Complex Ontology Research Articles

African Environmental Ethics and Its Ontological Foundations

Abstract The article carves out a tripartite ontology and related cosmological views, prevalent in many African philosophical accounts, and shows their significance for environmental ethics. It presents distinct cultural practices towards non-human animals and the environment such as totemism, taboos, and the sacralization of natural sites. In a next step, the author identifies specific moral principles that can be derived from this complex ontology and its related cultural practices, such as sufficiency, care, and sharing. This approach in environmental ethics can be situated as constituting a ‘plural holism’. Finally, the article contrasts this plural holism with neoliberal and neo-colonial practices prevalent in the contemporary world and calls for a consideration of these ethical values to guide the necessary transformation of our societies.

Toward a dynamic frame-based ontology of legal terminology

In the study of special languages and translation, the legal field is often insulated from other domains. This is primarily due to the extreme system dependence of the terminology of law, which results from a lack of a common legal system of reference throughout the world. The abstract nature of this human-made field and its dynamicity in view of the continuously evolving case law and constant changes in legislation make it difficult to illustrate its complex ontology through traditional terminology management techniques. Therefore, this paper argues for an interdisciplinary approach to constructing the ontology of legal concepts based on structural constituents from frame semantics and comparative law. Frames allowing for the representation of interconnected knowledge segments evoked by legal concepts and the distinction between micro- and macro-dimensions in legal comparison research make it possible to capture the complex ontology of legal terminology evoked in a specific point in time and a determined legal context. The ontological knowledge structure will be exemplified by terms from German social, commercial, employment, and tax law.

A Knowledge Graph Perspective on Knowledge Engineering

For over 50 years researchers and practitioners have searched for ways to elicit and formalize expert knowledge to support AI applications. Expert systems and knowledge bases were all results of these efforts. The initial efforts on knowledge bases were focused on defining a domain and task intensionally with rather complex ontologies. The increasing complexity of knowledge and knowledge-based systems eventually led to the development of knowledge engineering methodologies. Knowledge graphs, in contrast to the traditional knowledge bases, represent knowledge more extensionally with a very large set of explicit statements and rather simpler and smaller ontologies. This paradigm change calls for a new take on knowledge engineering that focuses on the curation of ABox statements. In this paper, we introduce various aspects of the knowledge graphs lifecycle namely creation, hosting, curation and deployment. We define each task, give example approaches from the literature and explain our approach with a running example. Additionally, we present the German Tourism Knowledge Graph that is being implemented with our methodology.

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.

Complex ontology alignment for autonomous systems via the Compact Co-Evolutionary Brain Storm Optimization algorithm

Autonomous Systems (ASs) that work in the open, dynamic environment are required to share their data entities and semantics to implement the co-operations. Typically, AS’s data schemas and semantics are described via ontology. Since ASs need to maintain their autonomy and conceptual specificity, their ontologies might define one concept with different terms or in different contexts, which yields the heterogeneity issue and hampers their co-operations. An effective solution is to establish a set of data entity’s correspondences through the Ontology Alignment (OA). Sine the simple correspondence of one-to-one style lacks expressiveness and cannot completely cover different types of heterogeneity, ASs’ co-operations require using the complex correspondence of one-to-many or many-to-may style. Inspired by the success of applying the Brain Storm Optimization algorithm (BSO) to solve diverse complex optimization problems, this work proposes a Compact Co-Evolutionary BSO (CCBSO) to face the challenge of aligning AS ontologies. In particular, the AS ontology aligning problem is formally defined, a hybrid confidence measure for distinguishing the simple and complex correspondences is proposed, and a problem-specific CCBSO is presented. The experiment tests CCBSO’s performance on different AS ontology aligning tasks, which consist of two simple ontology aligning tasks and one complex ontology aligning track. The experimental results show that CCBSO outperforms the state-of-the-art ontology aligning techniques on all simple and complex ontology aligning tasks.

Instance-Based Zero-Shot learning for semi-Automatic MeSH indexing

• Propose an instance-based Zero-shot learning method for facilitating the semi-automatic indexing of biomedical articles. • Novel labels of complex ontologies are ranked through exploiting similarity functions on an arbitrary semantic space. • We design a hybrid criterion based on underlying label dependencies, semantic similarities and pattern matching rules. • Robust ranking performance of the novel labels is obtained for ideal, artificially noisy and realistic oracles. • We obtain promising multi-label ranking performance regarding the novel labels on the last version of the PubMed dataset. Zero-shot learning constitutes a variant of the broader category of weakly supervised learning algorithms. Its main asset is the possibility of identifying entities for which no training data are provided in advance. Under this extreme scenario, conventional supervised learning methods cannot operate properly, while consumption of human resources for obtaining even limited instances may be highly restricted, especially when the label space is quite complex because of its cardinality and the underlying semantic dependencies. However, removing the human factor from the learning loop under complicated tasks cannot guarantee robust performance. Thus, semi-automated solutions are widely accepted by both the research and industrial communities, favoring cooperation of human and machine, mainly for alleviating the spent effort of the former, and for acquiring safer predictions. In contrast with the majority of existing Zero-shot learning approaches, we propose a generalized instance-based method oriented towards tackling the Multi-label classification task without performing any transductive operations over the test instances. Instead, we aim to provide a label ranking of the unseen classes exploiting sentence-based semantic embeddings and label similarities, through a dedicated fine-tuned language representational model. We also use a pattern matching rule to further boost the ranking of our method. Some realistic assumptions are made in order for our approach to work correctly and provide said ranking. Results on a biomedical database with a semantically rich fine-grained label space are really promising, rendering its utilization as a helpful and computationally inexpensive tool for facilitating semi-automated indexing.

Hobbes in the Anthropocene: Reconsidering the State of Nature in Its Relevance for Governing

The theoretical work of Thomas Hobbes marks the dawn of political modernity and thus also the beginning of modern reasoning about governing. In his Leviathan, Hobbes creates the modern space of the political through the exclusion of the world’s social and natural abundance. This crossroads of political thinking might not least be of relevance for the Anthropocene. After all, affirming the Anthropocene returns mankind to a cosmos of infinite human–nature interrelationships, which strongly resembles Hobbes’s conceptual depiction of the premodern state of nature and its incomprehensible, contingent, and precarious world, a world that Hobbes had intended to ban for good. In this context, this article reconsiders the state of nature’s internal dynamics in its relevance for governing in the Anthropocene—at the expense of the normative claims of modernist governing. After all, embracing the complex ontologies of the Anthropocene and the state of nature disperses agency among the human and nonhuman world, which questions the idea of ethical and political accountability. Without such a reference, governing runs the risk of becoming arbitrary and thereby another shallow projection of modernist conceptions. This article develops an interpretation of political subjectivity as a reference for governing, deriving from the materialistic world of the Hobbesian state of nature. On this foundation, the article elaborates on how this reading of subjectivity reconfigures the conception of political space and how this shift affects the scope of governing.

Rapid single cell evaluation of human disease and disorder targets using REVEAL: SingleCell\u2122

BackgroundSingle-cell (sc) sequencing performs unbiased profiling of individual cells and enables evaluation of less prevalent cellular populations, often missed using bulk sequencing. However, the scale and the complexity of the sc datasets poses a great challenge in its utility and this problem is further exacerbated when working with larger datasets typically generated by consortium efforts. As the scale of single cell datasets continues to increase exponentially, there is an unmet technological need to develop database platforms that can evaluate key biological hypotheses by querying extensive single-cell datasets.Large single-cell datasets like Human Cell Atlas and COVID-19 cell atlas (collection of annotated sc datasets from various human organs) are excellent resources for profiling target genes involved in human diseases and disorders ranging from oncology, auto-immunity, as well as infectious diseases like COVID-19 caused by SARS-CoV-2 virus. SARS-CoV-2 infections have led to a worldwide pandemic with massive loss of lives, infections exceeding 7 million cases. The virus uses ACE2 and TMPRSS2 as key viral entry associated proteins expressed in human cells for infections. Evaluating the expression profile of key genes in large single-cell datasets can facilitate testing for diagnostics, therapeutics, and vaccine targets, as the world struggles to cope with the on-going spread of COVID-19 infections.Main bodyIn this manuscript we describe REVEAL: SingleCell, which enables storage, retrieval, and rapid query of single-cell datasets inclusive of millions of cells. The array native database described here enables selecting and analyzing cells across multiple studies. Cells can be selected using individual metadata tags, more complex hierarchical ontology filtering, and gene expression threshold ranges, including co-expression of multiple genes. The tags on selected cells can be further evaluated for testing biological hypotheses. One such example includes identifying the most prevalent cell type annotation tag on returned cells.We used REVEAL: SingleCell to evaluate the expression of key SARS-CoV-2 entry associated genes, and queried the current database (2.2 Million cells, 32 projects) to obtain the results in < 60 s. We highlighted cells expressing COVID-19 associated genes are expressed on multiple tissue types, thus in part explains the multi-organ involvement in infected patients observed worldwide during the on-going COVID-19 pandemic.ConclusionIn this paper, we introduce the REVEAL: SingleCell database that addresses immediate needs for SARS-CoV-2 research and has the potential to be used more broadly for many precision medicine applications. We used the REVEAL: SingleCell database as a reference to ask questions relevant to drug development and precision medicine regarding cell type and co-expression for genes that encode proteins necessary for SARS-CoV-2 to enter and reproduce in cells.

КОНВЕРТАЦІЯ ФОРМАТІВ ЗБЕРІГАННЯ ГРАФОВИХ БАЗ ДАНИХ І ЇЇ РОЛЬ В ОНТОЛОГІЧНОМУ ІНЖИНІРИНГУ НА ПРИКЛАДІ СИСТЕМИ СУПРОВОДЖЕННЯ РЕАБІЛІТАЦІЇ

КОНВЕРТАЦІЯ ФОРМАТІВ ЗБЕРІГАННЯ ГРАФОВИХ БАЗ ДАНИХ І ЇЇ РОЛЬ В ОНТОЛОГІЧНОМУ ІНЖИНІРИНГУ НА ПРИКЛАДІ СИСТЕМИ СУПРОВОДЖЕННЯ РЕАБІЛІТАЦІЇ

Contrasting Ontology Modeling with Correlation Rules for Delivery Applications

With the increasing importance of knowledge management, variant management and the ever-growing quantity of data, ontologies emerged as a form of knowledge representation, especially in the field of technical communication for modelling metadata and to create correlations between them. In the area of delivery applications, the deliverable information objects receive a certain intelligence by semantic metadata. It is expected, that ontologies offer a higher level of intelligence which could lead to an improvement in classification, connection and delivery possibilities of content. On the contrary, creating those complex ontologies requires a time-consuming effort. Thus, the question arises, whether their use offers a decisive added benefit or if alternatives, such as untyped correlations, should be preferred. In that case, the concept of Semantic Correlation Rules can offer an opportunity to derive advantages from ontologies: By defining which classifications are connected to others, it is possible to present content tailored to user-specific information requirements. By developing use cases, we aim to evaluate the required level of intelligence of the metadata resulting from its modeling method to achieve this goal.

Natural or artificial? A reflection on a complex ontology

State-of-the-art planning theory considers cities as cyborg entities composed by a “natural” part (human beings and their social structures) and an “artificial” part (buildings, infrastructure and other urban artifacts). We contend that this hybrid conception is indissolubly coupled with the ability to discriminate perfectly between the “natural” and the “artificial”. But is this actually the case? We will provide a critical reflection on this ontological issue pointing out that current urban planning theory as well as the general philosophical reflection is not able to produce a rigorous, consistent epistemic criterion to draw this distinction. Long-standing difficulties in this respect are exponentially amplified by recent developments in artificial intelligence, nanotechnology and biotechnology, and their growing relevance in urban environments of the near future risk making the cyborg conception informing the complexity theory of cities obsolete. We will conclude our reflection identifying a possible path for overcoming this dualism toward a more socio-natural conception internalizing the proteiform character of the concept of “nature” itself as well as its inherent cognitive/political element.

Organizing for thoughtful food: a meshwork approach.

This paper provides an alternative narrative for organizing food systems. It introduces meshwork as a novel theoretical lens to examine the ontological assumptions underlying the shadow and informal dynamics of organizing food. Through a longitudinal qualitative case study, we place relationality and becoming at the centre of organizing food and food systems, demonstrating how entangled relationships can create a complex ontology through the meshwork knots, threads and weave. We show how issues of collective concern come together to form dynamic knots of interactions, how the threads within the meshwork indicate processes of movement, and how the weave suggests degrees of food system resilience—but always in flow. This theoretical approach thus provides a platform for addressing thoughtful concerns about “food matters” including the integrity of our global food system, the negative health and environmental impacts of industrialized food production, and food safety issues.

GeoLink Data Set: A Complex Alignment Benchmark from Real-world Ontology

Ontology alignment has been studied for over a decade, and over that time many alignment systems and methods have been developed by researchers in order to find simple 1-to-1 equivalence matches between two ontologies. However, very few alignment systems focus on finding complex correspondences. One reason for this limitation may be that there are no widely accepted alignment benchmarks that contain such complex relationships. In this paper, we propose a real-world data set from the GeoLink project as a potential complex ontology alignment benchmark. The data set consists of two ontologies, the GeoLink Base Ontology (GBO) and the GeoLink Modular Ontology (GMO), as well as a manually created reference alignment that was developed in consultation with domain experts from different institutions. The alignment includes 1:1, 1:n, and m:n equivalence and subsumption correspondences, and is available in both Expressive and Declarative Ontology Alignment Language (EDOAL) and rule syntax. The benchmark has been expanded from its original version to contain real-world instance data from seven geoscience data providers that has been published according to both ontologies. This allows it to be used by extensional alignment systems or those that require training data. This benchmark has been incorporated into the Ontology Alignment Evaluation Initiative (OAEI) complex track to help researchers test their automated alignment systems and algorithms. This paper also analyzes the challenges inherent in effectively generating, detecting, and evaluating complex ontology alignments and provides a road map for future work on this topic.

A consensual dataset for complex ontology matching evaluation

Abstract Simple ontology alignments, largely studied in the literature, link one single entity of a source ontology to one single entity of a target ontology. One of the limitations of these alignments is, however, their lack of expressiveness, which can be overcome by complex alignments, which are composed of correspondences involving logical constructors or transformation functions. While most work on complex ontology matching has been dedicated to the development of complex matching approaches, there is still a lack of benchmarks on which the complex approaches can be systematically evaluated. The aim of this paper is to present the process of constructing the consensual complex Conference dataset, describing the design choices and the methodology followed for constructing it. We discuss the issues the experts were faced with during the process and discuss the lessons learned and perspectives in the field.

Towards evaluating complex ontology alignments

Abstract The development of semi-automated and automated ontology alignment techniques is an important part of realizing the potential of the Semantic Web. Until very recently, most existing work in this area was focused on finding simple (1:1) equivalence correspondences between two ontologies. However, many real-world ontology pairs involve correspondences that contain multiple entities from each ontology. These ‘complex’ alignments pose a challenge for existing evaluation approaches, which hinders the development of new systems capable of finding such correspondences. This position paper surveys and analyzes the requirements for effective evaluation of complex ontology alignments and assesses the degree to which these requirements are met by existing approaches. It also provides a roadmap for future work on this topic taking into consideration emerging community initiatives and major challenges that need to be addressed.

Exploring duality on ontology debugging

Ontology debugging is an important and intractable reasoning task. Minimal unsatisfiability-preserving subset (MUPS) which is a minimal subset of an ontology to debug an unsatisfiable concept, is an important concept in ontology debugging. Hitting set relation between conflict set and diagnosis is essential for computing all conflict sets. And there has been many successful explorations about hitting set duality between conflict set and diagnosis in many fields. So in this paper, we will explore the duality between MUPS and minimal correctness-preserving subset (MCPS) which denotes the minimal diagnosis of a concept to debug unsatisfiable concepts on ontology debugging domain. Then several methods for computing all MUPSes will be devised based on the duality between MUPS and MCPS, meanwhile parallel strategies are also applied to newly proposed methods. And we show, by an empirical evaluation, that performances of different methods on real world ontologies from different domains. And it is proved that it is meaningful to explore duality on ontology debugging, as it really boosts the efficiency when applied to complex ontologies compared to the previous methods.

Cultural Structures of Knowledge from Wikipedia Networks of First Links

Knowledge is useless without structure. While the classification of knowledge has been an enduring philosophical enterprise, it recently found applications in computer science, notably for artificial intelligence. The availability of large databases allowed for complex ontologies to be built automatically, for example by extracting structured content from Wikipedia. However, this approach is subject to manual categorization decisions made by online editors. Here we show that an implicit classification hierarchy emerges spontaneously on Wikipedia. We study the network of first links between articles, and find that it centers on a core cycle involving concepts of fundamental classifying importance. We argue that this structure is rooted in cultural history. For European languages, articles like Philosophy and Science are central, whereas Human and Earth dominate for East Asian languages. This reflects the differences between ancient Greek thought and Chinese tradition. Our results reveal the powerful influence of culture on the intrinsic architecture of complex data sets.

Web video classification with visual and contextual semantics

SummaryOn the social Web, the amount of video content either originated from wireless devices or previously received from media servers has increased enormously in the recent years. The astounding growth of Web videos has stimulated researchers to propose new strategies to organize them into their respective categories. Because of complex ontology and large variation in content and quality of Web videos, it is difficult to get sufficient, precisely labeled training data, which causes hindrance in automatic video classification. In this paper, we propose a novel content‐ and context‐based Web video classification framework by rendering external support through category discriminative terms (CDTs) and semantic relatedness measure (SRM). Mainly, a three‐step framework is proposed. Firstly, content‐based video classification is proposed, where twofold use of high‐level concept detectors is leveraged to classify Web videos. Initially, category classifiers induced from VIREO‐374 detectors are trained to classify Web videos, and then concept detectors with high confidence for each video are mapped to CDT through SRM‐assisted semantic content fusion function to further boost the category classifiers, which intuitively provide a more robust measure for Web video classification. Secondly, a context‐based video classification is proposed, where twofold use of contextual information is also harnessed. Initially, cosine similarity and then semantic similarity are measured between text features of each video and CDT through vector space model (VSM)‐ and SRM‐assisted semantic context fusion function, respectively. Finally, classification results from content and context are fused to compensate for the shortcomings of each other, which enhance the video classification performance. Experiments on large‐scale video dataset validate the effectiveness of the proposed solution.

Legal ontologies over time: A systematic mapping study

Over the last 30 years, AI & Law has provided breakthroughs in studies involving case-based reasoning, rule-based reasoning, information retrieval and, most recently, conceptual models for knowledge representation and reasoning, known as Legal Ontologies. Ontologies have been widely used by legal practitioners, scholars, and lay people in a variety of situations, such as simulating legal actions, semantic search and indexing, and to keep up-to-date with the continual change of laws and regulations. Given the high number of legal ontologies produced, the need to summarize this research realm through a well-defined methodological procedure is urgent need. This study presents the results of a systematic mapping of the literature, aiming at categorizing legal ontologies along certain dimensions, such as purpose, level of generality, underlying legal theories, among other aspects. The reasons to carry out a systematic mapping are twofold: in addition to explaining the maturation of the area over recent decades, it helps to avoid the old problem of reinventing the wheel. Through organizing and classifying what has already been produced, it is possible to realize that the development of legal ontologies can rise to the level of reusability where prefabricated models might be coupled with new and more complex ontologies for practical law.

Consciousness, Creativity, and Complex Time in Music

CONSCIOUSNESS, CREATIVITY, AND COMPLEX TIME IN MUSIC GUERINO MAZZOLA 1. TIME IN PHYSICS NE OF THE MOST DRAMATIC ONTOLOGICAL CHANGES in modern physics of the twentieth century was the revolutionary reconceptualization of time. It started with Albert Einstein’s special relativity, where he embedded time in a four-dimensional space-time. Einstein had adopted the space-time approach of Hermann Minkowski, who, in a famous statement stated that “[h]enceforth, space for itself, and time for itself shall completely reduce to a mere shadow, and only some sort of union of the two shall preserve independence.”1 Time then became a multiple variable, the Newtonian singular “divine” time was replaced by a plurality, one in every frame of reference, and different frame times being related to each other by the Lorentz transformation of space-time. The second revolution of the time concept was introduced by Stephen Hawking (among others) in order to solve singularity problems of the Big Bang model of the evolution of our universe in the initial moment some 13.8 billion years ago. Hawking’s concept of time steps from the real time axis to the plane of complex numbers: Time now has two coordinates t = tR + itI, the real time tR and the O 432 Perspectives of New Music imaginary time tI. This complex ontology has also been proposed and studied by physicists Itzak Bar and John Terning (2010). These two revolutions in the concept of time, however, did not apply to the human cognitive reality, except in Einstein’s case for the metaphorical and vague popular statement that “everything is relative.”2 In what follows, I will introduce the thesis that complex time could be a key to some of the most virulent problems in the artistic reality of music, namely the nature of artistic consciousness and creativity, especially from the perspective of musical performance. This thesis is also in the spirit of John Rahn’s unique proactive spirit in music theory. 2. CONSCIOUSNESS AND CREATIVITY IN MUSIC The performing musician, whether rendering a composed work from a score, or creating in a more-or-less free or improvisatory fashion—in jazz for example—faces a complex combination of memory, technique, gestures, and the balance in the famous temporal καιρός (Kairos) between past and future moments. Successful musical performance is a highly creative and complex activity, at the very center of which is the sophisticated consciousness of the artist which manages the harmonious collaboration of the above-mentioned components in real time. “In real time” means in every moment of the performance; more precisely, in every infinitesimal point of physical time. I know as a performing free jazz pianist what every good performer experiences: the complex processual unfolding of musical performance is a rich machinery that defines and is happening in a big space of consciousness and presence. This configuration is described in Mazzola (2011, ch. 4.12). This artistic complexification is however a miraculous phenomenon since it happens in “no time,” in the physical moment of presence. We are confronted with a big “space” of consciousness that occupies no time in the physical time line. This makes evident the problematic status of creative artistic consciousness, and of consciousness in general: How can it be that a rich processuality is construed in no physical time? It seems that this type of phenomenon is enabled by the existence of a huge “space” of consciousness that is attached to every moment of physical time. This situation in its acute extremism in performing arts questions a classical rationale for understanding and even defining consciousness in cognitive and neuroscience. If no extra “space” is added to the classical physical ontology, consciousness cannot cope with its performative quality. So let us present the following thesis: Consciousness, Creativity, and Complex Time in Music 433 Thesis: Any workable concept of consciousness, and in particular artistic consciousness in the performing arts, must be built upon a “spacetime ” that is added to the classical physical spatio-temporal ontology. 3. CARTESIAN DUALISM The above thesis is given a familiar philosophical perspective if we review the Cartesian dualism which Descartes set up in his Principia philosophiae (Descartes 1983), where he describes the three substances of being: res...