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pdf:docinfo:title: CIDOC2VEC: Extracting Information from Atomized CIDOC-CRM Humanities Knowledge Graphs
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Keywords: data extraction; knowledge graph; CIDOC-CRM; digital humanities; Sphaera; recommender system
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subject: 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. 
dc:creator: Hassan El-Hajj and Matteo Valleriani
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title: CIDOC2VEC: Extracting Information from Atomized CIDOC-CRM Humanities Knowledge Graphs
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pdf:docinfo:keywords: data extraction; knowledge graph; CIDOC-CRM; digital humanities; Sphaera; recommender system
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dc:title: CIDOC2VEC: Extracting Information from Atomized CIDOC-CRM Humanities Knowledge Graphs
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cp:subject: 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. 
pdf:docinfo:subject: 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. 
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pdf:docinfo:creator: Hassan El-Hajj and Matteo Valleriani
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creator: Hassan El-Hajj and Matteo Valleriani
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dc:subject: data extraction; knowledge graph; CIDOC-CRM; digital humanities; Sphaera; recommender system
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meta:keyword: data extraction; knowledge graph; CIDOC-CRM; digital humanities; Sphaera; recommender system
Author: Hassan El-Hajj and Matteo Valleriani
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