In this paper, we present a new method to obtain large volumes of high-quality text corpora with event data for studying identity and reference relations. We report on the current methods to create event reference data by annotating texts and deriving the event data a posteriori. Our method starts from event registries in which event data is defined a priori. From this data, we extract so-called Microworlds of referential data with the Reference Texts that report on these events. This makes it possible to easily establish referential relations with high precision and at a large scale. In a pilot, we successfully obtained data from these resources with extreme ambiguity and variation, while maintaining the identity and reference relations and without having to annotate large quantities of texts word-by-word. The data from this pilot was annotated using an annotation tool created specifically in order to validate our method and to enrich the reference texts with event coreference annotations. This annotation process resulted in the Gun Violence Corpus, whose development process and outcome are described in this paper.

In this paper, we describe the Circumstantial Event Ontology (CEO), a newly developed ontology for calamity events that models semantic circumstantial relations between event classes, where we define circumstantial as inferred implicit causal relations. The circumstantial relations are inferred from the assertions of the event classes that involve a change to the same property of a participant. Our model captures that the change yielded by one event, explains to people the happening of the next event when observed. We describe the meta model and the contents of the ontology, the creation of a manually annotated corpus for circumstantial relations based on ECB+ and the first results on the evaluation of the ontology.

In this paper we describe the ongoing work on the Circumstantial Event Ontology (CEO), a newly developed ontology for calamity events that models semantic circumstantial relations between event classes. The circumstantial relations are designed manually, based on the shared properties of each event class. We discuss and contrast two types of event circumstantial relations: semantic circumstantial relations and episodic circumstantial relations. Further, we show the metamodel and the current contents of the ontology and outline the evaluation of the CEO.

In this article, we describe a system that reads news articles in four different languages and detects what happened, who is involved, where and when. This event-centric information is represented as episodic situational knowledge on individuals in an interoperable RDF format that allows for reasoning on the implications of the events. Our system covers the complete path from unstructured text to structured knowledge, for which we defined a formal model that links interpreted textual mentions of things to their representation as instances. The model forms the skeleton for interoperable interpretation across different sources and languages. The real content, however, is defined using multilingual and cross-lingual knowledge resources, both semantic and episodic. We explain how these knowledge resources are used for the processing of text and ultimately define the actual content of the episodic situational knowledge that is reported in the news. The knowledge and model in our system can be seen as an example how the Semantic Web helps NLP. However, our systems also generate massive episodic knowledge of the same type as the Semantic Web is built on. We thus envision a cycle of knowledge acquisition and NLP improvement on a massive scale. This article reports on the details of the system but also on the performance of various high-level components. We demonstrate that our system performs at state-of-the-art level for various subtasks in the four languages of the project, but that we also consider the full integration of these tasks in an overall system with the purpose of reading text. We applied our system to millions of news articles, generating billions of triples expressing formal semantic properties. This shows the capacity of the system to perform at an unprecedented scale.

This paper presents the Event and Implied Situation Ontology (ESO), a manually constructed resource which formalizes the pre and post situations of events and the roles of the entities affected by an event. The ontology is built on top of existing resources such as WordNet, SUMO and FrameNet. The ontology is injected to the Predicate Matrix, a resource that integrates predicate and role information from amongst others FrameNet, VerbNet, PropBank, NomBank and WordNet. We illustrate how these resources are used on large document collections to detect information that otherwise would have remained implicit. The ontology is evaluated on two aspects: recall and precision based on a manually annotated corpus and secondly, on the quality of the knowledge inferred by the situation assertions in the ontology. Evaluation results on the quality of the system show that 50% of the events typed and enriched with ESO assertions are correct.

Dutch version of WordNet More information about the project: http://www.cltl.nl/projects/current-projects/opensourcewordnet/ Authors and main associated publication: @InProceedings{Postma:Miltenburg:Segers:Schoen:Vossen:2016, author = "Marten Postma and Emiel van Miltenburg and Roxane Segers and Anneleen Schoen and Piek Vossen", title = "Open {Dutch} {WordNet}", booktitle = "Proceedings of the Eight Global Wordnet Conference", year = 2016, address = "Bucharest, Romania", }

This chapter lists and discusses open challenges for the ODP community in the coming years, both in terms of research questions that will need be answered, and in terms of tooling and infrastructur ...

This paper presents the Event and Implied Situation Ontology (ESO), a resource which formalizes the pre and post situations of events and the roles of the entities affected by an event. The ontology reuses and maps across existing resources such as WordNet, SUMO, VerbNet, PropBank and FrameNet. We describe how ESO is injected into a new version of the Predicate Matrix and illustrate how these resources are used to detect information in large document collections that otherwise would have remained implicit. The model targets interpretations of situations rather than the semantics of verbs per se. The event is interpreted as a situation using RDF taking all event components into account. Hence, the ontology and the linked resources need to be considered from the perspective of this interpretation model.

This paper presents the Event and Situation Ontology (ESO), a resource which formalizes the pre and post conditions of events and the roles of the entities affected by an event. The ontology reuses and maps across existing resources such as Wordnet, SUMO and Framenet and is designed for extracting information from text that otherwise would have been implicit. We present the metamodel of the ontology and the procedure for building the first version of ESO.

One of the goals of the STEVIN programme is the realisation of a digital infrastructure that will enforce the position of the Dutch language in the modern information and communication technology.A semantic database makes it possible to go from words to concepts and consequently, to develop technologies that access and use knowledge rather than textual representations.

Events have become central elements in the representation of data from domains such as history, cultural heritage, multimedia and geography. The Simple Event Model (SEM) is created to model events in these various domains, without making assumptions about the domain-specific vocabularies used. SEM is designed with a minimum of semantic commitment to guarantee maximal interoperability. In this paper, we discuss the general requirements of an event model for web data and give examples from two use cases: historic events and events in the maritime safety and security domain. The advantages and disadvantages of several existing event models are discussed in the context of the historic example. We discuss the design decisions underlying SEM. SEM is coupled with a Prolog API that enables users to create instances of events without going into the details of the implementation of the model. By a tight coupling to existing Prolog packages, the API facilitates easy integration of event instances to Linked Open Data. We illustrate use of the API with examples from the maritime domain.

Most digitised and online available objects from GLAMs (Galleries, Libraries, Archives, Museums) can be browsed through a predefined set of formal metadata, such as its creator, year of creation, and type of material. Standards for metadata management and exchange have matured and are being adopted widely. They enable intra-collection search and exploration, and are also main drivers behind supporting domain and cross-boundary access to collections. However, these formal metadata often do not give access to information pertaining to the content of the object, such as its topic, or what is depicted. This information is often given through textual descriptions which are mostly only accessible through keyword search. Keyword search is limited in the sense that it does not facilitate sorting, or retrieving objects whose descriptions contain terms that are synonymous to the search term. This paper provides results of an interdisciplinary research project, Agora, that is taking collection access one step further by enabling users to search and browse museum collections through the content descriptions of objects in a structured way. The three-year Agora project is funded by the Netherlands Organisation for Scientific Research and brings together computer scientists, cultural heritage experts, and humanities researchers.

Within cultural heritage collections, objects are often groun- ded in a particular historical setting. This setting can currently not be made explicit, as structured descriptions of events are either missing or not marked up explicitly. This poster reports a study on automatic ex- traction of an historical event thesaurus from unstructured texts. We also present a demo in which relations between events and museum ob- jects are visualised to accommodate event- and object-driven search and browsing of two cultural heritage collections. In this study, we investigate how historical events in unstructured text col- lections can be captured and modeled to create an event thesaurus for enriching metadata in cultural heritage collections. We adopt the SEM event model (8) to distinguish event types, actors, locations, and dates. We experiment with nat- ural language processing (NLP) techniques to extract event names and their associated actors, dates and locations. Additionally, we show how this resulting preliminary event thesaurus is employed in a new platform for event- and object driven search and browsing of the collections of the Rijksmuseum Amsterdam (RMA) and the Netherlands Institute for Sound and Vision (S&V).

Within cultural heritage collections, objects are often grounded in a particular historical setting. This setting can currently not be made explicit, as structured descriptions of events are either missing or not marked up explicitly. This paper reports a study on automatic extraction of an historical event thesaurus from unstructured texts. We show how this preliminary thesaurus accommodates event- and object-driven search and browsing of two cultural heritage collections.

Cultural heritage institutions are currently rethinking access to their collections to allow the public to interpret and contribute to their collections. In this work, we present the Agora project, an interdisciplinary project in which Web technology and theory of interpretation meet. This we call digital hermeneutics. The Agora project facilitates the understanding of historical events and improves the access to integrated online history collections. In this contribution, we focus on defining and modeling prototypical object-event and event-event relationships that support the interpretation of objects in cultural heritage collections. We present a use case in which we model historical events as well as relations between objects and events for a set of paintings from the Rijksmuseum Amsterdam collection. Our use case shows how Web technology and theory of interpretation meet in the present, and what technological hurdles still need to be taken to fully support digital hermeneutics.