Classification analysis of microarray data based on ontological engineering

Background knowledge is important for data mining, especially in complicated situation. Ontological engineering is the successor of knowledge engineering. The sharable knowledge bases built on ontology can be used to provide background knowledge to direct the process of data mining. This paper gives a common introduction to the method and presents a practical analysis example using SVM (support vector machine) as the classifier. Gene Ontology and the accompanying annotations compose a big knowledge base, on which many researches have been carried out. Microarray dataset is the output of DNA chip. With the help of Gene Ontology we present a more elaborate analysis on microarray data than former researchers. The method can also be used in other fields with similar scenario.

Representation of an Electrical Engineering Curriculum Using an Ontology of Controlled Quality

A curriculum is a complex system that includes a set of core competencies, objectives, contents, methodological and evaluation criteria, regulation among other things. In order to represent a curriculum as a piece of software the common tools used are databases, trees and lists of courses. However, none of these tools can capture the deep and complex relationships among the elements of a curriculum. To avoid this problem, a more complete representation of an engineering curriculum using ontologies has been developed. This paper presents the construction of an ontology for undergraduate electrical engineering curriculum domain at Universidad Nacional de Colombia, which aims to represent, organize, formalize and standardize the knowledge of this domain, so that it can be shared and reused by different groups of people in the field of education and engineering. The ontology includes four curriculum aspects： knowledge in electrical engineering, skills in engineering, electrical engineering curriculum and regulation. For the ontology development, Methontology was selected as methodology and Protege as implementation tool. In addition, there is a proposal of documentation for this methodology, based on principles of quality management systems. This ontology is designed in order to be used in any field of engineering.

DEBRA: On the Unsupervised Learning of Concept Hierarchies from (Literary) Text

With this work, we introduce a novel method for the unsupervised learning of conceptual hierarchies, or concept maps as they are sometimes called, which is aimed specifically for use with literary texts, as such distinguishing itself from the majority of research literature on the topic which is primarily focused on building ontologies from a vast array of different types of data sources, both structured and unstructured, to support various forms of AI, in particular, the Semantic Web as envisioned by Tim Berners-Lee. We first elaborate on mutually informing disciplines of philosophy and computer science, or more specifically the relationship between metaphysics, epistemology, ontology, computing and AI, followed by a technically in-depth discussion of DEBRA, our dependency tree based concept hierarchy constructor, which as its name alludes to, constructs a conceptual map in the form of a directed graph which illustrates the concepts, their respective relations, and the implied ontological structure of the concepts as encoded in the text, decoded with standard Python NLP libraries such as spaCy and NLTK. With this work we hope to both augment the Knowledge Representation literature with opportunities for intellectual advancement in AI with more intuitive, less analytical, and well-known forms of knowledge representation from the cognitive science community, as well as open up new areas of research between Computer Science and the Humanities with respect to the application of the latest in NLP tools and techniques upon literature of cultural significance, shedding light on existing methods of computation with respect to documents in semantic space that effectively allows for, at the very least, the comparison and evolution of texts through time, using vector space math.

Applying gravitational search algorithm in the QoS-based Web service selection problem

With the growing use of service-oriented architecture for designing next generation software systems,the service composition problem and its execution complexity have become even more important in responding to different user requests.The gravitational search algorithm is one of the latest heuristic algorithms.It has a number of distinguishing features,such as rapid convergence,lower memory usage,and the use of particular parameters,for instance,the distance between the solutions.In this paper,we propose a model for the optimization of the Web service composition problem based on qualitative measures and the gravitational search algorithm.To determine the efficacy of this proposed model we solve the problem with the particle swarm optimization algorithm for comparison.Simulation results show that the gravitational search algorithm has a high potential and substantial efficiency in finding the best combination of Web services.

SVM-based Ontology Matching Approach

There are a lot of heterogeneous ontologies in semantic web, and the task of ontology mapping is to find their semantic relationship. There are integrated methods that only simply combine the similarity values which are used in current multi-strategy ontology mapping. The semantic information is not included in them and a lot of manual intervention is also needed, so it leads to that some factual mapping relations are missed. Addressing this issue, the work presented in this paper puts forward an ontology matching approach, which uses multi-strategy mapping technique to carry on similarity iterative computation and explores both linguistic and structural similarity. Our approach takes different similarities into one whole, as a similarity cube. By cutting operation, similarity vectors are obtained, which form the similarity space, and by this way, mapping discovery can be converted into binary classification. Support vector machine (SVM) has good generalization ability and can obtain best compromise between complexity of model and learning capability when solving small samples and the nonlinear problem. Because of the said reason, we employ SVM in our approach. For making full use of the information of ontology, our implementation and experimental results used a common dataset to demonstrate the effectiveness of the mapping approach. It ensures the recall ration while improving the quality of mapping results.

Extracting a justification for OWL ontologies by critical axioms

Extracting justifications for web ontology language(OWL)ontologies is an important mission in ontology engineering.In this paper,we focus on black-box techniques which are based on ontology reasoners.Through creating a recursive expansion procedure,all elements which are called critical axioms in the justification are explored one by one.In this detection procedure,an axiom selection function is used to avoid testing irrelevant axioms.In addition,an incremental reasoning procedure has been proposed in order to substitute series of standard reasoning tests w.r.t.satisfiability.It is implemented by employing a pseudo model to detect“obvious”satisfiability directly.The experimental results show that our proposed strategy for extracting justifications for OWL ontologies by adopting incremental expansion is superior to traditional Black-box methods in terms of efficiency and performance.