Ontological Knowledge Representation Research Articles

A Knowledge-Based Perspective for Software Process Modeling

As the acquisition and sharing of knowledge form the backbone of the software development process, it is important to identify knowledge discrepancies between the process elements. Explicit representation of the knowledge components within a software process model can provide a means to expose these discrepancies. This paper presents an extension of the Software and System Process Engineering Metamodel (SPEM), to be used as a new knowledge modeling layer. The approach, which is based on ontologies for knowledge representation, constitutes an explicit method for representing knowledge within process models. A concept matching indicator shows the state of the process model in terms of the concept gaps for each task within the process. This indicator could lead to more informed decision making and better management of the associated risks, in terms of team competency, documentation quality, and the training required to mitigate them.

Construction of Design Process Ontology Database Based on Process Ontology

Based on product design process knowledge reuse, according to the characteristics of ontology knowledge representation, process ontology knowledge representation method is offered. The paper extracts the category of design process, defines and describes the attribute and relationship of these categories. Then, the design process ontology database based on process ontology is constructed. As a result, it supports to achieve the sharing and reusing of design process knowledge.

Domain Ontology, an Instrument of Semantic Web Knowledge Management in e-Learning

Domain ontology is proposed as an instrument for controlling of e-learning course results. This approach instead of traditional testing more objectively reflects the structure of studentsâ?? knowledge. Use of ontologies for knowledge representation provides interoperability of the used testing systems. The domain knowledge has been found and reuse in the semantic web world. Domain ontology developing is based on the principles of ontological analysis. The method of itâ??s use for evaluation of the learnersâ?? knowledge results is based on matching of the learnersâ?? ontology with the reference ontology proposed by the trainer/tutor. A multi-agent system for e-learning (M(e)L ) has been defined and a prototype system has been developed. In a case, the domain ontology and the semantic web representation has been applied in two e-learning university courses.

Reducing the Complexities in the Cognition of Ontology Knowledge Representation

Reducing the Complexities in the Cognition of Ontology Knowledge Representation

Special issue on fuzzy ontologies and fuzzy markup language applications

Latest research on formal methods for knowledge representation based on semantic web technologies witnessed how ontologies have become a fundamental and critical component for developing applications in different real world scenarios. Indeed, ontologies formally model application domains to enable reuse of knowledge and allow people and software agents to share a common understanding of the structure of information. The body of a knowledge modeled through an ontological approach is based on the conceptualization idea, i.e., an abstract and simplified view of the knowledge related to given application scenario. However, it is widely pointed out that classical ontology model is not sufficient to deal with imprecise and vague knowledge strongly characterizing some real world applications. Soft computing techniques can enhance ontological knowledge representation by providing methods for directly dealing with imprecision and vagueness. Indeed, fuzzy set theory may extend the conventional ontology idea with a collection of fuzzy or vague terms like ‘‘creamy’’, ‘‘hot’’, ‘‘large’’, for which a clear and precise definition is not possible. In this scenario, the fuzzy markup language (FML) represents an important result because it allows fuzzy scientists to express their ‘‘imprecise’’ ideas in abstract and interoperable way by improving their productivity and, at the same time, increasing the average quality of their works. The objective of this special issue is to highlight an ongoing research on fuzzy and FML approaches for knowledge semantic representation based on ontologies, as well as their applications on various domains. This volume contains seven papers that consider different aspects of research on fuzzy ontologies and FML. The first four papers describe the applications exploiting fuzzy theory and ontology. Updating Generalized Association Rules with Evolving Fuzzy Taxonomies by WenYang Lin, Ja-Hwung Su, and Ming-Cheng Tseng proposes an algorithm for mining generalized association rules with fuzzy taxonomic structures where the taxonomy may change as time passes; as shown by their empirical evaluations, the proposed algorithms yield high performances even in high degree of taxonomy evolution. Crowdsourcing techniques to create a fuzzy subset of SNOMED CT for semantic tagging of medical documents by David T. Parry and Tsung-Chun Tsai describes an approach to identifying subsets of medical knowledge contained in the SNOMED CT dictionary via a crowdsourcing technique. The research aims to assist clinicians in coding small freetext documents such as radiology reports. Fuzzy Ontologies-based User Profiles applied to enhance e-Learning Activities by Mateus Ferreira-Satler, Francisco P. Romero, Victor H. Menendez-Dominguez, Alfredo Zapata and Manuel E. Prieto shows how a fuzzy ontology can be used to represent user profiles into a recommender engine and enhances the user’s activities into e-Learning environments. Decision making with a fuzzy ontology by Christer Carlsson, Matteo Brunelli and Jozsef Mezei shows how soft computing techniques, e.g. aggregation functions and interval valued fuzzy numbers, will support effective and practical decision making on the basis of the fuzzy G. Acampora (&) Department of Computer Science, University of Salerno, Salerno, Italy e-mail: gacampora@unisa.it

Agent‐based modeling and biomedical ontologies: a roadmap

AbstractThe translational dilemma represents a foundational challenge for the biomedical research community. Addressing the dilemma will require an enhancement in the throughput capacity of the expression and evaluation of mechanistic hypotheses. Doing so will require the ability to place biomedical knowledge into a format where hypotheses can be readily instantiated such that the dynamics inherent to biological systems can be represented, and also technological enhancement of the generation of such dynamic models. We suggest that the former goal can be approached by using the meta‐structure of agent‐based models (ABMs) to integrate different knowledge hierarchies currently represented with bio‐ontologies and increase the expressiveness of formal knowledge representation to account for mechanistic biological rules. The development of an agent‐based modeling format (ABMF) will provide a bridge between ontological knowledge representation and methods for modeling and simulation (M&S). We further suggest that the latter goal of process enhancement can be targeted by the development of intelligent computational agents that can concatenate bio‐ontologies with M&S ontologies to semiautomate the generation of bio‐simulations. We believe that the application of these two complementary approaches would address the foundational nature of the current throughput bottleneck in the scientific cycle. WIREs Comp Stat 2011 3 343–356 DOI: 10.1002/wics.167This article is categorized under: Statistical Models > Agent-Based Models Applications of Computational Statistics > Computational and Molecular Biology Software for Computational Statistics > Artificial Intelligence and Expert Systems Statistical Models > Simulation Models

OntoPhoto and the Role of Ontology in Organizing Knowledge

This article is concerned with ontology and its applications in Knowledge Organization (KO) activities. Connec- tions are drawn between efforts in artificial intelligence (AI) to capture the meaning of information and make it accessible to machines and the efforts made in libraries to use KO tools in machine-based record building and search and retrieval systems. The practices used in AI that are of interest here include ontology and ontology-based knowledge representation. In this article their applications in KO are directed towards a particularly problematic document type—the photograph. There are two argu- ments motivating this article. First, ontology-based KO systems that join AI techniques with library cataloging practices make it possible to utilize higher levels of expressivity when describing photographs. Second, KO systems for photographs that are capable of reasoning over concepts and relationships can potentially provide richer, more relevant search results than systems utilizing word-matching alone.

Automatic web pages hierarchical classification using dynamic domain ontologies

The use of ontologies for knowledge representation has had a fast increase in the last years and they are used in several application context. One of these challenging applications is the web. Managing large amount of information on internet needs more efficient and effective methods and techniques for mining and representing information. In this article, we present a methodology for automatic topic annotation of web pages. We describe an algorithm for words disambiguation using an apposite metric for measuring the semantic relatedness and we show a technique which allows to detect the topic of the analysed document using ontologies extracted from a knowledge base. The strategy is implemented in a system where these information are used to build a topic hierarchy automatically created and not a priori defined for classifying web pages. Experimental results are presented and discussed in order to measure the effectiveness of our approach.

Knowledge Representation and Information Management for Financial Risk Management: Report of a Workshop

The National Science Foundation and The Pew Charitable Trusts co‐sponsored a Workshop on Knowledge Representation and Information Management for Financial Risk Management on July 21 and 22, 2010 in Arlington, Virginia (see Flood, Kyle and Raschid, 2010). The goal of the workshop was to initiate a research discussion about the knowledge representation challenges for effective financial information management. Over fifty invited academic researchers, financial regulators and industry practitioners participated in the event. The participants brought diverse perspectives and expertise in economics, computer science, finance, and information science, resulting in an interdisciplinary exchange of ideas. The specific topics covered a broad range, including financial risk management, ontologies for knowledge representation, formal logics, schema mapping, systemic risk, constraint languages, networks, simulation techniques, data integrity, operational risk and data security, to name a few. This report describes the discussions flowing from the workshop and its immediate aftermath in greater detail. We hope, however, that this is only the beginning of a much longer conversation.

Computational Environments with Multimodal Representations of Architectural Design Knowledge

This article discusses interaction between multimodal representations of architectural design knowledge, particularly focusing on relating explicit and implicit types of information. The aim of the presented research is to develop a computational environment that combines several modes of representation, including and integrating different forms of architectural design knowledge. Development of an interactive digital-models library and ontological model of architectural design factors are discussed, which are complementary in nature. In a time when BIM software is seen as embodiment of domain knowledge and the future medium of architectural design, this paper presents an interaction between ontological representation of architectural design knowledge and its embodiment in interactive models, thus focusing on the process of design and design space exploration. In the digital environments that we propose, representation of different formats of knowledge, such as visual, linguistic or numeric, are integrated with relational and procedural information, design rules, and characteristics. Interactive search and query based on contextual constraints, and parametric variation of the model based on the information received from ontology are the underlying drivers for design exploration and development.

Exploring e-Learning Knowledge Through Ontological Memetic Agents

E-Learning systems have proven to be fundamental in several areas of tertiary education and in business companies. There are many significant advantages for people who learn online such as convenience, portability, flexibility and costs. However, the remarkable velocity and volatility of modern knowledge due to the exponential growth of the World Wide Web, requires novel learning methods that offer additional features such as information structuring, efficiency, task relevance and personalization. This paper proposes a novel multi-agent e-Learning system empowered with (ontological) knowledge representation and memetic computing to efficiently manage complex and unstructured information that characterize e-Learning. In particular, differing from other similar approaches, our proposal uses (1) ontologies to provide a suitable method for modeling knowledge about learning content and activities, and (2) memetic agents as intelligent explorers in order to create ?in time? and personalized e-Learning experiences that satisfy learners' specific preferences. The proposed method has been tested by realizing a multi-agent software plug-in for an industrial e-Learning platform with experimentations to validate our memetic proposal in terms of flexibility, efficiency and interoperability.

Design and implementation of ontology-based knowledge base system for marine hydrocarbon geology

In order to efficiently apply and effectively spread the knowledge of marine hydrocarbon,in terms of the ontology for knowledge representation,a variety of documents were translated into standard TXT documents in which all the clauses were segmented into a set of words by marine-facies and relevant ones to extract ontology concepts from marine-facies geology.On the basis of B/S infrastructure,the knowledge system of marine hydrocarbon was successfully implemented,on which the marine-facies knowledge could be automatically acquired from a variety of documents.The maintainable function of the knowledge system for majority words and relevant words guarantees the accuracy of knowledge acquisition.The knowledge of marine hydrocarbon was retrieved quickly and shared widely.The knowledge system is proved to be reliable,efficient and practical.

A Semantic Web-Based Approach for Building Personalized News Services

This article proposes Hermes, a Semantic Web-based framework for building personalized news services. It makes use of ontologies for knowledge representation, natural language processing techniques for semantic text analysis, and semantic query languages for specifying wanted information. Hermes is supported by an implementation of the framework, the Hermes News Portal, a tool which allows users to have a personalized online access to news items. The Hermes framework and its associated implementation aim at advancing the state-of-the-art of semantic approaches for personalized news services by employing Semantic Web standards, exploiting domain information, using a word sense disambiguation procedure, and being able to express temporal constraints for the desired news items.

Semantic Image Segmentation and Object Labeling

In this paper, we present a framework for simultaneous image segmentation and object labeling leading to automatic image annotation. Focusing on semantic analysis of images, it contributes to knowledge-assisted multimedia analysis and bridging the gap between semantics and low level visual features. The proposed framework operates at semantic level using possible semantic labels, formally represented as fuzzy sets, to make decisions on handling image regions instead of visual features used traditionally. In order to stress its independence of a specific image segmentation approach we have modified two well known region growing algorithms, i.e., watershed and recursive shortest spanning tree, and compared them to their traditional counterparts. Additionally, a visual context representation and analysis approach is presented, blending global knowledge in interpreting each object locally. Contextual information is based on a novel semantic processing methodology, employing fuzzy algebra and ontological taxonomic knowledge representation. In this process, utilization of contextual knowledge re-adjusts labeling results of semantic region growing, by means of fine-tuning membership degrees of detected concepts. The performance of the overall methodology is evaluated on a real-life still image dataset from two popular domains

Fusion-based knowledge logistics for intelligent decision support in network-centric environment

An efficient knowledge sharing between multiple participating parties is required to provide for situation awareness and consequently to manage any networked organization. Thereby, it is necessary that the right knowledge from distributed sources is integrated and transferred to the right person within the right context at the right time to the right purpose. The aggregate of these interrelated activities is referred to as knowledge logistics (KL). The paper presents an approach that implements KL for an intelligent decision support. The approach assumes an ontological knowledge representation model based on the paradigm of object-oriented constraint networks. This makes it possible to perform problem solving by directly extracting slices of the common ontology and putting them into constraint solvers such as ILOG. Humanitarian relief operations are considered here as one of the approach applications what is illustrated via a case study of on-the-fly portable hospital configuration.

Towards a cognitive foundation for knowledge representation

Abstract. Knowledge engineering, knowledge management and conceptual modelling are concerned with representing knowledge of business and organizational domains. These research areas use ontologies for knowledge representation. Ontologies are understood either in the philosophical sense as firm metaphysical commitments or in the looser sense of dictionaries or taxonomies.This paper critically examines the understanding and use of ontologies and knowledge representation languages in information systems (IS) research and application. As ontologies are intended to be conceptualizations of a perceived reality, they should reflect the empirically observed reality. This motivates proposing psychology of language as a reference discipline for knowledge engineering and knowledge management. Natural language is argued to reflect the cognitive concepts we use to think about and perceive the world around us. These cognitive concepts are the relevant terms with which to structure and represent knowledge about the world.Psychology of language can provide empirical justification for a particular set of concepts to represent knowledge. This paper draws on psycho‐linguistic research to develop a proposal for a system of cognitive structures. This is argued to provide the relevant concepts on which to found knowledge representation schemata for knowledge engineering, knowledge management and conceptual modelling.

Diagnosis based on explicit means-end models

This article describes three diagnostic methods for use with industrial processes. They are measurement validation, i.e., consistency checking of sensor and measurement values using any redundancy of instrumentation; alarm analysis, i.e., analysis of multiple alarm situations to find which alarms are directly connected to primary faults and which alarms are consequential effects of the primary ones; and fault diagnosis, i.e., a search for the causes of and remedies for faults. The three methods use multilevel flow models (MFM), to describe the target process. They have been implemented in the real-time expert system tool G2, in C, and in Common Lisp, and successfully tested on simulations of several processes.The knowledge representation ontology used is based on the notion of flows, of mass, energy, and information, which are used to describe physical systems. The relationships between structure and function of a system is described by teleological relations, which connect the flow structures into a graph, built at model construction time. This allows the diagnostic reasoning to be implemented as searches in a static graph structure, and it can thus be performed extremely rapidly. As with other model-based approaches, general algorithms are used over a representation with generative capacities. The representation gains strength from being functional with a very abstract physical level, more abstract than most qualitative physics models. It works well with systems that can be described using flows, while it currently lacks the capability of capturing important aspects of other types of systems, for example, electronic circuits.