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Many of our innovative projects ends up with a whitepaper.
This paper presents a way of representation of semantic rules (SWRL) in controlled English in order to facilitate understanding the rules by humans interacting with a machine. This approach (implemented in FluentEditor) may be applied in many domains, where the understandability of the rules used to support a decision process is of great importance.
In this paper we present two tools that we are developing at Cognitum for managing large knowledge bases: Fluent Editor and the Ontorion Server. We have been able to build a collaborative knowledge management system using these two tools. We show how this system can be used for the concurrent modification of knowledge and how we can manage multiple modifications to the same knowledge.
Semantic technologies appear as a step on the way to creating systems capable of representing the physical world as real time computational processes. In this context, the paper presents a toolchain for an ontology based knowledge management system. It consists of the ontology editor, FluentEditor and the distributed knowledge representation system, Ontorion. FluentEditor is a comprehensive tool for editing and manipulating complex ontologies that uses Controlled Natural Language (CNL). Its main feature is the usage of Controlled English as a knowledge modelling language. Ontorion is a Distributed Knowledge Management System with Natural Language interfaces (CNL) and a built-in rules engine. The Ontorion system is equipped with plugins for connection with other software environments, for example rOntorion using an R language package to access ontologies. It is exemplified with the semantic extension of On Line Analytical Processing (OLAP) using R language.
The paper presents the definition of the design pattern language of Smart Cities in the form of an ontology. Since the implementation of a Smart City system is difficult, expensive and closely linked with the problems concerning a given city, the knowledge acquired during a single implementation is extremely valuable. The language we defined supports the management of such knowledge as it allows for the expression of a solution which, based on best practices recorded in the form of design patterns, is also tailored to the requirements of the city seeking to implement the Smart City solution. The formal/ontological structure of the language in turn allows the automatic management of the properties of a solution recorded in this way. This final feature of the introduced language is extremely important in the decision-making process regarding the choice of a particular solution by the relevant authorities.
The work is divided into five main parts. In the first part we discuss the implementation issue of the integration bus using the example of the IOC. In the next part we talk about the validity of using semantic technologies in order to expand the spectrum of potential implementations. Then we discuss the ontological implementation of the Smart City pattern language which we created, a language which allows for both the saving of requirements and the validation of solutions specified in it. We also present an example of usage, which at the same time serves as a validation of the language in real-life conditions. In the last part we discuss certain aspects of the pattern language and the possible ways to develop research related to it.
While to collect data, it is necessary to store it, to understand its structure it is necessary to do data-mining. Business Intelligence (BI) enables us to make intelligent, data-driven decisions by the mean of a set of tools that allows the creation of a potentially unlimited number of machine-generated, data-driven reports, which are calculated by a machine as a response to queries specified by humans. Natural Query Languages (NQLs) allow one to dig into data with an intuitive human-machine dialogue. The current NQL-based systems main problems are the required prior learning phase for writing correct queries, understanding the linguistic coverage of the NQL and asking precise questions.
Results: We have developed an NQL as well as an entire Natural Language Interface Database (NLIDB) that supports the user with BI queries with minimized disadvantages, namely Ask Data Anything. The core part – NQL parser – is a hybrid of CNL and the pattern matching approach with a prior error repair phase. Equipped with reasoning capabilities due to the intensive use of semantic technologies, our hybrid approach allows one to use very simple, keyword-based (even erroneous) queries as well as complex CNL ones with the support of a predictive editor.
The methodology of semantic clustering analysis of customer’s text-opinions collection is developed. The author’s version of the mathematical models of formalization and practical realization of short textual messages semantic clustering procedure is proposed, based on the customer’s text-opinions collection Latent Semantic Analysis knowledge extracting method. An algorithm for semantic clustering of the text-opinions is developed, the distinctive characteristics of which is the introduction of concepts and methods of identification point of reference in the scale of text-opinions collection closeness determination; instrument of the documents’ closeness degree identification; measure of similarity between pairs of documents. The version of quantitative evaluation of the clustering results is developed. The concepts of resolving power of the method of semantic clustering and level of the clustering procedure quality are proposed. Analysis of the specific features and the effectiveness level of various distance measures is conducted
Automatic generation of user interfaces from domain knowledge: ARBUI.Practical implementation of the proposed architecture (ARBUI): the Semantic MVC.Tested our approach in a pilot project at the Oncology Center in Warsaw. Motivation: The ability to directly trace how requirements are implemented in a software system is crucial in domains that require a high level of trust (e.g. medicine, law, crisis management). This paper describes an approach that allows a high level of traceability to be achieved with model-driven engineering supported by automated reasoning. The paper gives an introduction to the novel, automated user interface synthesis in which a set of requirements is automatically translated into a working application. It is presented as a generalization of the current state of the art model-driven approaches both from the conceptual perspective as well as the concrete implementation is discussed together with its advantages like the alignment of business logic with the application and ease of adaptability. It also presents how a high level of traceability can be obtained if runtime support of automated reasoning over models is applied.Results: We have defined the Automated Reasoning-Based User Interface (ARBUI) approach and implemented a framework for application programming that follows our definition. The framework, called Semantic MVC, is based on model-driven engineering principles enhanced with W3C standards for the semantic web. We will present the general architecture and main ideas underlying our approach and framework. Finally, we will present a practical application of the Semantic MVC that we created in the medical domain as a Clinical Decision Support System for GIST cancer in cooperation with the Maria Sklodowska-Curie Memorial Cancer Center and Institute of Oncology in Warsaw. The discussed expert system allows the expert to directly modify the executable knowledge on the fly, making the overall system cost effective.
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