EventKG: A Multilingual Event-Centric Temporal Knowledge Graph

Demidova

2019

Self Cite

One of the key requirements to facilitate the semantic analytics of information regarding contemporary and historical events on the Web, in the news and in social media is the availability of reference knowledge repositories containing comprehensive representations of events, entities and temporal relations. Existing knowledge graphs, with popular examples including DBpedia, YAGO and Wikidata, focus mostly on entity-centric information and are insufficient in terms of their coverage and completeness with respect to events and temporal relations. In this article we address this limitation, formalise the concept of a temporal knowledge graph and present its instantiation -EventKG. EventKG is a multilingual event-centric temporal knowledge graph that incorporates over 690 thousand events and over 2.3 million temporal relations obtained from several large-scale knowledge graphs and semi-structured sources and makes them available through a canonical RDF representation. Whereas popular entities often possess hundreds of relations within a temporal knowledge graph such as EventKG, generating a concise overview of the most important temporal relations for a given entity is a challenging task. In this article we demonstrate an application of EventKG to biographical timeline generation, where we adopt a distant supervision method to identify relations most relevant for an entity biography. Our evaluation results provide insights on the characteristics of EventKG and demonstrate the effectiveness of the proposed biographical timeline generation method.

Section: Appendix a Example Queriesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

EventKG – the hub of event knowledge on the web – and biographical timeline generation

Demidova

2019

Self Cite

“…The terms specific to Simple-ML are defined in the Simple-ML vocabulary, denoted using the sml prefix. 3 Domain Model: In Simple-ML, the domain model describes relevant concepts, their properties and relations in the specific application domain. The class sml:DomainModel represents the model of an application domain.…”

Section: Semantic Models For Sdawsmentioning

confidence: 99%

Simple-ML: Towards a Framework for Semantic Data Analytics Workflows

Lecture Notes in Computer Science

Tempelmeier

Kniesel³

et al. 2019

Self Cite

In this paper we present the Simple-ML framework that we develop to support efficient configuration, robustness and reusability of data analytics workflows through the adoption of semantic technologies. We present semantic data models that lay the foundation for the framework development and discuss the data analytics workflows based on these models. Furthermore, we present an example instantiation of the Simple-ML data models for a real-world use case in the mobility domain.

“…There has been research on several exemplary aspects of knowledge graph completeness, for example on the incompleteness of Wikidata [1,2] and the relation between obligatory attributes and missing attribute values [13]. In our previous work, we considered the problem of integration and fusion of event-centric information spread across different knowledge graphs and created the EventKG knowledge graph that integrates such information [8,10]. [28] addressed the inference of missing categorical information in event descriptions in Web markup.…”

Section: Related Workmentioning

confidence: 99%

HapPenIng: Happen, Predict, Infer—Event Series Completion in a Knowledge Graph

Lecture Notes in Computer Science

Demidova

2019

Self Cite

Event series, such as the Wimbledon Championships and the US presidential elections, represent important happenings in key societal areas including sports, culture and politics. However, semantic reference sources, such as Wikidata, DBpedia and EventKG knowledge graphs, provide only an incomplete event series representation. In this paper we target the problem of event series completion in a knowledge graph. We address two tasks: 1) prediction of sub-event relations, and 2) inference of real-world events that happened as a part of event series and are missing in the knowledge graph. To address these problems, our proposed supervised HapPenIng approach leverages structural features of event series. HapPenIng does not require any external knowledge -the characteristics making it unique in the context of event inference. Our experimental evaluation demonstrates that HapPenIng outperforms the baselines by 44 and 52 percentage points in terms of precision for the sub-event prediction and the inference tasks, correspondingly.