Structured Knowledge Representation Research Articles

DECISIONAL DNA: THE CONCEPT AND ITS IMPLEMENTATION PLATFORMS

Knowledge and experience engineering techniques are becoming increasingly useful and popular components of hybrid integrated systems used to solve complex real-life problems in different disciplines. These techniques offer features such as learning from experience, handling noisy and incomplete data, helping with decision making, and predicting capabilities. In this article, we present a number of different applications of a multidomain knowledge representation structure called decisional DNA that can be implemented and shared for the exploitation of embedded knowledge within different technologies.

Decisional DNA: A multi-technology shareable knowledge structure for decisional experience

Knowledge representation and engineering techniques are becoming useful and popular components of hybrid integrated systems used to solve complicated practical problems in different disciplines. These techniques offer features such as: learning from experience, handling noisy and incomplete data, helping with decision making, and predicting capabilities. In this paper, we present a multi-domain knowledge representation structure called Decisional DNA that can be implemented and shared for the exploitation of embedded knowledge in multiple technologies. Decisional DNA, as a knowledge representation structure, offers great possibilities on gathering explicit knowledge of formal decision events as well as a tool for decision making processes. Its applicability is shown in this paper when applied to different decisional technologies. The main advantages of using the Decisional DNA rely on: (i) versatility and dynamicity of the knowledge structure, (ii) storage of day-to-day explicit experience in a single structure, (iii) transportability and shareability of the knowledge, and (iv) predicting capabilities based on the collected experience. Thus, after analysis and results, we conclude that the Decisional DNA, as a unique multi-domain structure, can be applied and shared among multiple technologies while enhancing them with predicting capabilities and facilitating knowledge engineering processes inside decision making systems.

Ontology-based semantic similarity: A new feature-based approach

Estimation of the semantic likeness between words is of great importance in many applications dealing with textual data such as natural language processing, knowledge acquisition and information retrieval. Semantic similarity measures exploit knowledge sources as the base to perform the estimations. In recent years, ontologies have grown in interest thanks to global initiatives such as the Semantic Web, offering an structured knowledge representation. Thanks to the possibilities that ontologies enable regarding semantic interpretation of terms many ontology-based similarity measures have been developed. According to the principle in which those measures base the similarity assessment and the way in which ontologies are exploited or complemented with other sources several families of measures can be identified. In this paper, we survey and classify most of the ontology-based approaches developed in order to evaluate their advantages and limitations and compare their expected performance both from theoretical and practical points of view. We also present a new ontology-based measure relying on the exploitation of taxonomical features. The evaluation and comparison of our approach’s results against those reported by related works under a common framework suggest that our measure provides a high accuracy without some of the limitations observed in other works.

The Choice between MapMan and Gene Ontology for Automated Gene Function Prediction in Plant Science.

Since the introduction of the Gene Ontology (GO), the analysis of high-throughput data has become tightly coupled with the use of ontologies to establish associations between knowledge and data in an automated fashion. Ontologies provide a systematic description of knowledge by a controlled vocabulary of defined structure in which ontological concepts are connected by pre-defined relationships. In plant science, MapMan and GO offer two alternatives for ontology-driven analyses. Unlike GO, initially developed to characterize microbial systems, MapMan was specifically designed to cover plant-specific pathways and processes. While the dependencies between concepts in MapMan are modeled as a tree, in GO these are captured in a directed acyclic graph. Therefore, the difference in ontologies may cause discrepancies in data reduction, visualization, and hypothesis generation. Here provide the first systematic comparative analysis of GO and MapMan for the case of the model plant species Arabidopsis thaliana (Arabidopsis) with respect to their structural properties and difference in distributions of information content. In addition, we investigate the effect of the two ontologies on the specificity and sensitivity of automated gene function prediction via the coupling of co-expression networks and the guilt-by-association principle. Automated gene function prediction is particularly needed for the model plant Arabidopsis in which only half of genes have been functionally annotated based on sequence similarity to known genes. The results highlight the need for structured representation of species-specific biological knowledge, and warrants caution in the design principles employed in future ontologies.

Complex Structured Decision Making Model: A hierarchical frame work for complex structured data

We introduce a hierarchical framework we call Complex Structured Decision Making model for complexly structured knowledge representation in intelligent decision making. We show that our model extends non-hierarchical (flat) decision making models to hierarchical decision making models that are similar to comprehensible human decision making processes. Further, we make an argument that hierarchial representation of knowledge in a Complex Structured Decision Making Model simplifies the approximation of aggregation functions to easily adapt to the underline relation of the system. Additionally, using a real world complex structured data set, we show that hierarchically organized Fuzzy Integrals, e.g. Choquet Integral, and Sugeno Integral and Fuzzy Signatures outperform these non-hierarchical Fuzzy Integrals.

Semantic Manipulations and Formal Ontology for Machine Learning based on Concept Algebra

Towards the formalization of ontological methodologies for dynamic machine learning and semantic analyses, a new form of denotational mathematics known as concept algebra is introduced. Concept Algebra (CA) is a denotational mathematical structure for formal knowledge representation and manipulation in machine learning and cognitive computing. CA provides a rigorous knowledge modeling and processing tool, which extends the informal, static, and application-specific ontological technologies to a formal, dynamic, and general mathematical means. An operational semantics for the calculus of CA is formally elaborated using a set of computational processes in real-time process algebra (RTPA). A case study is presented on how machines, cognitive robots, and software agents may mimic the key ability of human beings to autonomously manipulate knowledge in generic learning using CA. This work demonstrates the expressive power and a wide range of applications of CA for both humans and machines in cognitive computing, semantic computing, machine learning, and computational intelligence.

Using Social Network Analysis to Support Collective Decision-Making Process

Current traditional technologies, while enabling effective knowledge sharing and accumulation, seem to be less supportive of knowledge organization, use and consensus formation, as well as of collaborative decision making process. To address these limitations and thus to better foster collective decision-making around complex and controversial problems, a new family of tools is emerging able to support more structured knowledge representations known as collaborative argument mapping tools. This paper argues that online collaborative argumentation has the rather unique feature of combining knowledge organization with social mapping and that such a combination can provide interesting insights on the social processes activated within a collaborative decision making initiative. In particular, the authors investigate how Social Network Analysis can be used for the analysis of the collective argumentation process to study the structural properties of the concepts and social networks emerging from users’ interaction. Using Cohere, an online platform designed to support collaborative argumentation, some empirical findings obtained from two use cases are presented.

Privacy protection of textual attributes through a semantic-based masking method

Using microdata provided by statistical agencies has many benefits from the data mining point of view. However, such data often involve sensitive information that can be directly or indirectly related to individuals. An appropriate anonymisation process is needed to minimise the risk of disclosure. Several masking methods have been developed to deal with continuous-scale numerical data or bounded textual values but approaches to tackling the anonymisation of textual values are scarce and shallow. Because of the importance of textual data in the Information Society, in this paper we present a new masking method for anonymising unbounded textual values based on the fusion of records with similar values to form groups of indistinguishable individuals. Since, from the data exploitation point of view, the utility of textual information is closely related to the preservation of its meaning, our method relies on the structured knowledge representation given by ontologies. This domain knowledge is used to guide the masking process towards the merging that best preserves the semantics of the original data. Because textual data typically consist of large and heterogeneous value sets, our method provides a computationally efficient algorithm by relying on several heuristics rather than exhaustive searches. The method is evaluated with real data in a concrete data mining application that involves solving a clustering problem. We also compare the method with more classical approaches that focus on optimising the value distribution of the dataset. Results show that a semantically grounded anonymisation best preserves the utility of data in both the theoretical and the practical setting, and reduces the probability of record linkage. At the same time, it achieves good scalability with regard to the size of input data.

GAINING KNOWLEDGE THROUGH EXPERIENCE: DEVELOPING DECISIONAL DNA APPLICATIONS IN ROBOTICS

In this article, we explore an approach that integrates Decisional DNA, a domain-independent, flexible, and standard knowledge representation structure, with robots in order to test the usability and suitability of this novel knowledge representation structure. Core issues in using this Decisional DNA–based method include capturing of knowledge, storage and indexing of knowledge, organization of the knowledge base memory, and retrieval of knowledge from memory according to current problems. We demonstrate our approach in a set of experiments in which the robots capture knowledge from their tasks and are able to reuse such knowledge in subsequent tests.

Reasoning with the finitely many-valued Łukasiewicz fuzzy Description Logic [formula omitted

Fuzzy Description Logics are a formalism for the representation of structured knowledge affected by imprecision or vagueness. They have become popular as a language for fuzzy ontology representation. To date, most of the work in this direction has focused on the so-called Zadeh family of fuzzy operators (or fuzzy logic), which has several limitations. In this paper, we generalize existing proposals and show how to reason with a fuzzy extension of the logic SROIQ , the logic behind the language OWL 2, under finitely many-valued Łukasiewicz fuzzy logic. We show for the first time that it is decidable over a finite set of truth values by presenting a reasoning preserving procedure to obtain a non-fuzzy representation for the logic. This reduction makes it possible to reuse current representation languages as well as currently available reasoners for ontologies.

Parsimony Analysis – a Novel Method of Structured Knowledge Representation for Teaching Computer Science

Knowledge may be structured in a manner that may either handicap or improve learning outcomes. Knowledge construction is therefore an important aspect of the learning and assessment process. This paper is primarily concerned with structural knowledge (knowing how) of which there are numerous representations and mapping methods each with their associated strengths and weaknesses. This paper presents the novel application of parsimony analysis for organizing and defining structural knowledge within the field of computer science. Work to date has clearly demonstrated there are considerable potential pedagogical advantages to this approach.

An Analysis of Description Logic Augmented with Domain Rules for the Development of Product Models

The languages and logical formalisms developed by information scientists and logicians concentrate on the theory of languages and logical theorem proving. These languages, when used by domain experts to represent their domain of discourse, most often have issues related to the level of expressiveness and need specific extensions. In this paper, we first analyze the requirements for the development of structured knowledge representation models for manufacturing products. We then explore how these requirements can be satisfied through the levels of logical formalisms and expressivity of a structured knowledge representation model. We report our analysis of description logic (DL) and domain-specific rules with respect to the requirements by giving an example of a product ontology developed with ontology web language-description logic (OWL) and augmented with semantic web rule language (SWRL) rules. Clearly, increasing the expressivity of a product ontology also improves that of domain-specific rules, but there exits the usual tradeoff between the expressivity of languages and the complexity of their reasoning tasks. We present a case study of an electromechanical product to validate the analysis and further show how the OWL-DL reasoner together with the rule engine can enable reasoning about the product ontology. We finally discuss the open issues such as capabilities and limitations related to the usage of DL, OWL, and SWRL for product modeling.

Уступительность как метатекстовое явление

Уступительность как метатекстовое явление

Automatic Defect Classification System in Semiconductors EDS Test Based on System Entity Structure Methodology

We exploit a structural knowledge representation scheme called System Entity Structure (SES) methodology to represent and manage wafer failure patterns which can make a significant influence to FABs in the semiconductor industry. It is important for the engineers to simulate various system verification processes by using predefined system entities (e.g., decomposition, taxonomy, and coupling relationships of a system) contained in the SES. For better computational performance, given a certain failure pattern, a Pruned SES (PES) can be extracted by selecting the only relevant system entities from the SES. Therefore, the SES-based simulation system allows the engineers to efficiently evaluate and monitor semiconductor data by i) analyzing failures to find out the corresponding causes and ii) managing historical data related to such failures.

Ontology‐based concept map for planning a personalised learning path

AbstractDeveloping personalised web‐based learning systems has been an important research issue in e‐learning because no fixed learning pathway will be appropriate for all learners. However, most current web‐based learning platforms with personalised curriculum sequencing tend to emphasise the learner preferences and interests in relation to personalised learning services but fail to consider the difficulty level of course materials, learning order of prior and posterior knowledge and learner abilities while constructing a personalised learning path. As a result, these ignored factors thus easily lead to the generation of poor quality learning paths. Generally, learners could generate cognitive overload or fall into cognitive disorientation owing to inappropriate curriculum sequencing during learning processes, thus, reducing the learning effect. With the advancement of artificial intelligence technologies, ontology technologies enable a linguistic infrastructure to represent conceptual relationships between course materials. Ontology can be served as a structured knowledge representation scheme, capable of assisting the construction of a personalised learning path. This study thus proposes a novel genetic‐based curriculum sequencing scheme based on a generated ontology‐based concept map, which can be automatically constructed by the pretest results of numerous learners, to plan appropriate learning paths for individual learners. The experimental results indicated that the proposed approach could create high‐quality learning paths for individual learners. The proposed approach thus can help learners to learn more effectively and to likely reduce learners' cognitive overloads during learning processes.

Reducing one class of machine learning algorithms to logical operations of plausible reasoning

The ways to transform a wide class of machine learning algorithms into processes of plausible reasoning based on known deductive and inductive rules of inference are shown. The employed approach to machine learning problems is based on the concept of a good classification (diagnostic) test for a given set of positive and negative examples. The problem of inferring all good diagnostic tests is to search for the best approximations of the given classification (partition or the partitioning) on the established set of examples. The theory of algebraic lattice is used as a mathematical language to construct algorithms of inferring good classification tests. The advantage of the algebraic lattice is that it is given both as a declarative structure, i.e., the structure for knowledge representation, and as a system of dual operations used to generate elements of this structure. In this work, algorithms of inferring good tests are decomposed into subproblems and operations that are the main rules of plausible human inductive and deductive reasoning. The process of plausible reasoning is considered as a sequence of three mental acts: implementing the rule of reasoning (inductive or deductive)with obtaining a new assertion, refining the boundaries of reasoning domain, and choosing a new rule of reasoning (deductive or inductive one).

Intensional dynamic programming. A Rosetta stone for structured dynamic programming

We present intensional dynamic programming ( IDP), a generic framework for structured dynamic programming over atomic, propositional and relational representations of states and actions. We first develop set-based dynamic programming and show its equivalence with classical dynamic programming. We then show how to describe state sets intensionally using any form of structured knowledge representation and obtain a generic algorithm that can optimally solve large, even infinite, MDPs without explicit state space enumeration. We derive two new Bellman backup operators and algorithms. In order to support the view of IDP as a Rosetta stone for structured dynamic programming, we review many existing techniques that employ either propositional or relational knowledge representation frameworks.

Associative Naïve Bayes classifier: Automated linking of gene ontology to medline documents

We demonstrate a text-mining method, called associative Naïve Bayes (ANB) classifier, for automated linking of MEDLINE documents to gene ontology (GO). The approach of this paper is a nontrivial extension of document classification methodology from a fixed set of classes C = { c 1 , c 2 , … , c n } to a knowledge hierarchy like GO. Due to the complexity of GO, we use a knowledge representation structure. With that structure, we develop the text mining classifier, called ANB classifier, which automatically links Medline documents to GO. To check the performance, we compare our datasets under several well-known classifiers: NB classifier, large Bayes classifier, support vector machine and ANB classifier. Our results, described in the following, indicate its practical usefulness.

An investigation into sustainable product conceptualization using a design knowledge hierarchy and Hopfield network

In recent years, there has been a paradigm shift on providing design services from manufacturing/marketing to sustainability. Although a number of studies have been conducted on sustainable issues in many aspects, impacts such as multidisciplinary integration and product life-cycle consideration have not been thoroughly investigated. Therefore, it is worthwhile to investigate into the integration of functional, marketing, and commercial perspectives with product sustainability in the early stage of product conceptualization. In this study, a sustainable product conceptualization system (SPCS) is proposed. The product platform of a specific product is first generated by designers or domain experts using general sorting, a requirement acquisition technique, and design knowledge hierarchy (DKH), a knowledge representation structure. Based on the product platform, initial design options can be obtained using morphological configuration, i.e., combining different part options. Subsequently, the Hopfield network is used to narrow down initial design space based on initial design criteria solicited by domain experts. To select the preferred design options for the purpose of sustainable product conceptualization, the sustainability-cost pairs can be obtained based on the rated sustainability and cost criteria solicited using repertory grids by domain experts. Consequently, the results are discussed on the coordination between product sustainability degree and cost efficiency. A case study on cellular-phone design is conducted for system illustration.

Iterative reconstruction of a global metabolic model of Acinetobacter baylyi ADP1 using high-throughput growth phenotype and gene essentiality data

BackgroundGenome-scale metabolic models are powerful tools to study global properties of metabolic networks. They provide a way to integrate various types of biological information in a single framework, providing a structured representation of available knowledge on the metabolism of the respective species.ResultsWe reconstructed a constraint-based metabolic model of Acinetobacter baylyi ADP1, a soil bacterium of interest for environmental and biotechnological applications with large-spectrum biodegradation capabilities. Following initial reconstruction from genome annotation and the literature, we iteratively refined the model by comparing its predictions with the results of large-scale experiments: (1) high-throughput growth phenotypes of the wild-type strain on 190 distinct environments, (2) genome-wide gene essentialities from a knockout mutant library, and (3) large-scale growth phenotypes of all mutant strains on 8 minimal media. Out of 1412 predictions, 1262 were initially consistent with our experimental observations. Inconsistencies were systematically examined, leading in 65 cases to model corrections. The predictions of the final version of the model, which included three rounds of refinements, are consistent with the experimental results for (1) 91% of the wild-type growth phenotypes, (2) 94% of the gene essentiality results, and (3) 94% of the mutant growth phenotypes. To facilitate the exploitation of the metabolic model, we provide a web interface allowing online predictions and visualization of results on metabolic maps.ConclusionThe iterative reconstruction procedure led to significant model improvements, showing that genome-wide mutant phenotypes on several media can significantly facilitate the transition from genome annotation to a high-quality model.