Uniform Knowledge Representation Research Articles

Information technologies of analytical processing of multilingual text sources as factor of safety of adoption of administrative decisions

The work is devoted to the analysis of the condition of information and analytical ensuring adoption of administrative decisions as object of information influence. The article considers the essence of knowledge-oriented approach to the construction of information technologies for analytical processing of information resources in the interests of ensuring the administrative decisions. Among sources and channels realizing threats the special attention is given to information technologies, information resources and consciousness of the person. The work proposes the concept of constructing information technology for analytical processing of text sources, which can neutralize the threats described in the article. Realization of methods of automation of extraction of knowledge which contain in the natural language text, their formalization and developments of technological bases of uniform representation of knowledge in computer for texts which are submitted by various source languages is the key moment of creation of such model.

KSPMI: A Knowledge-based System for Predictive Maintenance in Industry 4.0

In the context of Industry 4.0, smart factories use advanced sensing and data analytic technologies to understand and monitor the manufacturing processes. To enhance production efficiency and reliability, statistical Artificial Intelligence (AI) technologies such as machine learning and data mining are used to detect and predict potential anomalies within manufacturing processes. However, due to the heterogeneous nature of industrial data, sometimes the knowledge extracted from industrial data is presented in a complex structure. This brings the semantic gap issue which stands for the lack of interoperability among different manufacturing systems. Furthermore, as the Cyber-Physical Systems (CPS) are becoming more knowledge-intensive, uniform knowledge representation of physical resources and real-time reasoning capabilities for analytic tasks are needed to automate the decision-making processes for these systems. These requirements highlight the potential of using symbolic AI for predictive maintenance. To automate and facilitate predictive analytics in Industry 4.0, in this paper, we present a novel Knowledge-based System for Predictive Maintenance in Industry 4.0 (KSPMI). KSPMI is developed based on a novel hybrid approach that leverages both statistical and symbolic AI technologies. The hybrid approach involves using statistical AI technologies such as machine learning and chronicle mining (a special type of sequential pattern mining approach) to extract machine degradation models from industrial data. On the other hand, symbolic AI technologies, especially domain ontologies and logic rules, will use the extracted chronicle patterns to query and reason on system input data with rich domain and contextual knowledge. This hybrid approach uses Semantic Web Rule Language (SWRL) rules generated from chronicle patterns together with domain ontologies to perform ontology reasoning, which enables the automatic detection of machinery anomalies and the prediction of future events’ occurrence. KSPMI is evaluated and tested on both real-world and synthetic data sets.

Towards a Semantic Web of Things: A Hybrid Semantic Annotation, Extraction, and Reasoning Framework for Cyber-Physical System.

Web of Things (WoT) facilitates the discovery and interoperability of Internet of Things (IoT) devices in a cyber-physical system (CPS). Moreover, a uniform knowledge representation of physical resources is quite necessary for further composition, collaboration, and decision-making process in CPS. Though several efforts have integrated semantics with WoT, such as knowledge engineering methods based on semantic sensor networks (SSN), it still could not represent the complex relationships between devices when dynamic composition and collaboration occur, and it totally depends on manual construction of a knowledge base with low scalability. In this paper, to addresses these limitations, we propose the semantic Web of Things (SWoT) framework for CPS (SWoT4CPS). SWoT4CPS provides a hybrid solution with both ontological engineering methods by extending SSN and machine learning methods based on an entity linking (EL) model. To testify to the feasibility and performance, we demonstrate the framework by implementing a temperature anomaly diagnosis and automatic control use case in a building automation system. Evaluation results on the EL method show that linking domain knowledge to DBpedia has a relative high accuracy and the time complexity is at a tolerant level. Advantages and disadvantages of SWoT4CPS with future work are also discussed.

Representation of real-time decision-making by contextual graphs based simulation

An important aspect of mobile decision is its temporal nature. Experts develop a decision-making process jointly with the progressive elaboration of a context-specific model of the decision-making. However, context modeling is generally not considered explicitly, because only the result of the decision-making process is supposed to be of interest. For mobile decision, modeling this contextualization process supposes a formalism allowing a uniform representation of knowledge, reasoning and contexts like the Contextual-Graphs formalism in which all the contextual variants of a decision-making process are represented as a contextual graph. This paper presents the example of a workflow manager that is developed in a large project in breast cancer diagnosis. The workflow manager is a real-time decision-making process in which the result of an action may change the context of the decision-making process. Managing context jointly with the decision-making process is a way to tackle time and model effectively mobile decision-making.

Uniform knowledge representation for language processing in the B2 system

We describe the natural language processing and knowledge representation components of B2, a collaborative system that allows medical students to practice their decision-making skills by considering a number of medical cases that differ from each other in a controlled manner. The underlying decision-support model of B2 uses a Bayesian network that captures the results of prior clinical studies of abdominal pain. B2 generates story-problems based on this model and supports natural language queries about the conclusions of the model and the reasoning behind them. B2 benefits from having a single knowledge representation and reasoning component that acts as a blackboard for intertask communication and cooperation. All knowledge is represented using a propositional semantic network formalism, thereby providing a uniform representation to all components. The natural language component is composed of a generalized augmented transition network parser/grammar and a discourse analyzer for managing the natural language interactions. The knowlege representation component supports the natural language component by providing a uniform representation of the content and structure of the interaction, at the parser, discourse, and domain levels. This uniform representation allows distinct tasks, such as dialog management, domain-specific reasoning, and meta-reasoning about the Bayesian network, to all use the same information source, without requiring mediation. This is important because there are queries, such as Why?, whose interpretation and response requires information from each of these tasks. By contrast, traditional approaches treat each subtask as a “black-box” with respect to other task components, and have a separate knowledge representation language for each. As a result, they have had much more difficulty providing useful responses.

The design of a model-based multimedia interaction manager

We describe here the conceptual design ofCicero, an application-independent human-computer interaction manager that performs run-time media coordination and allocation, so as to adapt dynamically to the presentation context; knows what it is presenting, so as to maintain coherent extended human-machine dialogues; and is plug-in compatible with host information resources such as “briefing associate” workstations, expert systems, databases, etc., as well as with multiple media such as natural language, graphics, etc. The system design calls for two linked reactive planners that coordinate the actions of the system's media and information sources. To enable presentational flexibility, the capabilities of each medium and the nature of the contents of each information source are semantically modeled as Virtual Devices — abstract descriptions of device I/O capabilities — and abstract information types respectively in a single uniform knowledge representation framework. These models facilitate extensibility by supporting the specification of new interaction behaviors and the inclusion of new media and information sources.