An integrated catchment-coastal modelling system for real-time water quality forecasts

This paper presents a new marine water quality forecasting system for real-time and short-term predictions. The forecasting system comprises an integrated catchment-coastal model and a database management system. The integrated model is validated in an Irish catchment-coastal system using hydrodynamic and water quality data. The forecasting system was then used to provide short-term and real-time forecasts of Escherichia coli (E. coli) and Intestinal Enterococci concentrations (IE) in the near-shore coastal waters of Bray, Ireland. Two hind-cast scenarios were simulated: 5F in which predictions were based on rainfall forecasts only; and I-5F where forecasts of 5F were improved by incorporating real-time data. Results indicate that predictions of E. coli of scenario I-5F are improved. Also predicted IE concentrations by Scenario 5F were comparably higher than the I-5F predications, but due to the wide scatter of observed IE concentrations, the superiority of one scenario over the second could not be definitively determined.     

A new method to forecast of Escherichia coli promoter gene sequences: Integrating feature selection and Fuzzy-AIRS classifier system

We have investigated the real-world task of recognizing biological concepts in DNA sequences in this work. Recognizing promoters in strings that represent nucleotides (one of A, G, T, or C) has been performed using a novel approach based on feature selection (FS) and Artificial Immune Recognition System (AIRS) with Fuzzy resource allocation mechanism (Fuzzy-AIRS), which is first proposed by us. The aim of this study is to improve the prediction accuracy of Escherichia coli promoter gene sequences using a novel system based on FS and Fuzzy-AIRS. The E. coli promoter gene sequences dataset has 57 attributes and 106 samples including 53 promoters and 53 non-promoters. The proposed system consists of two parts. Firstly, we have reduced the dimension of E. coli promoter gene sequences dataset from 57 attributes to 4 attributes by means of FS process. Second, Fuzzy-AIRS classifier algorithm has been run to predict the E. coli promoter gene sequences. The robustness of the proposed method is examined using prediction accuracy, sensitivity and specificity analysis, k-fold cross-validation method and confusion matrix. Whilst only Fuzzy-AIRS classifier has obtained 50% prediction accuracy using 10-fold cross-validation, the proposed system has obtained 90% prediction accuracy in the same conditions. These obtained results have indicated that the proposed system obtain the success rate in recognizing promoters in strings that represent nucleotides.     

Measuring the understanding between two agents through concept similarity

Two agents previously unknown to each other cannot communicate by exchanging concepts (nodes of their own ontology): they need to use a common communication language. If they do not use a standard protocol, most likely they use a natural language. The ambiguities of it, and the different concepts the agents possess, give rise to imperfect understanding among them: How closely concepts in ontology O_A map¹ to which of O_B? Can we measure these mismatches?Given a concept from ontology O_A, a method is provided to find the most similar concept in O_B, and to measure the similarity between both concepts. The paper also gives an algorithm to gauge du(A, B), the degree of understanding that agent A has about the ontology of B. The procedures use word comparison, since no agent (except the Very Wise Creature, VWC) can measure du directly. Examples are given.     

Automated ontology instantiation from tabular web sources—The AllRight system

The process of populating an ontology-based system with high-quality and up-to-date instance information can be both time-consuming and prone to error. In many domains, however, one possible solution to this problem is to automate the instantiation process for a given ontology by searching (mining) the web for the required instance information.The primary challenges facing such system include: (a) efficiently locating web pages that most probably contain the desired instance information, (b) extracting the instance information from a page, and (c) clustering documents that describe the same instance in order to exploit data redundancy on the web and thus improve the overall quality of the harvested data. In addition, these steps should require as little seed knowledge as possible.In this paper, the AllRight ontology instantiation system is presented, which supports the full instantiation life-cycle and addresses the above-mentioned challenges through a combination of new and existing techniques. In particular the system was designed to deal with situations where the instance information is given in tabular form. The main innovative pillars of the system are a new high-recall focused crawling technique (xCrawl), a novel table recognition algorithm, innovative methods for document clustering and instance name recognition, as well as techniques for fact extraction, instance generation and query-based fact validation.The successful evaluation of the system in different real-world application scenarios shows that the ontology instantiation process can be successfully automated using only a very limited amount of seed knowledge.     

Ontology-based facility data model for energy management

Context: Definition of a comprehensive facility data model is a prerequisite for providing more advanced energy management systems capable of tackling the underlying heterogeneity of complex infrastructures, thus providing more flexible data interpretation and event management, advanced communication and control system capabilities. Objective: This paper proposes one of the possible implementations of a facility data model utilizing the concept of ontology as part of the contemporary Semantic Web paradigm. Method: The proposed facility ontology model was defined and developed to model all the static knowledge (such as technical vendor data, proprietary data types, and communication protocols) related to the significant energy consumers of the target infrastructure. Furthermore, this paper describes the overall methodology and how the common semantics offered by the ontology were utilized to improve the interoperability and energy management of complex infrastructures. Initially, a core facility ontology, which represents the generic facility model providing the general concepts behind the modelling, was defined. Results: In order to develop a full-blown model of the specific facility infrastructure, Malpensa and Fiumicino airports in Italy were taken as a test-bed platform in order to develop the airport ontology owing to the variety of the technical systems installed at the site. For the development of the airport ontology, the core facility ontology was first extended and then populated to reflect the actual state of the target airport facility. Conclusion: The developed ontology was tested in the environment of the two pilots, and the proposed solution proved to be a valuable link between separate ICT systems involving equipment from various vendors, both on syntax and semantic level, thus offering the facility managers the ability to retrieve high-level information regarding the performance of significant energy consumers.     

Ontology Integration: Approaches and Challenging Issues

In recent years, the decentralized development of ontologies has led to the generation of multiple ontologies of overlapping knowledge. This heterogeneity problem can be tackled by integrating existing ontologies to build a single coherent one. Ontology integration has been investigated during the last two decades, but it is still a challenging task. In this article, we provide a comprehensive survey of all ontology integration aspects. We discuss related notions and scrutinize existing techniques and literature approaches. We also detail the role of ontology matching in the ontology integration process. Indeed, the ontology community has adopted the splitting of the ontology integration problem into matching, merging and repairing sub-tasks, where matching is a necessary preceding step for merging, and repairing can be included in the matching process or performed separately. Ontology matching and merging systems have become quite proficient, however the trickiest part lies in the repairing step. We also focus on the case of a holistic integration of multiple heterogeneous ontologies, which needs further exploration. Finally, we investigate challenges, open issues, and future directions of the ontology integration and matching areas.     

Ontology-based concept similarity in Formal Concept Analysis

Both domain ontologies and Formal Concept Analysis (FCA) aim at modeling concepts, although with different purposes. In the literature, a promising research area concerns the role of FCA in ontology engineering, in particular, in supporting the critical task of reusing independently developed domain ontologies. With this regard, the possibility of evaluating concept similarity is acquiring an increasing relevance, since it allows the identification of different concepts that are semantically close. In this paper, an ontology-based method for assessing similarity between FCA concepts is proposed. Such a method is intended to support the ontology engineer in difficult activities that are becoming fundamental in the development of the Semantic Web, such us ontology merging and ontology mapping and, in particular, it can be used in parallel to existing semi-automatic tools relying on FCA.     

IT2FS-based ontology with soft-computing mechanism for malware behavior analysis

Antimalware application is one of the most important research issues in the area of cyber security threat. Nowadays, because hackers continuously develop novel techniques to intrude into computer systems for various reasons, many security researchers should analyze and track new malicious program to protect sensitive and valuable information in the organization. In this paper, we propose a novel soft-computing mechanism based on the ontology model for malware behavioral analysis: Malware Analysis Network in Taiwan (MAN in Taiwan, MiT). The core techniques of MiT contain two parts listed as follows: (1) collect the logs of network connection, registry, and memory from the operation system on the physical-virtual hybrid analysis environment to get and extract more unknown malicious behavior information. The important information is then extracted to construct the ontology model by using the Web Ontology Language and Fuzzy Markup Language. Additionally, MiT is also able to automatically provide and share samples and reports via the cloud storage mechanism; (2) apply the techniques of Interval Type-2 Fuzzy Set to construct the malware analysis domain knowledge, namely the Interval Type-2 Fuzzy Malware Ontology (IT2FMO), for malware behavior analysis. Simulation results show that the proposed approach can effectively execute the malware behavior analysis, and the constructed system has also released under GNU General Public License version 3. In the future, the system is expected to largely collect and analyze malware samples for providing industries or universities to do related applications via the established IT2FMO.     

Algorithms for job scheduling problems with distinct time windows and general earliness/tardiness penalties

This paper addresses the single machine scheduling problem with distinct time windows and sequence-dependent setup times. The objective is to minimize the total weighted earliness and tardiness. The problem involves determining the job execution sequence and the starting time for each job in the sequence. An implicit enumeration algorithm denoted IE and a general variable neighborhood search algorithm denoted GVNS are proposed to determine the job scheduling. IE is an exact algorithm, whereas GVNS is a heuristic algorithm. In order to define the starting times, an O(n²) idle time insertion algorithm (ITIA) is proposed. IE and GVNS use the ITIA algorithm to determine the starting time for each job. However, the IE algorithm is only valid for instances with sequence-independent setup times, and takes advantage of theoretical results generated for this problem. Computational experiments show that the ITIA algorithm is more efficient than the only other equivalent algorithm found in the literature. The IE algorithm allows the optimal solutions of all instances with up to 15 jobs to be determined within a feasible computational time. For larger instances, GVNS produces better-quality solutions requiring less computational time compared with the other algorithm from the literature.     

Ontology-assisted automatic precise information extractor for visually impaired inhabitants

As the internet grows rapidly, millions of web pages are being added on a daily basis. The extraction of precise information is becoming more and more difficult as the volume of data on the internet increases. Several search engines and information fetching tools are available on the internet, all of which claim to provide the best crawling facilities. For the most part, these search engines are keyword based. This poses a problem for visually impaired people who want to get the full use from online resources available to other users. Visually impaired users require special aid to get?along with any given computer system. Interface and content management are no exception, and special tools are required to facilitate the extraction of relevant information from the internet for visually impaired users. The HOIEV (Heavyweight Ontology Based Information Extraction for Visually impaired User) architecture provides a mechanism for highly precise information extraction using heavyweight ontology and built-in vocal command system for visually impaired internet users. Our prototype intelligent system not only integrates and communicates among different tools, such as voice command parsers, domain ontology extractors and short message engines, but also introduces an autonomous mechanism of information extraction (IE) using heavyweight ontology. In this research we designed domain specific heavyweight ontology using OWL 2 (Web Ontology Language 2) and for axiom writing we used PAL (Protégé Axiom Language). We introduced a novel autonomous mechanism for IE by developing prototype software. A series of experiments were designed for the testing and analysis of the performance of heavyweight ontology in general, and our information extraction prototype specifically.     

Detection and resolution of semantic inconsistency and redundancy in an automatic ontology merging system

In recent years, researchers have been developing algorithms for the automatic mapping and merging of ontologies to meet the demands of interoperability between heterogeneous and distributed information systems. But, still state-of-the-art ontology mapping and merging systems is semi-automatic that reduces the burden of manual creation and maintenance of mappings, and need human intervention for their validation. The contribution presented in this paper makes human intervention one step more down by automatically identifying semantic inconsistencies in the early stages of ontology merging. We are detecting semantic heterogeneities that occur due to conflicts among the set of Generalized Concept Inclusions, Property Subsumption Criteria, and Constraint Satisfaction Mechanism in local heterogeneous ontologies, which become obstacles for the generation of semantically consistent global merged ontology. We present several algorithms to detect such semantic inconsistencies based on subsumption analysis of concepts and properties in local ontologies from the list of initial mappings. We provide ontological patterns for resolving these inconsistencies automatically. This results global merged ontology free from ??circulatory error in class/property hierarchy??, ??common class between disjoint classes/properties??, ??redundancy of subclass/subproperty of relations?? and other types of ??semantic inconsistency?? errors. Experiments on the real ontologies show that our algorithms save time and cost of traversing local ontologies, improve system??s performance by producing only consistent accurate mappings, and reduce the users?? dependability for ensuring the satisfiability of merged ontology.     

Evaluating the validity of data instances against ontology evolution over the Semantic Web

It is natural for ontologies to evolve over time. These changes could be at structural and semantic levels. Due to changes to an ontology, its data instances may become invalid, and as a result, may become non-interpretable. In this paper, we address precisely this problem, validity of data instances due to ontological evolution. Towards this end, we make the following three novel contributions to the area of Semantic Web. First, we propose formal notions of structural validity and semantic validity of data instances, and then present approaches to ensure them. Second, we propose semantic view as part of an ontology, and demonstrate that it is sufficient to validate a data instance against the semantic view rather than the entire ontology. We discuss how the semantic view can be generated through an implication analysis, i.e., how semantic changes to one component imply semantic changes to other components in the ontology. Third, we propose a validity identification approach that employs locally maintaining a hash value of the semantic view at the data instance.     

一种基于本体的军用文书理解系统设计

本文提出了一套基于本体和自然语言理解相结合的军用文书理解的解决方案.系统通过信息抽取和军标本体匹配两个模块,针对军用文书与军队标号相对应的特点,通过计算机自动处理,将军用文书转化成一种无二义性的中间格式,传递给其他系统使用,以提高指挥作战的效能.     

Approximate calculation of the characteristics of multirate systems with inelastic and elastic calls

Models of multirate systems in which inelastic and elastic calls (ie-calls and e-calls, respectively) are jointly serviced are investigated. It is assumed that through the entire service period the iecalls simultaneously occupy b ≥ 1 channels of the system, moreover, all the channels begin and complete servicing of the same ie-call simultaneously. The elastic calls simultaneously occupy m channels, m ≤ m ≤ \(\bar m\), where m and \(\bar m\) are specified quantities, with the service rate being proportional to the number of channels busy servicing e-calls. Models with continuous and discrete band for servicing e-calls are investigated in detail. Effective numerical algorithms for calculating the characteristics of the models are developed and results of computational experiments are presented.     

A legal case OWL ontology with an instantiation of Popov v. Hayashi

The paper provides an OWL ontology for legal cases with an instantiation of the legal case Popov v. Hayashi. The ontology makes explicit the conceptual knowledge of the legal case domain, supports reasoning about the domain, and can be used to annotate the text of cases, which in turn can be used to populate the ontology. A populated ontology is a case base which can be used for information retrieval, information extraction, and case based reasoning. The ontology contains not only elements for indexing the case (e.g. the parties, jurisdiction, and date), but as well elements used to reason to a decision such as argument schemes and the components input to the schemes. We use the Protégé ontology editor and knowledge acquisition system, current guidelines for ontology development, and tools for visual and linguistic presentation of the ontology.     

Domain ontology graph model and its application in Chinese text classification

This paper proposes an ontology learning method which is used to generate a graphical ontology structure called ontology graph. The ontology graph defines the ontology and knowledge conceptualization model, and the ontology learning process defines the method of semiautomatic learning and generates ontology graphs from Chinese texts of different domains, the so-called domain ontology graph (DOG). Meanwhile, we also define two other ontological operations—document ontology graph generation and ontology graph-based text classification, which can be carried out with the generated DOG. This research focuses on Chinese text data, and furthermore, we conduct two experiments: the DOG generation and ontology graph-based text classification, with Chinese texts as the experimental data. The first experiment generates ten DOGs as the ontology graph instances to represent ten different domains of knowledge. The generated DOGs are then further used for the second experiment to provide performance evaluation. The ontology graph-based approach is able to achieve high text classification accuracy (with 92.3 % in f-measure) over other text classification approaches (such as 86.8 % in f-measure for tf–idf approach). The better performance in the comparative experiments reveals that the proposed ontology graph knowledge model, the ontology learning and generation process, and the ontological operations are feasible and effective.     

Set oriented mappings on neural networks

Multi-layer perceptrons (MLP) or feed-forward neural networks (FFNN) are generally used to represent many-to-one (m-o) mappings from \(\Re^{n}\) to co-domain \(\Re^{m}\). Input units distribute real values to hidden layer units and individual output units produce values in \(\Re\). Thus MLP's represent or simulate the mappings of functions where the range consists of vectors and ordered lists. However it is also useful to represent mappings where the range consists of elements that are sets or collections (bags) of vectors of real values. The question answered in this paper is “Can an MLP be trained and used to represent a mapping from vectors of real values to collections of vectors of real values?”. Representing mappings from vectors to sets of real numbers or vectors of real numbers has a useful application that is of interest since a one-to-many (o-m) mapping from \(\Re^{n}\) to co-domain \(\Re^{m}\) is equivalent to a m-o mapping from \(\Re^n\) to co-domain P(\(\Re^{m}\)) where P(\(\Re^{m}\)) is the power set of \(\Re\) ^m . The paper describes a gradient descent training algorithm that successfully stores a mapping from vectors to sets and thereby a one-many mapping, on a feed-forward network requiring a relatively small number of training epochs. The method is tried on two one-to-many relationships. One is obtained from the inverse of a function and the other is a relationship that maps ages of parents to ages of their children. The method is readily extended to representing mappings to fuzzy sets.     

A parametric similarity method: Comparative experiments based on semantically annotated large datasets

We present the parametric method SemSim^p aimed at measuring semantic similarity of digital resources. SemSim^p is based on the notion of information content, and it leverages a reference ontology and taxonomic reasoning, encompassing different approaches for weighting the concepts of the ontology. In particular, weights can be computed by considering either the available digital resources or the structure of the reference ontology of a given domain. SemSim^p is assessed against six representative semantic similarity methods for comparing sets of concepts proposed in the literature, by carrying out an experimentation that includes both a statistical analysis and an expert judgment evaluation. To the purpose of achieving a reliable assessment, we used a real-world large dataset based on the Digital Library of the Association for Computing Machinery (ACM), and a reference ontology derived from the ACM Computing Classification System (ACM-CCS). For each method, we considered two indicators. The first concerns the degree of confidence to identify the similarity among the papers belonging to some special issues selected from the ACM Transactions on Information Systems journal, the second the Pearson correlation with human judgment. The results reveal that one of the configurations of SemSim^p outperforms the other assessed methods. An additional experiment performed in the domain of physics shows that, in general, SemSim^p provides better results than the other similarity methods.     

Merging of axiomatic definitions of concepts in the complex OWL ontologies

In the last decade, ontology matching and mapping research has shown a measurable progress. This topic draws substantial attention within the research community, though it is not fully researched so far and new complex and effective solutions are needed. Current works are limited in finding alignments or mappings between concepts of heterogeneous ontologies. But, once ontology mappings are found, then how they (or their class expressions) are to be integrated automatically is left open for the ontology merging research. This paper elaborates the mapping of class expressions of concepts and contributes an algorithm for their merging in an automatic ontology merging process without any human intervention. However, the challenge of mapping axiomatic definitions is the most difficult task for merging concept definitions of the source ontologies, but it reveals significant increase in precision and recall values. In addition, with the study of these algorithms, we conclude that ontology merging facilitates when one wants to get ontology with the better quality as the combined rich axioms are added in the merged ontology. We also discuss the results of our first successful participation in the Conference, OA4QA and Anatomy track of OAEI 2015.