Index The premise architecture was developed by the consortium based on the following processes: As mentioned above, the most important components are : Ontology component The main objectives for this component were:Depending on the scope of the ontology, the ontology can be classified as follows:The ontology component is composed of the following modules:The objective of this research is to present a middleware based on multi-agent that provides semantically enabled information for SME knowledge workers. This middleware is based on the European project E! 9770 PREMISES [1]. Companies and universities from two EU countries (Romania and Spain) are working to help small and medium-sized businesses make better use of their information spaces. Say no to plagiarism. Get a tailor-made essay on "Why Violent Video Games Shouldn't Be Banned"? Get an original essay An important feature of PrEmISES is its ability to couple with existing data systems used by small and medium-sized businesses and in this way to enhance with a semantic layer/engine. The engine is used to find organizational documents within companies and make searches more accurate through the use of ontologies. The main purpose is to help companies make better use of the available information space. The engine is semantically enriched, meaning it is searching for the specified words/queries plus semantically related concepts. In this article we present the PrEmISES architecture. We will present the main components and the main steps that were followed to develop the ontologies used for the ontology component. The objective of this research is to present a product, called PrEmISES, which is used to help SMEs, from all over the world, to exploit their information space. Nowadays, every large company has its own information management departments and dedicated software used for these purposes. These software framework implementations are not suited to the needs of small and medium-sized businesses, due to their smaller budget and small number of employees. The framework we are developing is capable of coupling with existing legacy data systems that are already used in SMEs. Through the use of ontologies, this framework implementation adds semantically enabled information integration and also provides employees with embedded work process, context-sensitive information services. In this article we explain the high-level architecture of the product, the benefits of the automated ontology generation process, and how we use these ontologies in semantic search. PrEmISES was selected among hundreds of innovation projects by Eureka Eurostars and has the support of the national funding agencies CDTI (Spain) and ANCS (Romania). The PrEmISES project is financed by the Eurostars program and will last 24 months with a total budget of 1.3 million euros. The project is led by Anova IT Consulting. The aim of the project is to help SMEs make better use of their information space. The project is developed by a consortium made up of members (from industry and academia) of four entities from Romania and Spain. PrEmISES is a market-oriented research and development project that will prototype an intelligent middleware solution aimed at supporting SME knowledge workers in their common tasks. Our software product can provide: technologies for automatic knowledge structuring that do not require the redundancy of huge sets of information multi-agent system for user profiling semantic provision ofcorporate knowledge formalized to employees (not only search and retrieval, but also logic-reasoning based on user profiling). In terms of market analysis, a specific category of target customers has been identified: medium-sized knowledge companies looking to improve their overall business. PREMISES will address current market shortcomings. The market currently offers several commercial tools to enable KM such as KM systems, database management systems, information repository facility technologies, data warehouses, and intranet and extranet knowledge portals. However, these technological solutions do not take into account the fact that KM practices in SMEs are more congruent with apprenticeship-based learning rather than the formal training typical of large companies. This means that, to be effective for SMEs, a KM system must be able to provide users with contextually relevant business knowledge (constant estimation of the worker's context with a task-centric view of the context). In other words, PrEmISES is marketed internationally as a software license. It was initially developed for medium-sized Spanish, Portuguese and Romanian companies looking to improve business performance through KM solutions. This solution provides enriched standard information processing mechanisms of legacy systems with a semantically enabled information integration layer. On the customer side, it is implemented through a cross-platform user interface focused on high usability and smooth workflow integration. The provision of this information layer follows the SaaS software distribution model. The aim of the PrEmISES project is to develop a system capable of helping SMEs to better exploit the available information spaces. Due to the fact that the number of employees eager to gain knowledge increases quickly, knowledge also becomes more distributed, is generated faster and in greater quantity. In the next section we present some general data on the premises and the advantages of premises over other knowledge management systems. In the second section we describe the main features and the high-level architecture of the assumptions. Our software is used in technical areas such as processing, information system, knowledge management, process management, IT and telecommunications technology. The framework is marketed internationally as a software license in the computer software and embedded software solutions market area. Most of the existing frameworks available in the market are streamed through huge data sets to identify what is important. SMEs deal with small to medium data sets. The advantage of PrEmISES is its ability to work with medium and even small datasets. PREMISES represents an innovative framework in the field of knowledge management solutions [4, 5, 6]. PREMISES is marketed internationally as a software license. It is initially aimed at medium-sized Spanish, Portuguese and Romanian companies looking to improve business performance through KM solutions. In other words, PrEmISES is a cost-effective solution for knowledge management, adapted to the real needs of European SMEs. Our software product that provides: technologies for automatic knowledge structuring that do not require the redundancy of huge information sets; a multi-agent system for user profiling; semantic provision of formalized business knowledge to employees (not only search and retrieval, but also powered logic-based reasoningfrom user profiling). Unlike large organizations with dedicated information management departments, SMEs face obstacles when attempting to leverage their information assets and perform sustained knowledge management (KM). Current solutions on the market are not suited to the need of SMEs to exploit knowledge without large financial and time-consuming efforts [10]. PrEmISES addresses this market gap and helps SMEs improve business performance. In [x] we presented quantitative research to determine the functional and non-functional requirements of the PrEmISES search engine. In that research, the most important characteristics of the premises were highlighted according to a survey that was completed by 60 people of different ages and genders. We took these opinions into account when we began the development of Premises. In other words, the premises were built as an easy-to-use framework, with an intuitive user interface and is capable of returning relevant results in a short amount of time. Besides that, based on the needs of our customers, we have developed our framework taking into account the implementation of high security features and the possibility of using local as portable software capable of running on many operating systems. In [2] a similar framework was presented for the medical sector. The article presents the creation of an ontology-based digital library. Through the use of ontologies the mentioned framework helps patients select articles relevant to their condition. The framework creates a personalized digital library with filtered medical literacy. The results and benefits are presented in this article taking the condition of asthma as an example. According to [x] Premises is designed to be a low-budget software with high-precision results thanks to its ontological component. In the same article, the high-level architecture of the project was presented, focusing on the domain ontology development process used by the ontology architecture. Premises was developed by the consortium based on the following processes: Initial process scan (for a in-depth understanding of small and medium-sized enterprises) Analysis of the Social Subsystem Analysis of the Technical Subsystem Analysis Interpretation Solution Design Implementation. As mentioned above, the most important components are: Search and administration system component Analysis and indexing system component Ontology component Each of these three components has been built with a complex design and each of them is responsible for a task specific in the overall architecture. Due to their complexity, components were developed by building and integrating many subcomponents. Ontology Component The main objectives of this component were to: Generate domain ontologies Develop queries based on the developed ontologies An ontology defines a common vocabulary for researchers who need to share information in a domain. It includes machine-interpretable definitions of the basic concepts in the domain and the relationships between them. The purpose of creating ontologies is also the reuse of knowledge. Once the ontology for a domain is created, it should be (at least to some extent) reusable for other applications in the same domain. To simplify both ontology development and reuse, modular design is advantageous. The modular design uses ontology inheritance: higher ontologies describe general knowledge, and application ontologies describe knowledge for a particular application. Depending on the scope of the ontology,.