: A Dataset of Language-specific Event-centric User Interaction Traces

Cited by 2 publications

References 12 publications

An Event-Centric Knowledge Graph Approach for Public Administration as an Enabler for Data Analytics

An Event-Centric Knowledge Graph Approach for Public Administration as an Enabler for Data Analytics

Detection of dangerous events on social media: a critical review

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