Automatic Heritage Metadata Enrichment With Historic Events | Museumsandtheweb.com
Enriching museum metadata with historical events
Most digitized 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 enable intra-collection search and exploration, and are also the main drivers behind supporting domain and cross-boundary access to collections. However, these formal metadata often only give limited access to information pertaining to the content of the object, such as its topic, or what is depicted. In many cases, institutions have compiled vocabularies containing terms and concepts to categorize their collections. However, these vocabularies are often a) not standardized, and thus not usable across collections, and b) compiled ad-hoc, and thus biased to the collection and/or incomplete (Gilchrest, 2001). Often, additional information about the object and its topic is given through textual descriptions. These descriptions can be quite elaborate and rich in information, but they are mainly accessible only through keyword search. This is limited in the sense that it does not facilitate sorting, or retrieving objects whose descriptions contain terms that are synonymous or otherwise related to the search term.
We showcase an approach to enrich collection metadata with event information through linking it to an automatically built historical event thesaurus. Historical events often have a central place in the description of collection objects; e.g. they may depict an event, or an object with a relevance to an event, or they could have been commissioned to commemorate an event. Furthermore, events are multidimensional, requiring who, what, where and when. Therefore, collection objects can be linked through each of these dimensions, greatly increasing the number of relationships between objects and collections. We use, among others, techniques from text mining to achieve this.