Knowledge Representation Technologies Research Articles

Semantic Interoperability Architecture for Pervasive Computing and Internet of Things

Pervasive computing and Internet of Things (IoTs) paradigms have created a huge potential for new business. To fully realize this potential, there is a need for a common way to abstract the heterogeneity of devices so that their functionality can be represented as a virtual computing platform. To this end, we present novel semantic level interoperability architecture for pervasive computing and IoTs. There are two main principles in the proposed architecture. First, information and capabilities of devices are represented with semantic web knowledge representation technologies and interaction with devices and the physical world is achieved by accessing and modifying their virtual representations. Second, global IoT is divided into numerous local smart spaces managed by a semantic information broker (SIB) that provides a means to monitor and update the virtual representation of the physical world. An integral part of the architecture is a resolution infrastructure that provides a means to resolve the network address of a SIB either using a physical object identifier as a pointer to information or by searching SIBs matching a specification represented with SPARQL. We present several reference implementations and applications that we have developed to evaluate the architecture in practice. The evaluation also includes performance studies that, together with the applications, demonstrate the suitability of the architecture to real-life IoT scenarios. In addition, to validate that the proposed architecture conforms to the common IoT-A architecture reference model (ARM), we map the central components of the architecture to the IoT-ARM.

A Multi-Level Knowledge Representation Technology Research in Oil Management System

Ontology combined with the development of computer technology in all fields initiated the research of construction and application for so-called domain ontology. This article starts with the issues of current computer technology in the oil exploration and development domain, provides a construction method of oil exploration and development domain ontology based on a multi-level business process,and divides the oil exploration and development domain into multistage business activity levels,and takes hydrocarbon prospect characterization as example and discusses a construction method for corresponding micro-ontology.

An overview of interactive visual data mining techniques for knowledge discovery

In the past decade, the analysis of data has faced the challenge of dealing with very large and complex datasets and the real‐time generation of data. Technologies to store and access these complex and large datasets are in place. However, robust and scalable analysis technologies are needed to extract meaningful information from these datasets. The research field of Information Visualization and Visual Data Analytics addresses this need. Information visualization and data mining are often used complementary to each other. Their common goal is the extraction of meaningful information from complex and possibly large data. However, though data mining focuses on the usage of silicon hardware, visualization techniques also aim to access the powerful image‐processing capabilities of the human brain. This article highlights the research on data visualization and visual analytics techniques. Furthermore, we highlight existing visual analytics techniques, systems, and applications including a perspective on the field from the chemical process industry.This article is categorized under: Application Areas > Data Mining Software Tools Fundamental Concepts of Data and Knowledge > Knowledge Representation Technologies > Visualization

Automatic construction of lexicons, taxonomies, ontologies, and other knowledge structures

Abstract, structured, representations of knowledge such as lexicons, taxonomies, and ontologies have proven to be powerful resources not only for the systematization of knowledge in general, but to support practical technologies of document organization, information retrieval, natural language understanding, and question‐answering systems. These resources are extremely time consuming for people to create and maintain, yet demand for them is growing, particularly in specialized areas ranging from legacy documents of large enterprises to rapidly changing domains such as current affairs and celebrity news. Consequently, researchers are investigating methods of creating such structures automatically from document collections, calling on the proliferation of interlinked resources already available on the web for background knowledge and general information about the world. This review surveys what is possible, and also outlines current research directions.This article is categorized under: Algorithmic Development > Text Mining Fundamental Concepts of Data and Knowledge > Knowledge Representation Technologies > Structure Discovery and Clustering

Knowledge representation and processing with formal concept analysis

AbstractDuring the last three decades, formal concept analysis (FCA) became a well‐known formalism in data analysis and knowledge discovery because of its usefulness in important domains of knowledge discovery in databases (KDD) such as ontology engineering, association rule mining, machine learning, as well as relation to other established theories for representing knowledge processing, like description logics, conceptual graphs, and rough sets. In early days, FCA was sometimes misconceived as a static crisp hardly scalable formalism for binary data tables. In this paper, we will try to show that FCA actually provides support for processing large dynamical complex (may be uncertain) data augmented with additional knowledge. © 2013 Wiley Periodicals, Inc.This article is categorized under: Fundamental Concepts of Data and Knowledge > Key Design Issues in Data Mining Fundamental Concepts of Data and Knowledge > Knowledge Representation Technologies > Computational Intelligence

On new emerging concepts of petroleum digital ecosystem

AbstractPetroleum‐bearing sedimentary basin is an emerging digital ecosystem. Petroleum system and its elements are narrated for each and every oil and gas field in each and every petroleum‐bearing sedimentary basin. A new concept of ecosystem and its digitization are emerging within the generic petroleum system. The Significance of this concept comes with an ability to establish connectivity among petroleum subsystems, for example, Albertine Graben (Western Uganda), through attributes of their components and their contextualization and specifications. Unless the phenomenon of integration of elements and processes among subbasins in the context of digital representation, visualization, and interpretation are understood, the existence of petroleum system (both at local and global scales) and its survival, the phenomenon of ‘interconnectivity’ among ecosystems cannot be well explained. Its value cannot be added in terms of petroleum accumulations and volumes, unless these phenomena are made explicit. Authors propose ontology‐based data warehousing and data mining technologies in which conceptualization and contextualization of multiple data dimensions' are modeled (elements and processes of petroleum system), besides their integration (within data warehouse environment) and data mining of interpretable emerging petroleum digital ecosystem. Multidimensional data warehousing and mining facilitate an effective interpretation of petroleum ecosystems, minimizing the ambiguities involved during structure and reservoir qualifications and quantifications, especially during reserve computations. © 2012 Wiley Periodicals, Inc.This article is categorized under: Fundamental Concepts of Data and Knowledge > Data Concepts Fundamental Concepts of Data and Knowledge > Knowledge Representation Technologies > Visualization

Towards an Epistemic Web

In the beginning knowledge was local. With the development of more complex forms of economic organization knowledge began to travel. The Library of Alexandria was the fulfillment - however partial and transitory - of a vision to bring together all the knowledge of the world. But to obtain the knowledge one had to go to Alexandria. Today the World Wide Web promises to make universally accessible the knowledge of a world grown larger. To be sure, much work remains to be done: many documents need to be made available (i.e. digitized if they are not already, and freed from restrictive access controls); and various biases (economic, legal, linguistic, social, technological) need to be overcome. But what do we do with this knowledge? Is it enough to create a digital library of Alexandria, with (perhaps) improved finding aids? We propose that the crucial question is how to structure knowledge on the Web to facilitate the construction of new knowledge, knowledge that will be critical in addressing the challenges of the emerging global society. We begin by asking three questions about the Web and its future. In the remainder of the paper we explore the possibility of an 'Epistemic Web' in the context of a more general discussion of 'knowledge representation technologies,' technologies used for storing, manipulating and spreading knowledge.

Partitive clustering (K‐means family)

AbstractPartitional clustering is an important part of cluster analysis. Cluster analysis can be considered as one of the the most important approaches to unsupervised learning. The goal of clustering is to find clusters from unlabeled data, which means that data belonging to the same cluster are as similar as possible, whereas data belonging to different clusters are as dissimilar as possible. Partitional clustering is categorized as a prototype‐based model, i.e., each cluster can be represented by a prototype, leading to a concise description of the original data set. According to different definitions of prototypes, such as data point, hyperplane, and hypersphere, the clustering methods can be categorized into different types of clustering algorithms with various prototypes. Besides organizing these partitional clustering methods into such a unified framework, relations between some commonly used nonpartitional clustering methods and partitional clustering methods are also discussed here. We give a brief overview of clustering, summarize well‐known partitional clustering methods, and discuss the major challenges and key issues of these methods. Simple numerical experiments using toy data sets are carried out to enhance the description of various clustering methods. © 2012 Wiley Periodicals, Inc.This article is categorized under:Algorithmic Development > Structure DiscoveryFundamental Concepts of Data and Knowledge > Knowledge RepresentationTechnologies > Structure Discovery and Clustering

Overview of Intelligent Design on Mold

Applying Artificial Intelligence technology on mold design can help realize the design automation of mold. Because knowledge-based engineering is an effective intelligent design method, the paper systematically introduced the application and development of knowledge representation, knowledge reasoning, knowledge acquisition and other key technologies of knowledge engineering technology used in mold design field. Finally, the development tendency of mold design based on artificial intelligence was analyzed in detail

A study of methodological issues in design and development of rule‐based systems: proposal of a new approach

AbstractRule‐based systems (RBSs) constitute a powerful technology for declarative encoding and automated processing of large bodies of knowledge. A typical RBS consists of a knowledge base containing facts and production rules, and an inference engine managing the reasoning process. Despite their simple conceptual scheme, design and development of a RBS often turn out to be unexpectedly complex task. This paper presents an overview of issues concerning design and development of such systems. Differences between RBSs and classical software are exemplified, and design and implementation issues are analyzed. A novel, consistent, three‐phase methodology incorporating conceptual, logical, and physical design is outlined. Moreover, tools supporting the complete design and development process are presented. © 2011 John Wiley & Sons, Inc. WIREs Data Mining Knowl Discov 2011 1 117‐137 DOI: 10.1002/widm.11This article is categorized under: Application Areas > Business and Industry Fundamental Concepts of Data and Knowledge > Knowledge Representation Technologies > Visualization

Semantic-based document management system

In this paper, we present the work towards ORSIS, an ontology-based personal document management system which uses a reasoning engine to allow for knowledge discovery and inference. This work also presents the potentials of knowledge representation technologies, their performance and applicability for personal document management systems. As part of ORSIS, we also have developed simple but powerful query language which allows executing selection request with different joint criteria. Appropriate grammar has been developed and parser/lexer code was implemented. Users have all the necessary interface features to provide complex queries or to build search request by using appropriate dialogs.

Semantic data model in multibody system simulation

This article introduces an approach for using a semantic data model and knowledge representation technologies in the data management of multibody system simulation. This is accomplished by developing a semantic ontology for multibody system modelling. As an example of semantic ontology modelling, the introduced approach is applied to a simple double-pendulum model. The purpose of the example is to demonstrate that multibody system simulation data can be expressed using semantic data representation. In addition, semantic data representation enables a general and straightforward application integration solution for design tools and other information systems typically involved in the industrial product design process. It is also important to note that this approach enables knowledge of the domain of interest to be stored together with the modelling data. By including rules and constraints with the data and applying a semantic reasoning methodology, the apparent intelligence of the stored data is increased.

Enabling Product Design Reuse by Long-term Preservation of Engineering Knowledge

In the highly competitive engineering industry, product innovations are created with the help of a product lifecycle management (PLM) tool chain. In order to support fast-paced product development, a major company goal is the reuse of product designs and product descriptions. Due to the product’s complexity, the design of a product not only consists of geometry data but also of valuable engineering knowledge that is created during the various PLM phases. The need to preserve such intellectual capital leads engineering companies to introduce knowledge management and archiving their machine-readable formal representation. However, archived knowledge is in danger of becoming unusable since it is very likely that knowledge semantics and knowledge representation will evolve over long time periods, for example during the 50 operational years of some products. Knowledge evolution and knowledge representation technology changes are crucial issues since a reuse of the archived product information can only be ensured if its rationale and additional knowledge are interpretable with future software and technologies. Therefore, in order to reuse design data fully, knowledge about the design must also be migrated to be interoperable with future design systems and knowledge representation methods. This paper identifies problems, issues, requirements, challenges and solutions that arise while tackling the long-term preservation of engineering knowledge.

Interactional coherence in asynchronous learning networks: A rhetorical approach

Numerous studies have affirmed the value of asynchronous online communication as a learning resource. Several investigations, however, have indicated that discussions in asynchronous environments are often neither interactive nor coherent. The research reported sought to develop an enhanced understanding of interactional coherence, argumentation, and topic drift in asynchronous learning environments. Rhetorical structure theory (RST) was used to analyze and assess the coherence of several asynchronous discussions. Findings include that asynchronous discussions take the form of dynamic rhetorical structures which are continuously redefined as new messages are added to a thread, that argumentation may be more prevalent in some discussions than others, that topic drift does not seem to occur as a matter of chance, but rather topics are manipulated to suit the individual preferences of the participants, and that the use of threading differs considerably from one discussion group to another. By demonstrating the applicability of RST, argumentative analysis, and topic drift analysis to asynchronous discussion, this research provides a framework and a terminology for fine-grained analysis of interactional coherence. By showing the applicability of RST to asynchronous discussion, this study has offered evidence that essay assessment technology could be developed for evaluating the quality of online discussions. The development of rhetorical networks as a graph theory for representing the semantics of asynchronous interaction could lead to a richer knowledge representation technology for inter-agent collaboration.

Towards Establishing the Design Exemplar as a CAD Query Language

AbstractComputer aided design solid models are frequently interrogated by design engineers and other design stakeholders for reasons such as design reuse, standardization, manufacturability analysis, compatibility, feature extraction, or requirement validation. Currently, two approaches are employed in interrogating these solid models: interactive user interrogation and predefined feature recognition. A new interrogation approach is proposed here based upon the design exemplar. Using this new engineering knowledge representation schema, engineers may interactively define queries against their design models incorporating geometric, topologic, semantic, and algebraic entities and relations; specifying both the explicit and implicit desired characteristics. This paper presents the requirements for a CAD query language and positions the design exemplar as an appropriate solution for this problem.

Research of knowledge-based hammer forging design support system

Die design is experience-extensive in the field of plasticity engineering, in which knowledge and experience play a very important role in supporting the design process. In order to make good use of the accumulated engineering knowledge, the methodology of knowledge-based hammer forging design support is elaborated in detail, including: engineering knowledge representation, knowledge acquisition and knowledge-based design support techniques. Based on these, a knowledge-based hammer forging design support system is developed using the API of SolidWorks. This prototype system is a design guide system, which provides the right knowledge to support the designer, make decision/selection and design the die step by step. This way, the computerized public and private knowledge can be greatly shared and reused, which improves the design efficiency, and avoids previous mistakes effectively. Finally, a case study is shown to elaborate how the system is operated.

An aggregate resource model for the provision of dynamic ‘resource-aware’ planning

The realization of agile enterprises requires substantial development of the underpinning modelling, information management and knowledge representation technologies. This paper introduces a resource model that has been developed to support the dynamic, aggregate planning of manufacturing operations within large, complex production networks during the formative stages of design. The main goal of aggregate planning is the measurement of product manufacturability and the evaluation of alternative design configurations and manufacturing scenarios, through the allocation of multiple parts to remote facilities within the supply network. The term ‘resource aware’ planning is used to indicate the creation of a dynamic interrelationship between the planning entities (products and processes) and the enterprise resources, including humans and machines. The technologies employed for implementing the pilot methods include the aggregate resource modelling methodology, a web-centric co-development environment, unique methods for enriching planning entities with knowledge and evolutionary computing methods for the rapid generation and optimization of production routes. A pilot resource aware planning system has been implemented that supports many innovative modes of operation, the initial testing of which was very encouraging.

Designing intelligent agents to support universal accessibility of E-commerce services

We propose the design of an intelligent agent to improve accessibility of E-commerce applications and Web sites for visually impaired individuals. An important feature of this design is the application of knowledge representation technology—specifically conceptual structures—to explicitly capture and represent the navigational semantics of each document. This allows one to view the process of navigating a complex HTML document—e.g., a document containing tables and complex forms—as solving goals with respect to an action theory, i.e., as a planning problem. This information will be used by agents to provide a level of intelligence in the navigation process, allow users to express high level navigation goals, solve such goals using planning techniques. Effectively, the navigation of the complex components of HTML documents will be done by agents, on behalf of the visually impaired user.

Resource-Aware Aggregate Planning for the Distributed Manufacturing Enterprise

The realization of ‘intelligent and resource aware’ distributed enterprises requires substantial development of the underpinning modelling, information management and knowledge representation technologies. This paper deals with the ‘resource-aware, aggregate planning’ of manufacturing operations at early design stages. The term ‘resource aware’ indicates the creation of a dynamic inter-relationship between the planning entities and the enterprise resources, humans and machines. The technologies employed for implementing the pilot methods include; a web-centric co-development environment, unique methods for enriching planning entities with knowledge, and a flexible engine supporting planning scenarios by using evolutionary computing for optimisation and capability analysis techniques for feedback evaluation.

Better knowledge management through knowledge engineering

The authors believe that current knowledge management practice significantly under-utilizes knowledge engineering technology, despite recent efforts to promote its use. They focus on two knowledge engineering processes: using knowledge acquisition processes to capture structured knowledge systematically; and using knowledge representation technology to store the knowledge, preserving important relationships that are far richer than those possible in conventional databases. To demonstrate the usefulness of these processes, we present a case study in which the drilling optimization group of a large oil and gas service company uses knowledge engineering practices to support the three facets of the knowledge management task: knowledge capture; knowledge storage; and knowledge deployment.