deletions | additions       diff --git a/The_formal_models_of_ontologies__.tex b/The_formal_models_of_ontologies__.tex  index ed5632f..91c4c38 100644  --- a/The_formal_models_of_ontologies__.tex  +++ b/The_formal_models_of_ontologies__.tex 
...
The developed set of semantic rules allows to create the personal learning collection in accordance with the learning outcomes, current knowledge level and other preferences of the learner. This collection includes the relations between the learning resources to manage the learning process. To represent the ontology reasoning rules for learning resources retrieval and integration we used SWRL language. SWRL extends the OWL-DL and is based on first-order predicate calculus that allows to represent the reasoning rules as a set of Horn clauses.   Below are a few examples of semantic rules. The SWRL-rule for learning resources retrieval based on the preferred language has the following form:  \begin{multline}\label{eq:5} \begin{multline}\label{eq5}  COL:hasStudent(?c, ?u) \wedge L:hasLanguage(?u,?l) \wedge \\   \wedge ELR:hasLanguage(?r, ?l) \rightarrow \\  \rightarrow COL:hasResourceByLanguage(?c, ?r), 
...
$COL:hasStudent, L:hasLanguage, \\ ELR:hasLanguage, COL:hasResourceByLanguage$ -- ontology relations with ontology prefixes.  The SWRL-rule for learning resources retrieval based on the outcome learner competencies with allowance for the synonymy has the form:  \begin{multline}\label{eq:6} \begin{multline}\label{eq6}  COL:hasStudent(?c,?u)\wedge \\  \wedge L:hasIntentionalDataDomain(?u,?d) \wedge \\  \wedge DD:hasCompetence(?d,?cmp1) \wedge \\ 
...
\wedge DD:is(?cmp2, ?cmp) \wedge DD:is(?cmp1,?cmp) \rightarrow \\  \rightarrow COL:hasResourceByIntentional-\\competencies(?c,?r).  \end{multline}  So, the rules ~(\ref{eq:5}, \ref{eq:6}) ~(\ref{eq5}, \ref{eq6})  define the subset of learning resources that comply with the learner's language and outcome competencies. The developed two-stage method solves efficiently the problem of creating the personal learning collection as a result of search and integration of relevant learning resources at the expense of application of ontological model for knowledge representation and semantic rules for inference on ontology.