Research into language learning strategies began in the 1960s mainly influenced by the developments made in cognitive psychology (Williams and Burden, 1997). Keywords: Vocabulary learning strategy EAP students EFL majors Selection and peer-review under responsibility of Urmia University, Iran.
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© 2014 TheAuthors.Published byElsevierLtd.Thisis an openaccessarticle under the CC BY-NC-ND license The implications of the study are discussed in detail in the paper. However, running Chi square analyses, significant differences were found in individual strategy use in 7 out of 45 strategies. The results of independent samples t-tests indicated that, overall, the two groups were not significantly different in the choice and use of vocabulary learning strategies. The data collection instrument adopted was a validated Likert-scale structured questionnaire. The present study set out to investigate vocabulary learning strategies adopted by 173 undergraduate students (89 EAP students and 84 EFL majors studying at Bu-Ali Sina University-Hamedan, Iran). Hassan Soodmand Afshara' *, Ismail Moazzamb, Hassan Radi ArbabicĪ' b'c Bu-Ali Sina University, Hamedan, 651783695, Iran International Conference on Current Trends in ELTĪ Comparison of Iranian EAP Students and EFL Majors on the Use of Vocabulary Learning Strategies ResultSetFormatter.out(System.Procedia - Social and Behavioral Sciences 98 (2014) 1828 - 1835 QueryExecution qe = QueryExecutionFactory.create(query, m) Model m = ModelFactory.createOntologyModel( OntModelSpec.RDFS_MEM_RDFS_INF, ont ) Model ont = FileManager.get().loadModel( ontFile ) Model source = FileManager.get().loadModel( rdfFile ) I would change your example as follows (note: I haven't tested this code, but it should work): String rdfFile = ". You can do this through OntModel's sub-model factility. To reason over two models, you want the union.
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Update (following edit of original question) Be aware that large and/or complex models can make for slow queries. If you want a different reasoner, or OWL support, you can select a different OntModelSpec constant. While you can create inference models directly, it's often easiest just to create an OntModel with the required degree of inference support: Model model = ModelFactory.createOntologyModel( OntModelSpec.RDFS_MEM_RDFS_INF ) Thus you only need to change the first line of your example, where you create the model initially. An inferencing model is just a Model, in which some of the triples are present because they are entailed by inference rules rather than read in from the source document. The key is to recognise that, in Jena, Model is the one of the central abstractions. Update: now i have tow models : from which i should run my query ? QueryExecution qe = QueryExecutionFactory.create(query, ?)
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So how to link the ontology to my query? please help me. ResultSetFormatter.out(System.out, results, query) Īnd i have a wordNet Ontology in rdf file and i want to use this ontology in my query to do Inferencing automaticly (when i query for person the query should return eg. QueryExecution qe = QueryExecutionFactory.create(query, ?) Query query = QueryFactory.create(queryString) Model onto = ModelFactory.createOntologyModel( OntModelSpec.RDFS_MEM_RDFS_INF) Model model = ModelFactory.createMemModelMaker().createDefaultModel()
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I have some rdf & rdfs files and i want to use jena sparql implementation to query it and my code look like : //model of my rdf file
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