Abstract
Character generation in video games currently relies on game developers manually creating game characters which costs in time, effort and resources. Social media, in the form of blogs, microblogs, forums, wikis, social networks and review sites contain rich information about characters in video games that are not exploited for character generation. However, such information contained in various social media applications are disconnected from one another and are not structured or enriched that can be utilised for character generation. Semantic Web techniques provide ways of linking and enriching information contained in disconnected datasets. This enriched information can be used to build complete character models for generating new characters in video games. Moreover, a video game character knowledge graph can be constructed out of the semantically-enriched information that can be used not only for character generation in video games, but also in any application that requires information about video game characters. In this paper, we present our approach for exploiting social media platforms to create semantically-enriched character models. In particular, we present our Game Character Ontology (GCO) – a light-weight vocabulary for describing character information in video games – and our methodology for extracting and describing (using our ontology) game character information from social media platforms.
Highlights
Social media platforms, consisting of wiki-based systems, social networks, review sites, blog sites, and microblog sites, provide users with systems to create their own content and this resulted in the large amount of content currently available on the Web
In this paper we presented our game character ontology and extraction methodology for semantically annotating game character content from diverse Web content
This semantic information can be used for automatic game character generation and to build game character knowledge graphs
Summary
Social media platforms, consisting of wiki-based systems, social networks, review sites, blog sites, and microblog sites (amongst others), provide users with systems to create their own content and this resulted in the large amount of content currently available on the Web. The advantage of using metadata is that information is added with meaning whereby Web agents or Web enabled devices can process such meaning to carryout complex tasks automatically on behalf of users. Another advantage is that the semantics in metadata improved the way information is presented, for instance merging information from heterogeneous sources on the basis of the relationships amongst data, even if the underlying data schemata differ. The Semantic Web encouraged the creation of meta-formats to describe metadata that can be processed by machines to infer additional information, to allow for data sharing and to allow for interoperability amongst Web pages. The common format and recommended by W3C for Semantic data representation [13] is the Resource Description Framework (RDF)
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