The Rich Event Ontology

Abstract

In this paper we describe a new lexical semantic resource, The Rich Event Ontology, which provides an independent conceptual backbone to unify existing semantic role labeling (SRL) schemas and augment them with event-to-event causal and temporal relations. By unifying the FrameNet, VerbNet, Automatic Content Extraction, and Rich Entities, Relations and Events resources, the ontology serves as a shared hub for the disparate annotation schemas and therefore enables the combination of SRL training data into a larger, more diverse corpus. By adding temporal and causal relational information not found in any of the independent resources, the ontology facilitates reasoning on and across documents, revealing relationships between events that come together in temporal and causal chains to build more complex scenarios. We envision the open resource serving as a valuable tool for both moving from the ontology to text to query for event types and scenarios of interest, and for moving from text to the ontology to access interpretations of events using the combined semantic information housed there.

Weakly Supervised Subevent Knowledge Acquisition

“…We can potentially incorporate our learned subevent knowledge into a general event ontology to enrich subevent links in the ontology. For instance, the Rich Event Ontology (REO) (Brown et al, 2017) unifies two existing knowledge resources (i.e., FrameNet (Fillmore et al, 2003) and VerbNet (Kipper et al, 2008)) and two event annotated datasets (i.e., ACE (Doddington et al, 2004) and ERE ) to allow users to query multiple linguistic resources and combine event annotations. However, REO contains few subevent relation links between events.…”

Ontologies for finding journalistic angles

“…When assessing the overall confidence in an annotation, both annotation confidence and item confidence must be taken into account. 7 The strength describes how strongly a graded annotation applies to a news item. For example, for a sentiment annotation like anger, it gauges the degree of anger expressed in the text whereas, for a relation such as likes, it represents how strongly one entity (a prospective informant) likes another (a person in the news).…”

“…It provides a detailed framework for describing different types of events along with their relations to other events and to the agents and other entities they involve. -Other common ontologies that deal with events and situations include: Linked Open Description of Events (LODE) [42], which is an intentionally minimal model of events aimed to facilitate interoperability; DOLCE+DnS UltraLite (DUL) [10], which is a simplification and extension of the DOLCE [11] and the Descriptions and Situations ontologies; the F Model of Events [39], which builds a modularised event model over DUL to cover participation in, composition of, causality and correlation of, documentation/representation of, and interpretation of events; the Simple Event Model (SEM) [50]), which provides core classes for describing events in terms of their actors, places, and times; the Rich Event Ontology (REO) [7], which is an OWL ontology that unifies existing semantic role-labelling 13 https://iptc.org/standards/eventsml-g2/ [43], which is an event ontology and lexicon designed identify semantic relations between entities that appear in a texts or knowledge graphs.…”

KnowSemLM: A Knowledge Infused Semantic Language Model

“…Several recent works focus on narrative/story telling (Rishes et al, 2013), as well as studying event structures (Brown et al, 2017). Most recently, Mostafazadeh et al (2016Mostafazadeh et al ( , 2017 proposed story cloze test as a standard way to test a system's ability to model semantics.…”