Providing semantically enabled information

Software applications, Digital Era

The objective of this research is to present a Multi-agent-based middleware providing you with semantically-enabled info for SmES knowledge personnel. This middleware is based on the European task E! 9770 PrEmISES [1]. Businesses and universities from two EU countries (Romania and Spain) will work in order to help small and channel enterprises to better exploit their information spots.

A single important feature of Areas is its capability to few with the existing data devices that are used by small and medium companies in addition to this way to boost them with a semantic layer/engine. The engine is used to find organizational documents within companies and make the searches better by the use of ontologies.

The key purpose should be to help the companies to better make use of their readily available information space. The engine is semantically enriched meaning that it is searching for the specified words/query plus to get semantically related concepts.

In this conventional paper we present PrEmISES structures. We will show the main pieces and the key steps that had been followed to be able to develop the ontologies which were used for the ontology part.

The purpose of this research is to present a product or service, named Property, that is used to be able to help SMEs, from all over the world, to exploit all their informational space. Nowadays, every single large company has its own info management departments and dedicated software employed for these purposes. These software framework implementations are not appropriate for the demands of small and medium corporations, due to their lower budget and their smaller quantity of employees. The framework we are expanding is able to couple with the existing legacy info systems that are already utilized within SME companies.

By the use of ontologies, this platform implementation gives semantically allowed information integration and also provides for the employees a work process stuck, context-sensitive info services.

In this daily news, we explain the products high-level architecture, the benefits of ontologies’ automated era process and how we work with these ontologies in semantic search. PrEmISES has been picked among numerous innovation jobs by Eureka Eurostars and counts with all the support from the National Money Agencies CDTI (Spain) and ANCS (Romania).

Areas project can be funded by the Eurostars System and will previous 24 months which has a total finances of 1, 3M¬. The project is led by Anova IT Consulting. The aim of the project is always to help SMEs to better take advantage of their details space. The project can be developed by a consortium that is formed of members (from industry and academia) of 4 entities from Romania and Spain. Areas is a market-oriented RD project which will prototype an intelligent middleware solution geared towards supporting SME’s knowledge staff with their common tasks. Software product provides:

• technologies intended for the programmed knowledge building that avoid need the redundancy of huge info sets
• multi-agent system for an individual profiling
• semantic provisioning of official company knowledge to personnel (not just searching and retrieving, yet also logic-based reasoning given by the customer profiling).

Regarding market research, a specific category of target client has been identified: medium size knowledge companies who wants to boost their overall business. PrEmISES is going to tackle the current market lacks. Market at present offers a lot of commercial tools for allowing KM including KM devices, database management devices, technologies for facts repositories constructions, data stockroom and intranet and extranet knowledge sites. However , these types of technological solutions do not remember the fact that KM methods in SMEs are more congruous with apprenticeship-based learning as opposed to the formal teaching typical of big companies. Therefore, in order to be effective for SMEs a KM system has to be able to provide relevant business knowledge to users depending on context (constant worker context estimation with an activity centric view of context).

In other words, PrEmISES is internationally commercialized like a software permit. First it absolutely was developed pertaining to medium-size Spanish, Portuguese and Romanian businesses willing to improve business efficiency through KILOMETERS solutions.

This solution endows enriched standard information processing systems of legacy systems which has a semantically-enabled info integration level. On the client side it really is implemented through a multi-platform ui focusing on substantial usability and smooth work flow integration. The provision of the information part follows the SaaS software program delivery model.

The aim of the Areas project is always to develop a program capable of helping SMEs to better take advantage of their readily available information places.

Due to the fact that the number of staff that are desperate to gain know-how is elevating fast, expertise also turns into more given away, it is made faster and a higher amount. In the next section we present some basic data of premises and premises positive aspects over different knowledge managing systems. The other section all of us describe the key functionality and premises high level architecture.

Our software can be used in technical areas like processing, data system, expertise management, procedure management, THAT and telecoms technology. The framework is usually internationally released as a application license available in the market area of computer programs and built-in software solutions.

Most of the existing frameworks that are offered on the market relay in enormous sets of date in order to identify what is important. SMEs are coping with small or medium units of data. The main advantage of PrEmISES is definitely its capability to work with moderate and even small data units. PrEmISES represents an innovative framework in the area of expertise management solutions [4, 5, 6]. PrEmISES is usually internationally launched as a software program license. It addresses at first the medium-size Spanish, Costa da prata and Romanian enterprises ready to improve organization performance through KM solutions. In other words, PrEmISES is an inexpensive knowledge management solution personalized on the real needs of European SMEs. Our software product that provides:

• technologies for the automatic understanding structuring that don’t require the redundancy of huge information models
• a multi-agent system for the person profiling
• semantic provisioning of formalized company know-how to employees (not just searching and retrieving, although also logic-based reasoning given by the customer profiling).

Contrary to big organizations with dedicated information managing departments, SMEs face obstructions when looking to exploit their information solutions and execute sustained know-how management (KM). The actual alternatives on the market are not suited for the SMEs want of taking advantage of knowledge without big financial and time-consuming efforts [10]. Property solves this market gap that help SMEs to improve business overall performance.

In [x] we certainly have presented a quantitative study for deciding the practical and nonfunctional requirements of the PrEmISES internet search engine. In that exploration there were highlighted the most important features for premises according to a survey that was finished by 70 persons of numerous age and gender. We-took into account those opinions whenever we have began the development of Areas. In other words, the premises was built as a easy to use structure, with a friendly UI in fact it is capable to return relevant brings about only a quick amount of time. More than that, based on the customers want, we have developed our construction by taking into account the implementation of high security features plus the possibility to work with premises as a portable application that is capable to run on a large number of operating systems.

In [2] a similar construction was presented for the medical website. The article gives a digital library creation depending on ontologies. With the use of ontologies the mentioned construction is aiding patients to choose relevant content articles for their condition. The construction creates a personal digital catalogue with filtered medical literacy. The results and advantages are provided in this article by taking as example the asthma condition.

According to [x] Property is intended to be a minimal budget software with substantial accuracy benefits due to its ontological component. In the same article the high-level architecture of the project was presented, focusing on the domain ontology creation process utilized by the ontological.

Premises structure was developed by consortium based on the following procedures:

• Initial Method Scanning (for a profound understanding of small and medium businesses realities)
• Social Subsystem Analysis
• Technical Subsystem Analysis
• Analyses Meaning
• Remedy Design
• Implementation.

As mentioned above the most crucial components will be:

• Search Management System Aspect
• Inspecting Indexing System Component
• Ontology element

Every of those three components were built with a complex design and style and each of them is responsible for a unique task inside the overall buildings. Due to their complexness the components had been developed by building and adding many sub-components.

Ontology aspect

The main aims for this element were:

• Site ontology generation
• Producing queries based upon the ontologies that were created

A great ontology specifies a common language for researchers who need to talk about information in a domain. It includes machine-interpretable explanations of simple concepts inside the domain and relations most notable.

The objective of authoring ontologies is also reusing of knowledge. Once ontology is made for a site, it should be (at least to many degree) reusable for different applications in the same domain name. To simplify both ontology development and reuse, do it yourself design is helpful. The flip design uses inheritance of ontologies upper ontologies identify general knowledge, and application ontologies describe know-how for a particular program.

Depending on the opportunity of the ontology, ontology can be classified the following:

• upper, general, top-level ontology describing general knowledge, such as what is time and precisely what is space
• website ontology describing a domain, including medical domain name or electric powered engineering site, or narrow domains, just like personal computers website
• task ontology suitable for a unique task, just like assembling parts together
• application ontology created for a certain application, such as assembling personal computers

At each level modularization can be used as well for example , upper ontology may possibly consist of segments for actual numbers, topology, time, and space (these parts of the top ontology are usually called general ontologies). Ontologies at lower levels transfer ontologies from upper amounts and add added specific expertise. In this way, ontologies form a lattice of ontologies described by partial ordering of inheritance of ontologies. Activity and domain name ontologies can be independent and are also merged pertaining to application ontology, or it is which for example activity ontology imports domain ontology. The upper ontologies are the the majority of reused ones while application ontologies can be suitable for 1 application only.

The moment developing fresh ontology, it is desirable to reuse existing ontologies whenever you can. The new ontology should be started by adding upper level ontologies when ever appropriate ontologies exist. This will simplify the expansion since one can focus in the domain or perhaps application specific knowledge only. It will also simplify integration among applications down the road since defined parts of ontologies will be distributed.

The ontology produced by the ontology generation part will be used in PrEmISES to be able to tag documents in the record repository with relevant ideas in the certain domain where the enterprise can be operating. This means that the type of ontology produced by the component is actually a domain ontology. For more intricate queries the domain ontologies can be in-line to existing upper ontologies (such because Dolce, Cyc).

The ontology technology component is liable for creating the business specific domain name ontologies that is used for indexing the company documents and for constructing semantic questions. Another reason for the aspect is to get subontologies for each user profile in order to support context-aware semantic searching.

The ontological component is composed from the following modules:

• Crawlers the group of dedicated spiders for structured data, semi-structured data, unstructured data which will gather the input info for the ontology novice component.
• Ontology spanish student Discovers ideas, instances and properties in the data accumulated by the crawlers and stores them in RDF/OWL formatting.
• Ontology editor Allows ontology experts to make changes to the generated ontology within a lightweight aesthetic editor.

Subontology extractor Uses data from your company end user profiles and usage data to extract context-dependent subontologies. The come ontologies will be stored in an RDF data source which is devoted for saving triples and answering to SPARQL queries. One of the most applied RDF sources is Sesame.

The ontologies that we get developed in [x] will be validated applying queries intended for knowledge removal. We have used Sparql in order to test and validate our ontologies. SPARQL identifies queries in terms of graph habits that are matched against the described graph addressing the RDF data.

SPARQL consists of capabilities to get querying essential and optionally available graph habits along with their conjunctions and disjunctions. The result of the match can also be used to construct fresh RDF charts using individual graph patterns.

Another important tool that we get used is usually Sesame. Sesame is an open source Java framework pertaining to processing RDF data. This includes parsing, holding, inferencing and querying of/over such info. It offers a great easy-to-use API that can be linked to all leading RDF safe-keeping solutions. That allows us to interact with SPARQL endpoints and create applications that leverage the potency of linked info and Semantic Web.

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