Hierarchical Knowledge Representation Research Articles

A hierarchical assembly knowledge representation framework and microdevice assembly ontology

Microdevice assembly knowledge is dispersed in different product development phases, such as assembly design, assembly simulation and assembly process, and a lot of essential knowledge is implicit and heterogeneous. It is difficult for researchers and computer-aided systems to share and reuse different assembly knowledge quickly and accurately, leading to inefficient and inaccurate assembly process planning. To integrate and structurally represent the assembly design knowledge, assembly simulation knowledge and assembly process knowledge of microdevice, this paper proposes a hierarchical assembly knowledge representation framework and develops a microdevice assembly ontology. There are four layers in the framework, including the organizational structure, the structural relationship, the assembly accuracy, and the process characteristics. The assembly design knowledge that is integrated involves the basic properties of the assembly object as well as the spatial, mating, and assembly relationship, etc. Assembly simulation knowledge refers to the permissible range of assembly force and contact force. Knowledge of assembly processes comprises assembly sequence and operating method of the part. The microdevice assembly ontology is developed based on METHONTOLOGY, and implemented with Protégé. The corresponding SWRL rules have been established to inference the implicit knowledge in assembly design. An ignition target assembly knowledge model based on the microdevice assembly ontology is constructed. In the assembly task of the ignition target, engineers can quickly and accurately access the required assembly knowledge from the ignition target assembly knowledge model, thus verifying the integrity and validity of the microdevice assembly ontology.

GROOLS: reactive graph reasoning for genome annotation through biological processes

BackgroundHigh quality functional annotation is essential for understanding the phenotypic consequences encoded in a genome. Despite improvements in bioinformatics methods, millions of sequences in databanks are not assigned reliable functions. The curation of protein functions in the context of biological processes is a way to evaluate and improve their annotation.ResultsWe developed an expert system using paraconsistent logic, named GROOLS (Genomic Rule Object-Oriented Logic System), that evaluates the completeness and the consistency of predicted functions through biological processes like metabolic pathways. Using a generic and hierarchical representation of knowledge, biological processes are modeled in a graph from which observations (i.e. predictions and expectations) are propagated by rules. At the end of the reasoning, conclusions are assigned to biological process components and highlight uncertainties and inconsistencies. Results on 14 microbial organisms are presented.ConclusionsGROOLS software is designed to evaluate the overall accuracy of functional unit and pathway predictions according to organism experimental data like growth phenotypes. It assists biocurators in the functional annotation of proteins by focusing on missing or contradictory observations.

A web-based knowledge management system integrating Western and Traditional Chinese Medicine for relational medical diagnosis.

This article presents the design of a web-based knowledge management system as a training and research tool for the exploration of key relationships between Western and Traditional Chinese Medicine, in order to facilitate relational medical diagnosis integrating these mainstream healing modalities. The main goal of this system is to facilitate decision-making processes, while developing skills and creating new medical knowledge. Traditional Chinese Medicine can be considered as an ancient relational knowledge-based approach, focusing on balancing interrelated human functions to reach a healthy state. Western Medicine focuses on specialties and body systems and has achieved advanced methods to evaluate the impact of a health disorder on the body functions. Identifying key relationships between Traditional Chinese and Western Medicine opens new approaches for health care practices and can increase the understanding of human medical conditions. Our knowledge management system was designed from initial datasets of symptoms, known diagnosis and treatments, collected from both medicines. The datasets were subjected to process-oriented analysis, hierarchical knowledge representation and relational database interconnection. Web technology was implemented to develop a user-friendly interface, for easy navigation, training and research. Our system was prototyped with a case study on chronic prostatitis. This trial presented the system's capability for users to learn the correlation approach, connecting knowledge in Western and Traditional Chinese Medicine by querying the database, mapping validated medical information, accessing complementary information from official sites, and creating new knowledge as part of the learning process. By addressing the challenging tasks of data acquisition and modeling, organization, storage and transfer, the proposed web-based knowledge management system is presented as a tool for users in medical training and research to explore, learn and update relational information for the practice of integrated medical diagnosis. This proposal in education has the potential to enable further creation of medical knowledge from both Traditional Chinese and Western Medicine for improved care providing. The presented system positively improves the information visualization, learning process and knowledge sharing, for training and development of new skills for diagnosis and treatment, and a better understanding of medical diseases.

Simulation of Inference Operator for Hierarchical Knowledge Representation Formalisms

Simulation of Inference Operator for Hierarchical Knowledge Representation Formalisms

Prediction of Period-Doubling Bifurcation Based on Dynamic Recognition and Its Application to Power Systems

In this paper, a bifurcation prediction approach is proposed based on dynamic recognition and further applied to predict the period-doubling bifurcation (PDB) of power systems. Firstly, modeling of the internal dynamics of nonlinear systems is obtained through deterministic learning (DL), and the modeling results are applied for constructing the dynamic training pattern database. Specifically, training patterns are chosen according to the hierarchical structured knowledge representation based on the qualitative property of dynamical systems, which is capable of arranging the dynamical models into a specific order in the pattern database. Then, a dynamic recognition-based bifurcation prediction approach is suggested. As a result, perturbations implying PDB on the testing patterns can be predicted through the minimum dynamic error between the training patterns and testing patterns by recalling the knowledge restored in the pattern database. Finally, the second-order single-machine to infinite bus power system model is introduced to check the effectiveness of this prediction approach, which implies PDB under small periodic parameter perturbations. The key point that determines the prediction effect mainly lies in two methods: (1) accurate approximation of the unknown system dynamics through DL guarantees the feasibility of the prediction process; (2) the qualitative property of PDB and the generalization ability of DL algorithm ensure the validity of the selected training patterns. Simulations are included to illustrate the effectiveness of the proposed approach.

Growing Hierarchical Self-Organizing Map Using Category Utility

In order to automatically obtain hierarchical knowledge representation from a certain data, an unsupervised learning method has been developed that overcomes two problems of the growing hierarchical self-organizing map (GHSOM) method, which uses the quantization error, the deviation of the input data, as evaluation measure of the growing maps: proper control of the growth process of each map is difficult due to the use of the quantization error and the clusters in the hierarchical structure may be excessively subdivided. This improved GHSOM method uses the category utility (CU), a measure used in conceptual clustering for predicting the preferred level of categorization, instead of the quantization error. The CU is useful for organizing the clustering so that people can effortlessly understand it. The basic principle of this method is that the growth and unification processes are appropriately and autonomously controlled by the CU. Evaluation using computer experiments showed that the proposed method can automatically construct an appropriate hierarchical and topological knowledge representation for high-dimensional input data through unsupervised learning. It also showed that it is easier to use and more effective than the original conventional GHSOM method using the quantization error as an evaluation measure.

Transfer metric learning for action similarity using high-level semantics

The goal of transfer learning is to exploit previous experiences and knowledge in order to improve learning in a novel domain. This is especially beneficial for the challenging task of learning classifiers that generalize well when only few training examples are available. In such a case, knowledge transfer methods can help to compensate for the lack of data. The performance and robustness against negative transfer of these approaches is influenced by the interdependence between knowledge representation and transfer type. However, this important point is usually neglected in the literature; instead the focus lies on either of the two aspects. In contrast, we study in this work the effect of various high-level semantic knowledge representations on different transfer types in a novel generic transfer metric learning framework. Furthermore, we introduce a hierarchical knowledge representation model based on the embedded structure in the semantic attribute space. The evaluation of the framework on challenging transfer settings in the context of action similarity demonstrates the effectiveness of our approach compared to state-of-the-art.

추상화계층에 기반한 작업영역분석의 모델링 개념 및 적용 원칙

As a work analysis technique, Work Domain Analysis (WDA) aims to identify the design knowledge structure of a work domain that human operators interact with through human-system interfaces. Abstraction hierarchy (AH) is a multi-level, hierarchical knowledge representation framework for modeling the functional structure of any kinds of systems. Thus, WDA based on AH aims to identify the functional knowledge structure of a work domain. AH has been used in a range of work domains and problems to model their functional knowledge structure and has proven its generality and usefulness. However, many of researchers and system designers have reported that it is never easy to understand the concepts underlying AH and use it effectively for WDA. This would be because WDA is a form of work analysis that is different from other types of work analysis techniques such as task analysis and AH has several unique characteristics that are differentiated from other types of function analysis techniques used in systems engineering. With this issue in mind, this paper introduces the concepts of WDA based on AH and offers a comprehensive list of references. Next, this paper proposes a set of principles for effectively applying AH for work domain analysis, which are developed based on the author`s experiences, consultation with experts, and literature reviews.

Procedural Steps for Knowledge Mining in Time Series

intervals which form temporal patterns are usually formulated through Allen's interval relations that originate in temporal reasoning. But this representation is not advantages for knowledge discovery. The Hierarchical Time series Knowledge Representation (HTKR) is the hierarchical language which expresses the temporal aspects of coincidence and partial order, for interval patterns. We present mining procedural steps which are more efficient, effective and based on item set techniques. Pruning of the search space minimizes the mining result size considerably, thereby speeding up the procedural steps and easing the interpretations. When applied on the real data set, HTKR can provide the explanation of underlying temporal phenomena, but whereas the numerous Allen's relation patterns only explains fragmented data.

A hierarchical view of grounded, embodied, and situated numerical cognition

There is much recent interest in the idea that we represent our knowledge together with the sensory and motor features that were activated during its acquisition. This paper reviews the evidence for such "embodiment" in the domain of numerical cognition, a traditional stronghold of abstract theories of knowledge representation. The focus is on spatial-numerical associations, such as the SNARC effect (small numbers are associated with left space, larger numbers with right space). Using empirical evidence from behavioral research, I first describe sensory and motor biases induced by SNARC, thus identifying numbers as embodied concepts. Next, I propose a hierarchical relationship between grounded, embodied, and situated aspects of number knowledge. This hierarchical conceptualization helps to understand the variety of SNARC-related findings and yields testable predictions about numerical cognition. I report several such tests, ranging from cross-cultural comparisons of horizontal and vertical SNARC effects (Shaki and Fischer in J Exp Psychol Hum Percept Perform 38(3):804-809, 2012) to motor cortical activation studies in adults with left- and right-hand counting preferences (Tschentscher et al. in NeuroImage 59:3139-3148, 2012). It is concluded that the diagnostic features for each level of the proposed hierarchical knowledge representation, together with the spatial associations of numbers, make the domain of numerical knowledge an ideal testing ground for embodied cognition research.

Semantic Knowledge-Based Framework to Improve the Situation Awareness of Autonomous Underwater Vehicles

This paper proposes a semantic world model framework for hierarchical distributed representation of knowledge in autonomous underwater systems. This framework aims to provide a more capable and holistic system, involving semantic interoperability among all involved information sources. This will enhance interoperability, independence of operation, and situation awareness of the embedded service-oriented agents for autonomous platforms. The results obtained specifically affect the mission flexibility, robustness, and autonomy. The presented framework makes use of the idea that heterogeneous real-world data of very different type must be processed by (and run through) several different layers, to be finally available in a suited format and at the right place to be accessible by high-level decision-making agents. In this sense, the presented approach shows how to abstract away from the raw real-world data step by step by means of semantic technologies. The paper concludes by demonstrating the benefits of the framework in a real scenario. A hardware fault is simulated in a REMUS 100 AUV while performing a mission. This triggers a knowledge exchange between the status monitoring agent and the adaptive mission planner embedded agent. By using the proposed framework, both services can interchange information while remaining domain independent during their interaction with the platform. The results of this paper are readily applicable to land and air robotics.

A Novel Knowledge-Based System for Interpreting Complex Engineering Drawings: Theory, Representation, and Implementation

We present a novel knowledge-based system to automatically convert real-life engineering drawings to content-oriented high-level descriptions. The proposed method essentially turns the complex interpretation process into two parts: knowledge representation and knowledge-based interpretation. We propose a new hierarchical descriptor-based knowledge representation method to organize the various types of engineering objects and their complex high-level relations. The descriptors are defined using an Extended Backus Naur Form (EBNF), facilitating modification and maintenance. When interpreting a set of related engineering drawings, the knowledge-based interpretation system first constructs an EBNF-tree from the knowledge representation file, then searches for potential engineering objects guided by a depth-first order of the nodes in the EBNF-tree. Experimental results and comparisons with other interpretation systems demonstrate that our knowledge-based system is accurate and robust for high-level interpretation of complex real-life engineering projects.

A knowledge-based system for cost modelling of aircraft gas turbines

This paper presents a novel technique for representing cost information of a candidate design. The underlying principle is to provide a knowledge-based system for cost modelling that makes the cost effects of design decisions more visible and quickly available to the designer. The development of a hybrid between hierarchical trees and object-oriented knowledge representation environment to represent cost information is shown. The development of an abstract product structure, reusable tree object libraries and an automated system to extract design information needed for estimation from computer-assisted design geometry is discussed. It is shown how the tool provides fast incremental cost fluctuations in response to changes in component geometry. This paper also considers the uncertain quantities present in cost models and analyses cost risk. A case study demonstrating the research applied to a Rolls-Royce aircraft gas turbine engine component is presented.

Reference metadata extraction using a hierarchical knowledge representation framework

The integration of bibliographical information on scholarly publications available on the Internet is an important task in the academic community. Accurate reference metadata extraction from such publications is essential for the integration of metadata from heterogeneous reference sources. In this paper, we propose a hierarchical template-based reference metadata extraction method for scholarly publications. We adopt a hierarchical knowledge representation framework called INFOMAP, which automatically extracts metadata. The experimental results show that, by using INFOMAP, we can extract author, title, journal, volume, number (issue), year, and page information from different kinds of reference styles with a high degree of precision. The overall average accuracy is 92.39% for the six major reference styles compared in this study.

Modelling biological processes using workflow and Petri Net models.

Biological processes can be considered at many levels of detail, ranging from atomic mechanism to general processes such as cell division, cell adhesion or cell invasion. The experimental study of protein function and gene regulation typically provides information at many levels. The representation of hierarchical process knowledge in biology is therefore a major challenge for bioinformatics. To represent high-level processes in the context of their component functions, we have developed a graphical knowledge model for biological processes that supports methods for qualitative reasoning. We assessed eleven diverse models that were developed in the fields of software engineering, business, and biology, to evaluate their suitability for representing and simulating biological processes. Based on this assessment, we combined the best aspects of two models: Workflow/Petri Net and a biological concept model. The Workflow model can represent nesting and ordering of processes, the structural components that participate in the processes, and the roles that they play. It also maps to Petri Nets, which allow verification of formal properties and qualitative simulation. The biological concept model, TAMBIS, provides a framework for describing biological entities that can be mapped to the workflow model. We tested our model by representing malaria parasites invading host erythrocytes, and composed queries, in five general classes, to discover relationships among processes and structural components. We used reachability analysis to answer queries about the dynamic aspects of the model. The model is available at http://smi.stanford.edu/projects/helix/pubs/process-model/.

Toward principles for the representation of hierarchical knowledge in formal ontologies

Early ontological engineering methodologies have necessarily focussed on the management of the whole ontology development process. There is a corresponding need to provide advice to the ontological engineer on the finer details of ontology construction. Here, we specifically address the representation of hierarchical relationships in an ontology. We identify five types of problem that may be encountered in moving from an informal description of a domain to a formal representation of hierarchical knowledge. Each problem type is discussed from the perspective of knowledge sharing and examples from biological ontologies are used to illustrate each type.

Knowledge-Based Approach in Intelligent Handwritten Form Processing

In this paper, we introduce a knowledge-based approach applied in the postprocessing of intelligent hand-written fax-form processing. This approach significantly improves recognition in application systems. The main topics covered in this article include (a) hierarchical representation of knowledge, (b) description of domain knowledge, and (c) inference mechanisms on the knowledge hierarchy.

AMS formalism

The Activity Manager System (AMS) formalism anchored on key concepts such as activity, action, activity network, state, and MOPA (Memory Organization Packets for Activities) is capable of modeling a broad spectrum of office activities, ranging from the structured to the ill-structured, in a rigorous and comprehensive manner. The formalism can also be used to guide the development of Office Information Systems (OISs). It is domain independent and is particularly suited for the hierarchical representation of procedural knowledge. The formalism has the following features: 1) As in the rule-based formalism, control is separated from knowledge, and programming is replaced by the explicit, declarative representation of packets of knowledge; 2) Using the activity network concept, we can obtain an explicit hierarchical representation of procedural knowledge; 3) The activity concept provides the formalism with a representation of a rule-like entity; and 4) The MOPA concept plays the role of organizing and reconstructing knowledge in an appropriate manner, and allows the dynamic generation of Activity Networks at various levels of abstraction. The formalism as a theoretical underpinning for the development of OISs has been demonstrated at Bull's Advanced Studies Laboratory by Ader (in developing the office model) and Li (in developing the AMS).

A Methodology for Knowledge Acquisition and Reasoning About Faults in Conditions of Incomplete Information

In this paper the problem of modelling knowledge about fault propagation and utilizing this model for reasoning in conditions of incomplete information is addressed. The term incomplete information is used to indicate the situations when available amount of information isn’t enough to build well known analytical or statistical models for problem of failure analysis. A solution that merges digraph and event tree failure analysis with three types of reasoning is presented. The methodology is divided into two phases. The first phase is structural modelling phase and the second phase is the model utilization phase for cause-consequence rule derivation. The results of these phases are the topological knowledge base and the deep knowledge rule base, respectively. The two knowledge bases are combined to form a novel hierarchical knowledge representation scheme. Upper levels of this hierarchy are made up of the structural models visualized by digraphs. Each level represents the system from the point of view of morphological and functional structure, respectively. The lowest level consists of a set of event trees. The two knowledge bases support three kinds of reasoning, namely, reasoning in logic, diagnostic and predictive reasoning. Formal methods for model building, event tree construction and derivation of cause-consequence rules are discussed.

AMS: a declarative formalism for hierarchical representation of procedural knowledge

This paper presents a domain-independent formalism, called Activity Manager System (AMS), for the explicit, hierarchical representation of procedural knowledge. The formalism is able to: (1) describe procedures at a reasonable level of complexity and completeness; (2) generate dynamically procedures at different levels of abstraction; (3) organize knowledge hierarchies by means of abstract entities; and (4) allow knowledge reusability at different levels of abstraction. AMS uses the notions of activity, activity network, state, and memory organization packet for activities (MOPA) to represent abstractions, and it allows the user to model an application in a hierarchical manner. The advantages and limitations of AMS are also discussed.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>