Abstract
The development of the field of digital humanities in recent years has led to the increased use of knowledge graphs within the community. Many digital humanities projects tend to model their data based on CIDOC-CRM ontology, which offers a wide array of classes appropriate for storing humanities and cultural heritage data. The CIDOC-CRM ontology model leads to a knowledge graph structure in which many entities are often linked to each other through chains of relations, which means that relevant information often lies many hops away from their entities. In this paper, we present a method based on graph walks and text processing to extract entity information and provide semantically relevant embeddings. In the process, we were able to generate similarity recommendations as well as explore their underlying data structure. This approach was then demonstrated on the Sphaera Dataset which was modeled according to the CIDOC-CRM data structure.
Highlights
The term knowledge graph was first coined by Google in 2012, defining a large database connecting things, not strings, through different types of relations
We considered the CIDOC-CRM knowledge graph as G = (V, E), where G is the entire knowledge graph, V represents the set of nodes in this graph, which in this case is represented by all entities, and E represents the set of directed edges—in this case, relations connecting the entities within G
How do we judge whether two editions are alike? How do we decide whether two editions are dissimilar? To what degree are they similar or dissimilar? These measures remain abstract when dealing with a humanities dataset, whether we are dealing with the Sphaera dataset or dealing with data whose notion of similarity is not inherent, or not specified
Summary
The term knowledge graph was first coined by Google in 2012, defining a large database connecting things, not strings, through different types of relations. With the recent developments in information technology, the field of digital humanities has been keen on curating and developing humanities knowledge graphs, taking advantage of openly available knowledge graphs such as DBpedia [10] and Wikidata [11] to enhance their databases. Such knowledge graphs are being constructed and published according to linked data principles [12]. This increase in knowledge graph use within the digital humanities community opens the door for standardization efforts such as the one presented by the International Committee for Documentation—Conceptual
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