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The ontology-based approach to creation of personal learning collections implies utilizing the ontologies for modeling the learning course data domain, the learner profile, the learning resources and the personal learning collection. The data domain of learning course is described by competencies (knowledge, skills, abilities) which the student should acquire as a result of the learning process within the learning course or some part of it. The knowledge field is represented as a set of knowledge domain concepts and relations between the  concepts. The learner profile is described using the current and outcome competencies (knowledge, skills, abilities) of the learner which are defined on the same domain. It also includes individual learner properties such as preferred languages, current and outcome knowledge level/comprehension etc. Each learning resources is annotated with competencies (knowledge, skills, abilities) which the learner can get using this resource, and prerequisite competencies which he should have before using this resource. These properties are defined on the same domain along with some specific properties (resource name, authors, resource location and type, language, knowledge level, didactic role etc.). Annotated in this way learning resources can be collected into in  open repositories for further use. The meta-ontology for retrieval and integration of learning resources into in  the personal collections was developed to integrate and manage the domain ontologies. The formal models of ontologies mentioned above are described in detail in \cite{Anikin_KBSE2014}. The new two-stage method for electronic learning resources retrieval and integration based on ontology reasoning rules was developed which includes the following main stages:  1st stage: search for a  fragment of semantic net which is relevant to search query. The search is performed using inference engine on the base of semantic rules defined in the ontology as a set of Horn clauses. To implement this stage the semantic rules were formulated for parametrized search on the following parameters: preferred language; outcome learner competencies; mastering level of competences; synonymy of the terms in subject domain. 2nd 2d  stage: redefining the subnet  obtained at the first stage subnet. stage.  To implement this stage the semantic rules were formulated to search the additional resources on the base of current learner competencies, and search the auxiliary resources, as well as the rules for defining the logical links between learning resources in personal collection. Auxiliary resources are the resources which are not included in the set of additional resources because of too long learning sequence but these resources can help to get the missing competences which are not provided by the set of additional resources. 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. 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.  \section{Software Architecture. Implementation Aspects}  To create the personal learning collections using the proposed ontologies method  and method, developed ontologies,  it is necessary to use appropriate software tools, providing the logic reasoning on ontologies (reasoning engines). To implement the logical reasoning the reasoning engine RacerPro (Racer Systems GmbH and Co. KG, Germany) was used. This engine supports the ontologies described with OWL language and reasoning rules described in SWRL. It also can work as server with both DIG (DL Implementors Group) interface and TCP/IP interface with internal queries language nRQL. DIG is a application interface to interact with the reasoning engine that also allows to work with OWL-ontologies and interact with other software tools like ontologies editor Protégé. However this interface has some restrictions, including impossibility to work with SWRL reasoning rules, so the TCP/IP interface was used to implement the SWRL-reasoning. RacerPro (Fig. \ref{fig:racer}) allows to load ontologies and ontology-based annotations in OWL (1-5) - Learning course domain ontology, Learning resource ontology, Learner profile ontology, Personal learning collection ontology, resources annotations within Learning resources repository - that can also include SWRL reasoning rules. Personal collection builder module interacts with the RacerPro reasoning engine, it generates requests for the OWL files load, forward chaining reasoning and nRQL-request for the ontology (7) and gets back the corresponding results from the RacerPro (6). On the base of the received results the Personal collection builder generates the learning resources presentation for the learner as the Personal learning collection (8) and provides the ability for learning management on this collection.