AL!SE: the smart semantic processing engine

What is AL!SE?

AL!SE is a semantic data model based on years of research and development in the field of semantic web. While the Semantic Web tries to simplify the identification and exchange of information throughout the web using RDF approaches (= Resource Description Framework), AL!SE aims to make these smart mechanisms available professionally and in a wide variety of contexts for closed information systems deliver. On a functional level, this enables:
  • precise identification, storage & search of data
  • processing of data into meaningful information
  • strategic distribution of the relevant information
In this way, AL!SE realizes special application possibilities of the universal principles of semantic technology – each adapted to the requirements and needs of specific information systems.

Why AL!SE?

The development of AL!SE followed the realization that digitization created the technical prerequisites to collect ever larger amounts of data, but there is a lack of tools / systematic methods to process this data in a meaningful way. Especially in context of closed information systems, too many resources are wasted (studies speak of up to 30 percent) in the constant search, formatting and unsystematic dissemination of information. The technical developments (with regard to storage media, applications and process optimization) do not keep the promises made to them to make work easier in everyday life, but on the contrary lead to frayed, unsystematic, slowed down processes. This is especially true for the handling of data. AL!SE provides a practical, quick and easy to implement solution for this in the form of a semantic data model.

How is this done by AL!SE?

AL!SE systematically inserts data into an ordering reference structure. In principle, other data models also do this: But in contrast to conventional data structures, a semantic data structure not only inserts the individual data into a hierarchical, one-dimensional order pattern, but also creates a meaningful order. This is done by linking the available data with a targeted metadata structure. By using a metadata structure, certain properties, but also functions and connection options for the data described in more detail are made clear. Based on the linguistic dimension of the term semantics: By assigning metadata, data is enriched with meaning. In computer science, this is referred to as a transformation from files to siles (= semantically enriched files). That means: Individual, unconnected data become targeted information carriers.

How does data become information?

AL!SE uses a fully developed structural model to insert the existing data into a formulated reference system of metadata:
  • Classifications
  • Attributes and
  • Connections
The basis of the semantic structuring already exists in the form of the data model: It only has to be adapted to the respective requirements of the information system (i.e. type of data storage, data structure, data distribution). This is done through an individual adaptation of the semantic spelling. In terms of grammar, orthography means the sum of the rules for making meaningful statements from data. The big advantage here: since the structure only needs to be adapted and not specially programmed Development, implementation and implementation of the semantic data model can be realized with only a fraction of the otherwise necessary resources (time, energy, costs) compared to conventional software solutions. The linguistic origin of the semantic term is particularly clear in the type of semantic data links. This is because these are syntactically formed in the form of subject-predicate-object constructions. Individual characters (= files) are thus set in a certain relationship (= predicate) to one another through the orthography (= syntactic law of formation) and thus form a meaningful statement.

Data becomes statements

In computer science, subject-predicate-object constructions are simply visualized using graphs: In the example, file 1 is assigned to the subject class by the assignment of a metafile, and file 2 to the object class. Both are linked by a targeted, specially defined link: the predicate. In the regulated combination of these elements, the files form a statement; e.g .: The man lives in the house. The individual dates can bein our example, of course, describe it further (e.g. using attributes) or link it with other elements (e.g. other objects) to form more complex statements; e.g .: The little man lives in the blue house that is in the city. Individual data thus acquire the ability to “speak” through logical links (in a further step, to “speak” automatically). The embedded structure transforms them into meaningful, meaningful information. In more complex information systems, this type of semantic data modeling leads to multi-dimensional network structures that are not only based on human language, but also on the basic mechanisms of human thought. This reveals points of contact between semantic technology and the field of artificial intelligence. This leads to an intuitive and user-friendly approach between purely logical information systems and natural language. And at the same time, in a practical sense, this creates a technical basis for (semantic) search, filtering and distribution of data.

How does AL!SE work?

From a technical point of view, the AL!SE data model works like a large construction kit. AL!SE consists of individual modular components that can be combined in a wide variety of ways – depending on the systematic requirements of the practical application. However, the individual components are already fully operational and only need to be adapted. The individual elements:
  • LETO, the heart of AL!SE: a semantic server that already contains the basis of the metadata structure, i.e. a well-defined structure of classifications, attributes and links. Due to this basic structure of Siles (i.e. semantic files), LETO enables the development of all special ontologies in practice and thus defines the specific semantic storage and organizational forms of the data. LETO thus functions as a functional framework that is implemented as an application in the context of specific information systems.
  • AGLAIA lies above LETO: the interface of the semantic data model. As an interface, AGLAIA takes on the function of an interface and, in the role of a translator, enables LETO to communicate efficiently with all other internal and external components (e.g. other servers or clients).
  • HEPHAISTOS describes a certain part of LETO, which as an additional function on the server establishes a further communication level between LETO and AGLAIA and enables natural language recognition in the sense of a search assistant – e.g. a semantic search query to LETO in the form of a complete question. In reverse, HEPHAISTOS constitutes the basis of a semantic text analysis – that is, the automated breaking down of texts into their individual building blocks of meaning.
  • CHARON, in turn, describes a service component that is responsible for synchronizing data. As an independent function, CHARON ensures a data comparison between LETO and specific clients and is expressed in specific information systems, for example in the form of an automated upgrade function.
This (only small) excerpt of components is supplemented by the actual connection to differently configurable distribution structures. Coupling with individual mobile and / or web-based clients is possible, including a connection to an app, as well as the bundling of several clients via the interconnection of one or more peer servers. Thanks to the numerous interfaces created in the system, various differentiated forms of distribution can be implemented without any problems – from data protection barriers to specially defined access rights in the form of licensed subscription models, etc. The systemic openness of the data model and the full-stack programming in JAVA also allow a very quick connection to all relevant operating systems (Windows, Linux, iOS, etc.) as well as to differently designed user interfaces. The advantages of the modular semantic data model are shown in the extremely high degree of adaptability. As described, this applies in particular to the following aspects:
  • systemic and functional composition
  • specifically defined distribution structure in each case
  • connection to operating systems and user interfaces

Specific applications of AL!SE

The systemic and functional variability of AL!SE allows an extremely wide range of applications for the semantic data model. In principle, this includes the optimization of all types of closed information systems – regardless of the type of content, processing or distribution. Accordingly, AL!SE has already been able to work in a wide variety of industries with different contexts, requirements and objectives are successfully implemented.

AL!SE transforms your data into information. AL!SE creates meaning.