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An automatic document classifier system based on ontology and the naive Bayes classifier is proposed in this paper. The main concept is to first establish a keyword synonymous table by experts for narrowing down the range and getting the consistency of keywords. The formal concept analysis is then used for
Most of the current information retrieval systems are mainly based on full text matching of keywords or topic-based classification, often return a large number of irrelevant information, and are unable to meet the user's request. Ontology-based semantic retrieval is a hot issue in current research. In this paper, the
Currently, Web of Things is based on keyword matching which is not beneficial to the development regarding Web of Things. Accordingly, "Semantic Web of Things" is proposed. As far as Semantic Web of Things concerned, the information of things should be represented as ontology-based semantic annotation
In this paper, we present a method we implemented to help a user index documents (and, in particular, learning objects) according to a given set of concepts (terms referring to domains or topics). The user first associates keywords to the concepts. Our method uses such associations to suggest simple rules for indexing
comprises of several components; (1) using a Stemming algorithm for text processing, (2) Formal Concept Analysis for dynamic extraction of keywords, (3) Ontology based concept extraction, (4)Google API is used to query the Google Image Database and extract the required multimedia elements, which are then mapped accordingly. A
Traditionally information retrieval consists mainly of determining which documents of a collection contain the keywords in the user query. However, a growing number of tasks, especially those related to Semantic Web technologies and applications rely on accurately measuring the similarity between documents and online
Web page recommendation model traces userspsila Web-surfing trails, extracts the useful information including keywords, Web page URLs and userspsila evaluations on Web pages, and automatically generates FCA (formal concept analysis) knowledge base and enterprise ontology knowledge base with WordNet. While users are
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