One of the biggest challenges is information extraction on historical events from different textual data. We will start by identifying the ‘bigger’ events that have been deemed important enough to receive a proper name (e.g., French Revolution, Second World War). As the behaviour of references to this type of events is similar to other named entities, we are recasting the identification of these event labels as an named entity recognition task[9]. In order to detect accompanying actors, locations and temporal information and the relations between these, as well as smaller events that do not have a proper name we will first employ state of the art named entity and term recognition techniques[10], followed by relation finding[11].

Even our central concept, event, is treated differently by the parties involved in our project; in the history domain, the notion of an event has been defined as “what agents make happen or undergo”[2]. This has the implication that actions are a species of events. Furthermore, events are concrete particulars. They are unrepeatable entities with a location in space and time[3]. Within computational linguistics, the notion of event is often not defined, and if defined, the definition is mostly pragmatic and broad to ensure reusability across different domains. Another difference is that in computer science ‘event extraction’ often does not stretch beyond the literal task of iden- tifying event labels, participants, locations and time stamps, whereas historians are interested in the interpretation of events. Computer science also considers events mostly as separate entities, whereas historians consider events in their connection with other events. The significance of an event depends on this connection and is usually expressed in the form of a narrative[4]. [...] Although there is no consensus on the definition of event in computer science, most event modelling approaches share the charac- teristic that they want to model: Who does what, where and when?. We take this as at least the minimal requirement an event modelling approach should be able to express. Once a minimal event definition has been developed, we can start to think about modelling additional aspects that play a role in our domain and are closely related to events, namely: granularity, interpretation, perspective, and causality. We are currently investigating the use of the simple event model (SEM) to model historical events[5]. SEM aims to provide the minimal set of classes to describe events, minimising possible clashes between different domain-specific event definitions.

In our case, we are dealing with the question of how museums can present the specific information that belongs to objects in their collection in a way that strengthens users’ historical understanding and involvement in relevant historical debates. We are also ask- ing ourselves how we can prevent users from ending up perusing the collection in a zapping-like, incidence-based viewing that will only lead to a confirmation of his or her preconceived views and insights as no relations between objects are presented that are novel or surprising to the user.

The Agora project started in October 2009 with the aim of facilitating context-driven browsing and search in heterogeneous museum collections. The context that unites these collections is pro- vided by historical events that can be linked to the collection objects, as historical event-descriptions are comprised of causal language, locations, the actors involved and the time of the event. [..] The goal of Agora is threefold: (1) a historical event thesaurus linked to museum artefacts, (2) a semi-automatic event modelling approach that satisfies both the needs of experts and the general public, and (3) an online social platform in which both the general public and expert historians can explore various perspectives on events, build their own narratives, and contribute to the evolution of the event thesaurus [..] The results of both Agora and Semantics of History will be deployed in a social cultural heritage platform that will allow different users (e.g., experts, interested laypersons and secondary school students) to have event-based access to the S&V and RMA collections.

A clear definition of the shared terminology is a prerequisite for successful multidisciplinary collaboration. This need is particularly pressing within collaborative projects in the field of cultural heritage and computer science such as ours as each field has different definitions and theories about shared concepts.

There is a vast amount of historical knowledge locked in museum collections. This knowledge is often explicitly present in textual descriptions accompanying museum objects or implicitly present through the fact that an object belongs to a particular collection and was collected for a particular purpose. In this sense, objects from one museum collection only tell part of the story, as they present a view on the past from one only perspective, limited to their collection. Through combining objects from different collections, a more comprehensive view of a certain historical period can be given. When unlocked, this knowledge can help casual users understand the significance of museum objects and historical events better and aid experts (curators, art historians and historians) in their search for objects relevant to a topic.

We argue that collection access through events can remedy these bottlenecks as events provide the context that can link a variety of objects together, providing a more comprehensive overview than facets. To facilitate cross-collection access, we aim to develop an event thesaurus with which different collections can align their internal thesauri. [...] We envision two types of exploration: object-driven and event-driven exploration. Object-driven exploration involves a search or browsing activity where the user starts by selecting an object from the collections and subsequently finds new objects and events through the relations with the first object. In the event-driven exploration, the user starts by selecting an event and builds a sequence of related events and objects. As a user may hop between events and objects on his or her search through the collections the object-driven and event-driven explorations alternate.

Although museum collections and their accompanying information are becoming available in digital forms, search is often limited to keyword search and browsing through predefined facets[1]. These access methods are not optimal; in keyword search, for example, it is not clear how the retrieved results are related to each other as they are simply presented in a list. Specifying a search query through facets resolves this problem, as it enables users to specify which relations they find relevant. However, facet browsing is often limited in that there is usually one set of facets that may not provide sufficiently finegrained access to all artefacts. Most museum collections, for example, are searchable through facets that describe meta-data that is available for every object such as title, year, artist, technique, dimensions and object id, but users may also want to search via locations that play a role in the object (for example because a location is depicted in the artwork, or because the birthplace of the artist might be relevant)