Spreading Activation Way of Knowledge Integration

Autor:	Deepak Khemani, Shubhranshu Shekhar, Sutanu Chakraborti
Rok vydání:	2015
Předmět:	business.industry Computer science Recommender system Machine learning computer.software_genre Body of knowledge Knowledge-based systems Text processing Knowledge extraction Knowledge integration Spreading activation Artificial intelligence business Set (psychology) computer
Zdroj:	Mining Intelligence and Knowledge Exploration ISBN: 9783319268316 MIKE
DOI:	10.1007/978-3-319-26832-3_1
Popis:	Search and recommender systems benefit from effective integration of two different kinds of knowledge. The first is introspective knowledge, typically available in feature-theoretic representations of objects. The second is external knowledge, which could be obtained from how users rate or annotate items, or collaborate over a social network. This paper presents a spreading activation model that is aimed at a principled integration of these two sources of knowledge. In order to empirically evaluate our approach, we restrict the scope to text classification tasks, where we use the category knowledge of the labeled set of examples as an external knowledge source. Our experiments show a significantly improved classification effectiveness on hard datasets, where feature value representations, on their own, are inadequate in discriminating between classes.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::b3b307c8cea2e5ee00c9c0bee6d69203 https://doi.org/10.1007/978-3-319-26832-3_1 Zobrazit plný text záznamu