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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 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 and method, 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.