So-called Ontology Research Articles

Augmented intelligence facilitates concept mapping across different electronic health records

IntroductionWith the advent of artificial intelligence, the secondary use of routinely collected medical data from electronic healthcare records (EHR) has become increasingly popular. However, different EHR systems typically use different names for the same medical concepts. This obviously hampers scalable model development and subsequent clinical implementation for decision support. Therefore, converting original parameter names to a so-called ontology, a standardized set of predefined concepts, is necessary but time-consuming and labor-intensive. We therefore propose an augmented intelligence approach to facilitate ontology alignment by predicting correct concepts based on parameter names from raw electronic health record data exports. MethodsWe used the manually mapped parameter names from the multicenter “Dutch ICU data warehouse against COVID-19” sourced from three types of EHR systems to train machine learning models for concept mapping. Data from 29 intensive care units on 38,824 parameters mapped to 1,679 relevant and unique concepts and 38,069 parameters labeled as irrelevant were used for model development and validation. We used the Natural Language Toolkit (NLTK) to preprocess the parameter names based on WordNet cognitive synonyms transformed by term-frequency inverse document frequency (TF-IDF), yielding numeric features. We then trained linear classifiers using stochastic gradient descent for multi-class prediction. Finally, we fine-tuned these predictions using information on distributions of the data associated with each parameter name through similarity score and skewness comparisons. ResultsThe initial model, trained using data from one hospital organization for each of three EHR systems, scored an overall top 1 precision of 0.744, recall of 0.771, and F1-score of 0.737 on a total of 58,804 parameters. Leave-one-hospital-out analysis returned an average top 1 recall of 0.680 for relevant parameters, which increased to 0.905 for the top 5 predictions. When reducing the training dataset to only include relevant parameters, top 1 recall was 0.811 and top 5 recall was 0.914 for relevant parameters. Performance improvement based on similarity score or skewness comparisons affected at most 5.23% of numeric parameters. ConclusionAugmented intelligence is a promising method to improve concept mapping of parameter names from raw electronic health record data exports. We propose a robust method for mapping data across various domains, facilitating the integration of diverse data sources. However, recall is not perfect, and therefore manual validation of mapping remains essential.

Solving Ontology Metamatching Problem through Improved Multiobjective Particle Swarm Optimization Algorithm

In recent years, knowledge representation in the Artificial Intelligence (AI) domain is able to help people understand the semantics of data and improve the interoperability between diverse knowledge-based applications. Semantic Web (SW), as one of the methods of knowledge representation, is the new generation of World Wide Web (WWW), which integrates AI with web techniques and dedicates to implementing the automatic cooperations among different intelligent applications. Ontology, as an information exchange model that defines concepts and formally describes the relationships between two concepts, is the core technique of SW, implementing semantic information sharing and data interoperability in the Internet of Things (IoT) domain. However, the heterogeneity issue hampers the communications among different ontologies and stops the cooperations among ontology-based intelligent applications. To solve this problem, it is vital to establish semantic relationships between heterogeneous ontologies, which is the so-called ontology matching. Ontology metamatching problem is commonly a complex optimization problem with many local optima. To this end, the ontology metamatching problem is defined as a multiobjective optimization model in this work, and a multiobjective particle swarm optimization (MOPSO) with diversity enhancing (DE) (MOPSO-DE) strategy is proposed to better trade off the convergence and diversity of the population. The well-known benchmark of the Ontology Alignment Evaluation Initiative (OAEI) is used in the experiment to test MOPSO-DE’s performance. Experimental results prove that MOPSO-DE can obtain the high-quality alignment and reduce the MOPSO’s memory consumption.

Large-scale complex ontology matching through anchor-based semantic partitioning technique and confidence matrix based evolutionary algorithm

Ontology presents a field knowledge by defining the meaning of domain concepts and relations among them. To conduct the inter-operability between two heterogeneous ontologies, we need to determine a number of entity correspondences, the so-called Ontology Matching (OM). Traditional OM techniques dedicate to find the simple correspondences which cannot sufficiently express the diverse kinds of heterogeneity, and therefore, complex correspondences are required, which maps one or multiple entities from one ontology to those from the other. Due to the semantic complexity, automatically discovering large-scale simple and complex correspondences has become a challenge. To face this challenge, in this work, an Anchor-based Semantic Partitioning Technique (ASPT) is first presented to divide the large-scale ontologies into several semantic segments, whose computational complexity is lower than the existing ontology partitioning algorithms. Then, a Pattern-based Similarity Measure (PSM) is used to distinguish both simple and complex correspondences. Finally, a Confidence Matrix based Evolutionary Algorithm (CM-EA) is proposed to efficiently match the ontology segments. We compare our proposal with EA-based matching technique and state-of-the-art ontology matching systems on OAEI’s Complex track, and the experimental results show that our approach is able to efficiently match large-scale complex ontologies.

Matching Ontologies through Multi-Objective Evolutionary Algorithm with Relevance Matrix

The ultimate goal of semantic web (SW) is to implement mutual collaborations among ontology-based intelligent systems. To this end, it is necessary to integrate those domain-independent and cross-domain ontologies by finding the correspondences between their entities, which is the so-called ontology matching. To improve the quality of ontology alignment, in this work, the ontology matching problem is first defined as a sparse multi-objective optimization problem (SMOOP), and then, a multi-objective evolutionary algorithm with a relevance matrix (MOEA-RM) is proposed to address it. In particular, a relevance matrix (RM) is presented to adaptively measure the relevance of each individual’s genes to the objectives, which is applied in MOEA’s initialization, crossover and mutation to ensure the population’s sparsity and to speed up the the algorithm’s convergence. The experiment verifies the performance of MOEA-RM by comparing it with the state-of-the-art ontology matching techniques, and the experimental results show that MOEA-RM is able to effectively address the ontology matching problem with different heterogeneity characteristics.

Matching sensor ontologies with unsupervised neural network with competitive learning

Sensor ontologies formally model the core concepts in the sensor domain and their relationships, which facilitates the trusted communication and collaboration of Artificial Intelligence of Things (AIoT). However, due to the subjectivity of the ontology building process, sensor ontologies might be defined by different terms, leading to the problem of heterogeneity. In order to integrate the knowledge of two heterogeneous sensor ontologies, it is necessary to determine the correspondence between two heterogeneous concepts, which is the so-called ontology matching. Recently, more and more neural networks have been considered as an effective approach to address the ontology heterogeneity problem, but they require a large number of manually labelled training samples to train the network, which poses an open challenge. In order to improve the quality of the sensor ontology alignment, an unsupervised neural network model is proposed in this work. It first models the ontology matching problem as a binary classification problem, and then uses a competitive learning strategy to efficiently cluster the ontologies to be matched, which does not require the labelled training samples. The experiment utilizes the benchmark track provided by the Ontology Alignment Evaluation Initiative (OAEI) and multiple real sensor ontology alignment tasks to test our proposal’s performance. The experimental results show that the proposed approach is able to determine higher quality alignment results compared to other matching strategies under different domain knowledge such as bibliographic and real sensor ontologies.

Matching heterogeneous ontologies with adaptive evolutionary algorithm

An ontology provides a formal description on the domain concepts and their relationships. Due to the subjectivity of ontology engineers, one concept might be expressed in various ways, yielding the so-called ontology heterogeneity problem, and ontology matching is a ground method to address this problem. Ontology matching technique uses the similarity measure to determine the correspondences between two heterogeneous ontology entities. In order to improve the quality of ontology alignment, it is necessary to combine different kinds of similarity measures, and how to optimize the aggregating weights is called the ontology meta-matching problem. Tin this work, a heuristic evaluating metric on the ontology alignment is presented to evaluate the ontology alignment's quality, and a mathematical model on ontology meta-matching problem is constructed. Then, an Adaptive Evolutionary Algorithm (AEA) is proposed to effectively solve this problem. In particular, when the elite solution remains unchanged, AEA adaptively activates three independent exploring strategies, which, respectively use the adaptive selection, crossover and mutation operators based on the population diversity metric. In the experiment, we compare AEA among EA based matching technique and the state-of-the-art ontology matching technique, and the experimental results show its effectiveness.

Death in Modern Theatre: Stages of Mortality by Adrian Curtin

Reviewed by: Death in Modern Theatre: Stages of Mortality by Adrian Curtin Julia A. Walker DEATH IN MODERN THEATRE: STAGES OF MORTALITY. By Adrian Curtin. Theatre: Theory, Practice, Performance series. Manchester: Manchester University Press, 2019; pp. 268. As Adrian Curtin observes in the introduction to his new book Death in Modern Theatre: Stages of Mortality, the topic of "liveness" has long defined contemporary theories of theatre and performance. Discussions about the so-called ontology of performance typically concern the paired ideas of presence and disappearance, recording technologies and documentation, trace and memory—all of which posit in some way the living, breathing body of an actor often co-present with those in the audience. Focusing on the work of Herbert Blau, Marvin Carlson, Alice Rayner, and Linda and Michael Hutcheon, Curtin briefly surveys theories of theatrical embodiment as a way of framing his study of how death and dying have been represented on the modern stage, proposing that "[t]heatrical deathliness may be thought to shadow theatrical liveliness" (12). Shifting his focus from theory to practice, Curtin surveys the history of such representations in the modern theatre, analyzing various aesthetic strategies used in five distinct periods to assess changing cultural attitudes toward death in the Western hemisphere. His temporal scope of "modernity" and his geographical parameter of the "West" are best explained with reference to French social historian Philippe Ariès's seminal study Western Attitudes Toward Death (1976), which Curtin uses to anchor his discussion. Just as Ariès analyzed culturally specific rituals of death to assess how social attitudes toward this universal existential experience have changed over time, so does Curtin use a comparative method in his study of theatrical representations of death in the modern period. But where Ariès's expansive study typified "modern" attitudes in the "West" according to a single logic, Curtin explores variability within Ariès's dominant narrative of the period, using the history of the stage to chart changes in secular rituals for understanding death. The book's subtitle alludes to the "stages" of grief outlined by psychologist Elisabeth Kübler-Ross in her landmark study On Death and Dying (1970), providing Curtin with a model of progressive but overlapping temporalities by which the book's five chapters unfold. The first chapter begins at the end of the nineteenth century with the symbolists, who innovated a multisensory, proto-immersive experience for their audiences in order to invite them to imaginatively inhabit a metaphysical realm beyond the material world. To do that, they cultivated a charged atmosphere of heightened sensory stimulation, using color, light, sound, and even scent to suffuse the space of the audience and blur its distinction from the fictional world of the stage. Curtin offers a fascinating history of symbolist theatre practice that is most compelling when he brings affect theory to bear on his analysis of these largely neglected because misunderstood plays. As he suggests, the charged energy that creates the shared atmosphere of a performance is nebulous, paradoxically manifesting as something both perceptible and unknowable that, in its effects, erases the felt distinction between subject and object. Examining Rachilde's Madame La Mort, Charles van Lerberghe's The Night-Comers, Maurice Maeterlinck's The Intruder, Leonid Andreyev's Requiem, and W. B. Yeats's Purgatory, Curtin demonstrates a range of techniques these playwrights used to conjure the experience of death for their audiences, along with the different conclusions each presented about this state of existence and its ultimate knowability. Chapter 2 looks at fantastical allegories of death that appeared on stage after the Great War. These include Satan the Waster by Vernon Lee (Violet Paget), The Transfiguration by Ernst Toller, and The Last Days of Mankind by Karl Krauss, allowing Curtin to explore different kinds of modernist abstraction that set theatrical elements of music, movement, light, and sound in expressionistic counterpoint to capture the shock of death by war. Chapter 3 examines the phenomenon of "death denial" in plays appearing after World War II by way of existentialist concerns expressed in Dino Buzzati's A Clinical Case, the Open Theater's Terminal (text by Susan Yankowitz), and Eugene Ionesco's Exit the King and Amédée...

Semantic Integration of Sensor Knowledge on Artificial Internet of Things

Artificial Internet of Things (AIoT) integrates Artificial Intelligence (AI) with the Internet of Things (IoT) to create the sensor network that can communicate and process data. To implement the communications and co-operations among intelligent systems on AIoT, it is necessary to annotate sensor data with the semantic meanings to overcome heterogeneity problem among different sensors, which requires the utilization of sensor ontology. Sensor ontology formally models the knowledge on AIoT by defining the concepts, the properties describing a concept, and the relationships between two concepts. Due to human’s subjectivity, a concept in different sensor ontologies could be defined with different terminologies and contexts, yielding the ontology heterogeneity problem. Thus, before using these ontologies, it is necessary to integrate their knowledge by finding the correspondences between their concepts, i.e., the so-called ontology matching. In this work, a novel sensor ontology matching framework is proposed, which aggregates three kinds of Concept Similarity Measures (CSMs) and an alignment extraction approach to determine the sensor ontology alignment. To ensure the quality of the alignments, we further propose a compact Particle Swarm Optimization algorithm (cPSO) to optimize the aggregating weights for the CSMs and a threshold for filtering the alignment. The experiment utilizes the Ontology Alignment Evaluation Initiative (OAEI)’s conference track and two pairs of real sensor ontologies to test cPSO’s performance. The experimental results show that the quality of the alignments obtained by cPSO statistically outperforms other state-of-the-art sensor ontology matching techniques.

Matching biomedical ontologies through compact differential evolution algorithm

ABSTRACTAlthough biomedical ontologies have been widely used in the life science domain, the heterogeneous problem among biomedical ontologies hampers their inter-operability. Thus, the establishment of meaningful links between heterogeneous biomedical ontologies, so-called biomedical ontology matching, is critical to the success of biomedical ontology engineering. To determine the biomedical ontology alignment with high quality, in this work, a Hybrid Compact Differential Evolution (HCDE) algorithm-based biomedical ontology matching technique is proposed. In particular, we propose a similarity metric on biomedical concepts, construct an optimal model for the biomedical ontology matching problem, and introduce a binomial crossover into CDE's evolving the process to enhance its performance. The experiments are carried out on the Disease and Phenotype track and Biodiversity and Ecology track from the Ontology Alignment Evaluation Initiative (OAEI 2018). The experimental results show that HCDE can significantly improve the CDE in terms of the alignment's quality, and the alignments obtained by HCDE are also better than OAEI 2018's participants.

A generic ontological network for Agri-food experiment integration – Application to viticulture and winemaking

This paper presents an ontological approach of scientific experimental data integration across complementary sub-domains, i.e., agricultural production and food processing, with an application to viticulture and winemaking. The two main steps in this approach are (i) to integrate preexisting ontologies to create a so-called ontology network and (ii) to populate the ontology network with experimental data from various sources. The Agri-Food Experiment Ontology (AFEO), a new ontology network, was developed, based on two ontological resources, i.e., AEO (Ontology for Agricultural Experiments) and OFPE (Ontology for Food Processing Experiments). It contains 136 concepts which cover various viticulture practices, as well as winemaking products and operations. AFEO was used to guide the data integration of two different data sources, i.e., viticulture experimental data stored in a relational database, and winemaking experimental data stored in Microsoft Excel files. Two applications illustrate the approach. The first one is on wine traceability and the second one is related to the influence of irrigation practices and winemaking methods on GSH concentration in wine. These examples show that data integration guided by an ontology network can provide researchers with the information necessary to address extended research questions.

A segment-based approach for large-scale ontology matching

The most ground approach to solve the ontology heterogeneous problem is to determine the semantically identical entities between them, so-called ontology matching. However, the correct and complete...

A Multi-Level Knowledge Representation Technology Research in Oil Management System

Ontology combined with the development of computer technology in all fields initiated the research of construction and application for so-called domain ontology. This article starts with the issues of current computer technology in the oil exploration and development domain, provides a construction method of oil exploration and development domain ontology based on a multi-level business process,and divides the oil exploration and development domain into multistage business activity levels,and takes hydrocarbon prospect characterization as example and discusses a construction method for corresponding micro-ontology.

A Framework to Defense against Insider Attacks on Information Sources

As for development and growth of information systems and security organizations, protecting information against probable attacks is of great importance. External raids on these organizations, for the most part, are not practicable due to high defensive layers. Therefore to intrude on such organizations, insiders are employed. In this paper, by introducing c onsequence and necessity of recognition of insider attacks perils, we intend to propose a new framework for detecting and preventing from insider attacks on information systems. The suggested framework is defined according to ontology graphs, thus, a struc ture of so-called ontology is firstly explained. This composition represents data structure for saving and presenting information and is then practiced to detect user’s behavioral patterns within such framework. The framework consists of three phases of construction, comparison and analysis such that it first receives a set of user’s requests alongside with his legal access level and in case of encountering an attack it communicates an appropriate warning message to the organization administrative system.

Enhancing ontology alignment through a memetic aggregation of similarity measures

Modern infrastructures for information and communication technologies are aimed at providing enhanced services by integrating the knowledge spread on the web through an ontological representation of information. However, ontology usefulness in managing different knowledge sources is limited by the so-called semantic heterogeneity problem arising when several interacting software components use different ontologies for representing the same information. In order to bridge this gap and, consequently, enable a full interoperability across the software components, it is necessary to bring the corresponding ontologies into a mutual agreement by identifying a set of semantic relationships among their entities. This result is achieved by means of a so-called ontology alignment process that, for each pair of entities belonging to the ontologies under alignment, computes their semantic closeness through an optimized aggregation of different similarity measures. Unfortunately, this similarity aggregation is a hard optimization process, above all, when no information is known about ontology features. The aim of this paper is to define an ontology alignment process based on a memetic algorithm able to efficiently aggregate similarity measures without using a priori knowledge about ontologies under alignment. As shown by a statistical multiple comparison procedure, our approach yields high performance in terms of alignment quality with respect to top-performers of well-known Ontology Alignment Evaluation Initiative campaigns.

A Framework to Defense Against Insider Attacks on Information Sources

As for development and growth of information systems and security organizations, protecting information against probable attacks is of great importance. External raids on these organizations, for the most part, are not practicable due to high defensive layers. Therefore to intrude on such organizations, insiders are employed. In this paper, by introducing consequence and necessity of recognition of insider attacks perils, we intend to propose a new framework for detecting and preventing from insider attacks on information systems. The suggested framework is defined according to ontology graphs, thus, a structure of so-called ontology is firstly explained. This composition represents data structure for saving and presenting information and is then practiced to detect user’s behavioral patterns within such framework. The framework consists of three phases of construction, comparison and analysis such that it first receives a set of user’s requests alongside with his legal access level and in case of encountering an attack it communicates an appropriate warning message to the organization administrative system.

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.

1.2. «To be» o «esse»? La questione dell’essere nel tomismo analitico

An overview of current scholarship in ontology places one before two parallel lines that never meet: on the one hand there are essays in so-called “analytic ontology”, on the other hand, there are studies developed within so-called Aristotelian-Thomistic ontology. However, even though it is frequently ignored, there is an interesting philosophical current of thought within so-called “analytical Thomism”, which joins contemporary debates of “analytic ontology” from the perspective of an Aristotelian-Thomistic ontology. The main contributors to this current are P. Geach and B. Miller, who have proposed a new theory about existence, called “two sense theory”. It is a view intermediate between the well known Fregean and Meinongian views, arguing for the possibility of two senses of “exists”: as first- and as second-order predicate. The object of this paper is to present this current of thought, and to evaluate it from the point of view of the history of ontology and of “continental” Thomism.

Comparison of implemented ontologies

A number of groups developing knowledge-based systems have found (or at least posited) that the design and representation of a limitative set of concepts and relations, a so-called ontology, can contribute to sharing and reusing knowledge bases. However, very few descriptions of implemented ontologies have appeared in the literature. No comparison of competing proposals is available, let alone an empirical determination of the benefits of using an ontology. There is no accepted method for designing and building such ontologies, nor is it clear how ontologies can best be evaluated.