Name
Abstract | ||
|---|---|---|
Recently, studies about culturally-aware systems have arisen to address digitized culture. Among these systems those enculturated driven by cultural knowledge embed culture in their design. To deal with the specifics of cultural groups, the development of machine-readable cultural knowledge representations can provide a substantial help. In this research we present a process to build time-affordable, emic, conceptually-sound and machine-readable cultural representations. These representations originate from Cognitive Anthropology. They follow a three steps methodology: ethnographic sampling, individuals' personal knowledge elicitation and cultural consensus analysis. We use lexico-semantic relation extraction as a mean to automatically elicit knowledge structures. Their formalisation is achieved through Ontology Engineering. We conducted experiments to build three cultural ontologies in order to assess the whole process. It came out that with the lexico-semantic relation extraction technique, the best representations we can obtain are consensually-limited, incomplete and contain some errors. However, many clues indicate that these problems should be solved by using higher quality elicitation techniques. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1007/978-3-319-55970-4_8 | ARTIFICIAL INTELLIGENCE FOR KNOWLEDGE MANAGEMENT (AI4KM 2015): 3RD IFIP WG 12.6 INTERNATIONAL WORKSHOP, AI4KM 2015 |
Keywords | Field | DocType |
Cultural ontology, Cultural representation, Emic approach, Culturally-aware systems, Lexico-semantic relation extraction | Ontology (information science),Data science,Ontology engineering,Personal knowledge base,Cultural group selection,Computer science,Emic and etic,Artificial intelligence,Ethnography,Machine learning,Relationship extraction,Cognitive anthropology | Conference |
Volume | ISSN | Citations |
497 | 1868-4238 | 0 |
PageRank | References | Authors |
0.34 | 2 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jean Petit | 1 | 0 | 0.34 |
| Jean-Charles Boisson | 2 | 0 | 0.68 |
| Francis Rousseaux | 3 | 18 | 16.78 |