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Validation experiments are conducted at discrete settings within the domain of interest to assess the predictive maturity of a model over the entire domain. Satisfactory model performance merely at these discrete tested settings is insufficient to ensure that the model will perform well throughout the domain, particularly at settings far from validation experiments. The goal of coverage metrics is to reveal how well a set of validation experiments represents the entire operational domain. The authors identify the criteria of an exemplary coverage metric, evaluate the ability of existing coverage metrics to fulfill each criterion, and propose a new, improved coverage metric. The proposed metric favors interpolation over extrapolation through a penalty function, causing the metric to prefer a design of validation experiments near the boundaries of the domain, while simultaneously exploring inside the domain. Furthermore, the proposed metric allows the coverage to account for uncertainty associated with validation experiments. Application of the proposed coverage metric on a practical, non-trivial problem is demonstrated on the Viscoplastic Self-Consistent material plasticity code for 5182 aluminum alloy.

Semantic Web was proposed to make the content machine-understandable by developing ontologies to capture domain knowledge and annotating content with this domain knowledge. Although, the original idea of semantic web was to make content on the World Wide Web machine-understandable, with recent advancements and awareness about these technologies, researchers have applied ontologies in many interesting domains. Many phases in software engineering are dependent on availability of knowledge, and the use of ontologies to capture and process this knowledge is a natural choice. This chapter discusses how ontologies can be used in various stages of the system development life cycle. Ontologies can be used to support requirements engineering phase in identifying and fixing inconsistent, incomplete, and ambiguous requirement. They can also be used to model the requirements and assist in requirements management and validation. During software design and development stages, ontologies can help software engineers in finding suitable components, managing documentation of APIs, and coding support. Ontologies can help in system integration and evolution process by aligning various databases with the help of ontologies capturing knowledge about database schema and aligning them with concepts in ontology. Ontologies can also be used in software maintenance by developing a bug tracking system based upon ontological knowledge of software artifacts and roles of developers involved in software maintenance task.

Semantic similarity is a metric used to know the similarity degree of two concepts in an ontology or a taxonomy. Semantic similarity has a wide variety applications on artificial intelligence, natural language processing, biomedical informatics, geoinformatics and semantic web and is usually applied on machine translation and word-sense disambiguation. In this research, semantic similarity measures are used to evaluate taxonomic relationships in a domain ontology. This evaluation was carried out by using a proposed algorithm and through the accuracy measure. The semantic similarity measures implemented are based on information content and were proposed by the following authors: Resnik, Lin, Jiang & Conrath and Mazandu & Mulder. Mainly, this research contributes to the automatic evaluation of ontologies in the task of evaluating the ontology taxonomy. The experimental results show that the measures have at least 88% accuracy. In addition, the system has an accuracy of 94% compared to validation responses from an expert.

The important role given to domain ontologies for knowledge representation implies increasing need for development and maintenance of them. However, we have a scarcity of tools supporting the life cycle from creation to management and adaptation. Our research group focuses on developing an autonomic process for knowledge representation through a systematic evaluation and redesign of ontologies. This autonomic behavior intends to specify a self-management repository for domain ontologies with an evolution strategy to make adjustments to optimize and protect against undesirable scenarios. In this article, we present the repository with embedded autonomic features to perform the abstraction action of knowledge upon documents collection, using the background knowledge already specified in the domain ontologies.

The development of very large ontologies may involve several domain experts, ontology engineers and final users, all with very different objectives. New scenarios like biomedicine, genomics and biology require methodologies that consider new dimensions in the development process, namely: dynamism, distribution and control. Existing works lack some important aspects with respect to these dimensions. In this paper we propose a view-based methodology for the collaborative, distributed and modular development of large ontologies, which achieves the identified requirements for the proper evolution and use of very large ontologies

Ontology is a representation term used to describe and represent a domain of knowledge. Manually ontology development is currently considered complex, requiring a lot of time and effort. This research was proposed to develop methods to build automatic domain ontology bilingual in Indonesian and English by using corpus and ontology design patterns (ODPs) in tuberculosis disease. In this study, the methods used were to combine ontology learning from text and correspond with ontology design patterns to decrease the role of expert knowledge. The methods in this research consist of six stages: (i) Term and relation extraction (ii) Corresponding with Tuberculosis glossary (iii) Corresponding the ontology design patterns (iv) Score computation similarity term and relations with ODPs (v) Ontology Building (vi) Ontology evaluation. The results of ontology construction were 361 terms and 44 relations with 260 terms were added. The calculation accuracy of ontology construction was 71%. Ontology construction had higher complexity and shorter time as well as decreases the role of the expert knowledge which proof that the automatic ontology evaluation is better than manual ontology construction.

In domain ontology, concept relation is the key component. Traditional concept relation mining methods usually cannot be used to construct large scale domain ontology because of their dependence on some high cost resources like tagged corpus. In this paper, we propose a simple but effective method to mine some complex concept relations for domain ontologies. Compared with traditional methods, our method has a large scope of applications. Besides, our method can be easily transplanted and implemented. And experimental results show that our method can mine some complex relations among domain concepts with encouraging performance.

Development and evaluation of an ontology for guiding appropriate antibiotic prescribing

Formal Ontology is a discipline whose business is to develop formal theories about general aspects of reality such as identity, dependence, parthood, truthmaking, causality, etc. A foundational ontology is a specific consistent set of these ontological theories that support activities such as domain analysis, conceptual clarification, and meaning negotiation. A (well-founded) core ontology specifies, under a foundational ontology, the central concepts and relations of a given domain. Foundational and core ontologies can be seen as ontology engineering frameworks to systematically address the laborious task of building large (more specific) domain ontologies. However, both in research and industry, it is common that ontologies as computational artifacts are built without the aid of any framework of this kind, often yielding modeling mistakes and representation gaps. In this paper, we analyze a case in the domain of cybersecurity, namely, the case of D3FEND - an OWL knowledge graph of cybersecurity countermeasure techniques proposed by the MITRE Corporation. Based on the Reference Ontology for Security Engineering (ROSE), a core ontology of the security domain founded in the Unified Foundational Ontology (UFO), our investigation reveals a number of semantic issues and opportunities for improvement in D3FEND, including missing concepts, semantic overload of terms, and lacking constraints that cause an under-specification of the model. As a result of our ontological analysis, we propose several suggestions for the appropriate redesign of D3FEND to overcome those issues.

An pipeline feature clustering algorithm based on binarization image pipeline linear feature extraction in terms of traditional Hough transform is proposed for autonomous underwater vehicle (AUV) pipeline recognition, and a K-means clustering algorithm is involved to classify the line according to the pipeline location constraint. Thus, a frame image of recognition area in the final position of the pipeline central axis can be obtained accordingly. The position estimation method based on pipeline identification is presented to locate the pipeline, and then estimate the longitude and latitude of the pipeline with saving data according to the frame. We performed a pool experiment to verify the proposed algorithm.

In this article, we propose a metric for the automatic evaluation of restricted domain ontologies. The metric is defined in terms of the evaluation of different lexico-syntactic, statistical and semantic approaches. A syntactic approach employed is the use of lexical syntactic patterns, other approaches as grouping by formal concept analysis, similarity, latent semantic analysis and dependence graphs are used as well. These approaches focus on reference corpora to find evidence of the validity of concepts and semantic relationships stored in the target ontology. The proposed evaluation approach is able to provide a score obtained through the metric, which is based on the accuracy measure used for each ontology evaluated. The score is associated in some way with the ontology quality. This score is given with a certain degree of reliability, and it is obtained by comparing the results given against the evaluation of human experts and a baseline.

Rapid population aging and advances in sensing technologies motivate the development of unobtrusive healthcare systems, designed to unobtrusively collect an elderly's personalized information of daily living and help him actively enjoy a healthy lifestyle. Existing studies towards this goal typically focus on recognition of activities of daily living (ADLs) and abnormal behavior detection. However, the applicability of these approaches is often limited by an offline analysis strategy, complex parameter tuning, obtrusive data collection, and a need for training data. To overcome these shortcomings, this paper presents a novel framework, named the online daily habit modeling and anomaly detection (ODHMAD) model, for the real-time personalized ADL recognition, habit modeling, and anomaly detection for the solitary elderly. In contrast to most existing studies which consider activity recognition and abnormal behavior detection separately, ODHMAD links both in a system. Specifically, ODHMAD performs online recognition of the elderly's daily activities and dynamically models the elderly's daily habit. In this way, ODHMAD recognizes the personalized abnormal behavior of an elderly by detecting anomalies in his learnt daily habit. The developed online activity recognition (OAR) algorithm determines the occurrence of activities by modeling the activation status of sensors. It has advantages of online learning, light parameter tuning, and no training data required. Moreover, OAR is able to obtain details of the detected activities. Experimental results demonstrate the effectiveness of the proposed OAR model for online activity recognition in terms of precision, false alarm rate, and miss detection rate.

The present paper proposes a new ground penetrating radar (GPR) system for obtaining high resolution images for objects buried under the surface of a ground soil. Four sets of clustered two-dimensional data representing the complex scattering parameters (coupling coefficients) between transmitting and receiving antennas are obtained. The paper introduces a new algorithm using a sliding window with a thresholding mechanism to extract a two-dimensional drawing to identify the shape of the buried object from the polarimetric GPR data. The obtained drawing for the buried object shape is compared to a reference target shape and a least-squared-error method is applied to take a match or mismatch decision. The proposed GPR antenna system and the suggested algorithms for shape extraction and target recognition are examined by their application to the scattering parameters data of the four polarization channels obtained through electromagnetic computer simulation using the Finite-Difference-Time-Domain (FDTD) method. A variety of targets with different shapes are modeled and immersed into a ground soil. The numerically obtained results show that proposed GPR system with the suggested algorithms for shape extraction and target shape recognition are successful in all the examined cases.

Digital agility is a critical dynamic capability that is becoming increasingly important in the context of collaborative product development processes (PDPs). This paper aims to address the complexity of today's PDPs by considering various quality aspects including safety, environment, and the entire lifecycle, along with diverse dynamic capabilities such as digital agility and circular economy. The authors employed a semantic web methodology and created an ontology-based knowledge model. The proposed ontology uses Design for X techniques, circular economy, digital agility, and the semantic web under the PDP perspective to increase performance and cooperation between designers and the project team. To validate the ontology, measures for domain ontology evaluation have been used. The paper presents a detailed guide for ontology engineering and evaluation for collaborative smart PDP, which incorporates digital agility as a critical dynamic capability. The proposed ontology can help boost PDP performance and increase customer satisfaction.

Nowadays, it is widely accepted that the data warehouse design task should be largely automated. Furthermore, the data warehouse conceptual schema must be structured according to the multidimensional model and as a consequence, the most common way to automatically look for subjects and dimensions of analysis is by discovering functional dependencies (as dimensions functionally depend on the fact) over the data sources. Most advanced methods for automating the design of the data warehouse carry out this process from relational OLTP systems, assuming that a RDBMS is the most common kind of data source we may find, and taking as starting point a relational schema. In contrast, in our approach we propose to rely instead on a conceptual representation of the domain of interest formalized through a domain ontology expressed in the DL-Lite Description Logic. We propose an algorithm to discover functional dependencies from the domain ontology that exploits the inference capabilities of DL-Lite, thus fully taking into account the semantics of the domain. We also provide an evaluation of our approach in a real-world scenario.