Formal Ontology Research Articles

MARTIN-LÖF’S TYPE THEORУ: BETWEEN PHENOMENOLOGY AND ANALYTICAL PHILOSOPHY

Martin-Löf’s type theory stems simultaneously from Frege and Russell’s logic-ontological ideas and Husserl’s phenomenology. The article examines this intermediate status of type theory using as examples Martin-Löf ’s syntactical-semantic method and the role of evidence and canonical objects in his approach. Martin-Löf borrows the syntactical-semantic method from Frege and extends it drawing on Husserl’s theory of meaning. In type theory this method leads to the identity (isomorphism) of syntax and semantics (formal logic and formal ontology). Unlike traditional formal logic the type theory is an interpreted system from the very beginning. Being intuitionistic, Martin-Löf ’s theory is based on the notion of proof, not truth. From the meaning theory point of view, it is a variant of proof-theoretic semantics (Gentzen, Prawitz, Dummett) which understands meaning as an object constructed according to certain rules. So understood, the proof is based on evidence, which allows us to associate it with the theory of intentionality by Husserl. The article compares Martin-Löf ’s type theory with Husserl’s intentionality theory, especially with the latter’s noematic component. We may consider type-theoretical rules for constructing objects and operating with them as a concretization and formalization of the phenomenological notion of noema. Both are explications of the more general concept of meaning. The article discusses the interrelation between notions of sense and meaning (Sinn, Bedeutung) in Frege, Husserl and Martin-Löf. This reveals the uncertainty of Martin-Löf ’s position in relation to meaning theories of Frege and Husserl.

Prediction of Drug-Target Interactions with High-Quality Negative Samples and A Network-Based Deep Learning Framework.

Identification of drug-target interactions (DTIs) plays a crucial role in drug discovery. Compared to traditional experimental methods, computer-based methods for predicting DTIs can significantly reduce the time and financial burdens of drug development. In recent years, numerous machine learning-based methods have been proposed for predicting potential DTIs. However, a common limitation among these methods is the absence of high-quality negative samples. Moreover, the effective extraction of multisource information of drugs and proteins for DTI prediction remains a significant challenge. In this paper, we investigated two aspects: the selection of high-quality negative samples and the construction of a high-performance DTI prediction framework. Specifically, we found two types of hidden biases when randomly selecting negative samples from unlabeled drug-protein pairs and proposed a negative sample selection approach based on complex network theory. Furthermore, we proposed a novel DTI prediction method named HNetPa-DTI, which integrates topological information from the drug-protein-disease heterogeneous network and gene ontology (GO) and pathway annotation information of proteins. Specifically, we extracted topological information of the drug-protein-disease heterogeneous network using heterogeneous graph neural networks, and obtained GO and pathway annotation information of proteins from the GO term semantic similarity networks, GO term-protein bipartite networks, and pathway-protein bipartite network using graph neural networks. Experimental results show that HNetPa-DTI outperforms the baseline methods on four types of prediction tasks, demonstrating the superiority of our method. Our code and datasets are available at https://github.com/study-czx/HNetPa-DTI.

Hierarchical graph transformer with contrastive learning for gene regulatory network inference.

Gene regulatory networks (GRNs) are crucial for understanding gene regulation and cellular processes. Inferring GRNs helps uncover regulatory pathways, shedding light on the regulation and development of cellular processes. With the rise of high-throughput sequencing and advancements in computational technology, computational models have emerged as cost-effective alternatives to traditional experimental studies. Moreover, the surge in ChIPseq data for TF-DNA binding has catalyzed the development of graph neural network (GNN)-based methods, greatly advancing GRN inference capabilities. However, most existing GNN-based methods suffer from the inability to capture long-distance structural semantic correlations due to transitive interactions. In this paper, we introduce a novel GNN-based model named Hierarchical Graph Transformer with Contrastive Learning for GRN (HGTCGRN) inference. HGTCGRN excels at capturing structural semantics using a hierarchical graph Transformer, which introduces a series of gene family nodes representing gene functions as virtual nodes to interact with nodes in the GRNS. These semanticaware virtual-node embeddings are aggregated to produce node representations with varying emphasis. Additionally, we leverage gene ontology information to construct gene interaction networks for contrastive learning optimization of GRNs. Experimental results demonstrate that HGTCGRN achieves superior performance in GRN inference.

ИНФОРМАЦИОННОЕ СОСТОЯНИЕ ДЕНЕЖНОЙ СИСТЕМЫ

The article examines the information state of the monetary system. It is shown that one of the fundamental difficulties in the development of the modern economy is high-quality and timely financial information. The idea of obtaining open government data is formulated. Regulatory legal acts are provided for the publication of open data in the Russian Federation. Certain areas of fintech have been identified, which already play an important role in the information state of the global monetary system. Embedded finance or seamless services (Embedded Finance), salary on call (Payon Demand), autonomous Finance, and Open Banking have been studied. The main tasks of obtaining open government data are given. It is shown that the syntactic and semantic quality of data should be taken into account when managing the monetary system. It is proposed to use open financial information in the ontology format to build a single semantic financial information portal.

Information: Modern Concepts

The paper reappraises our understanding of information without favoring any specific perspective. The paper presents the various conceptualizations of information, including biological information, natural information, pragmatic information, physical information, quantum information, quantified information, relative information, semantic information, semiotic information, epistemic information, ontological information, and syntactic information, together with some of their variants. In the search for a unifying perspective on information, the paper looks at two general theories of information: the General Definition of Information (GDI) and the General Theory of Information (GTI), arguing that the GTI appears to be the better of these two options. The paper is intended to be as complete and comprehensive as possible, sacrificing the analytical part (that may be found in referred sources) for the breadth of coverage.

Modification of the ontology using the teacher’s personal materials to increase the degree of automation of EC preparation

The object of the research is the technology of constructing a computer ontology (CO) for electronic textbooks of the subject area (KSA). As an example, the industry is considered-information and communication technologies (ICT) and the topic "databases". The subject of the study are models and methods of automated construction of a CO of the KSA. The methodology of modification of CO in the field of ICT is proposed by introducing into the formal computer ontology a description of the technologies of the learning process for secondary schools or extracurricular institutions, as well as for professional retraining of specialists, which allows preserving and reusing the collective experience of learning in an educational institution. The proposed solution differs from the existing ones in that the system of ontologized design of electronic courses (SODEC). It allows to automate the development of electronic courses (EC) and / or electronic textbooks (ET), reduces the cost and time of preparing ET, ensures compliance of EC with the current state in the field of ICT. As an example, the design section of ET for the topic "databases" is considered. The proposed solution allows to accumulate personal materials of the teacher and contributes to the rapid updating of the content and content of ET, if necessary. It is concluded that for more effective use of the proposed methodology, it is necessary to develop a similar SODEC, taking into account the language of description of CO – OWL, which will allow to accumulate collective experience in the methods of submitting material to teachers of educational institutions or extracurricular institutions, as well as during professional retraining of specialists

Axiomatizing SNOMED CT Disorders: Should There Be Room for Interpretation?

SNOMED CT is a large concept-based terminology designed according to epistemic, semantic and pragmatic principles relevant to clinicians. Its goal is structured clinical reporting in electronic healthcare records (EHRs). The Basic Formal Ontology (BFO) is an ontology designed on the basis of types claimed to exist in reality based on a domain-independent ontological theory. Its goal is faithful representation of reality within that theory. The Ontology for General Medical Science (OGMS) extends the BFO by providing definitions for types relevant within the clinical domain. Combining SNOMED CT with the ontological rigor of BFO and OGMS might improve clinical reporting by, f.i., preventing data entry mistakes and inconsistencies, and make EHRs more comparable. To that end, we are developing a logical framework capable of exploiting what SNOMED CT offers terminologically and realism-based ontologies such as the BFO and the OGMS ontologically by means of bridging axioms compatible with the BFO, and expressed in the same CLIF-dialect as used in its axiomatization in first order logic. In this paper, we report on our attempts to detect in the combinations of binary relations that are used in the definition of SNOMED CT's definitions of disorder concepts patterns which might at least partially automate the construction of such axioms. Our findings suggest that this partial automation is indeed possible, but to a smaller extent than we had hoped for. We compare our approach with a recent proposal that seeks to bring SNOMED CT and BFO closer together by reinterpreting SNOMED CT disorders as clinical occurrents. The proposal has its merit in providing a realist underpinning for that part of SNOMED CT's concept model in terms of the BFO, but is not discriminatory enough for an automatic translation into OGMS. Key problem is the lack of face validity of SNOMED CT disorder terms as compared to the formal definitions they are given and this in absence of textual definitions.

In search of a non-anthropocentric middle-range theory of the firm: On how the Patagonia Purpose Trust granted a controlling stake to nature

Patagonia, an outdoor clothing company thrust into the limelight by a New York Times full-page ad that in 2001 invited customers not to buy its own gear, has recently responded to the inherently low capacity of firms to supply ecosystem services. Since the firm is a generic term encompassing many different bundles of property rights, the specific mechanism of the Patagonia Purpose Trust is researched to find whether it has plausibly locked the company in on preserving ecosystems through its environmentally driven community investment, product re-design, and supply chain overhaul. A mechanism-based methodology is employed, organizing the units of observation in keeping with a formal ontology and benchmarking Patagonia's mechanism against the generic templates of a corporation and a charitable Trust to highlight what is distinct about Patagonia. The findings are that, in a nutshell, Patagonia's mechanism consists of: 1) increasing the opportunity cost of not redressing a breach of purpose by designating an ascertained beneficiary with an unequivocal interest in Nature's preservation; 2) offsetting fiduciaries' influence on asset management with a countervailing mechanism; and 3) designing a procedure centered on the renewal of credible co-trustees to keep checking on fiduciaries in the long run.

Онтология направляемого развития научных способностей молодѐжи. Часть 2: общая схема

The formation of a holistic ontology of developmental activities of gifted youth aged between 14 and 23, called “advanced educational and research activities,” which began in the first part of the article [1], is continued. This form of independent activity of a motivated schoolchild or student is aimed at satisfying his cognitive, intellectual and other needs and developing creative potential, is associated with the consistent solution of research problems, and is supported by a specially organized developmental scientific and educational environment, which involves a periodic unified assessment of the developmental effect and the scientific significance of the results obtained. In addition to the article's first part [1], the ontology of the development of a young researcher's personality characteristics, which include research activity, erudition, range of interests and socialization in the scientific community, is described. For the first time, on a quantitative basis using the author's mathematical model, the possibilities of taking into account the connection between research functionality and the research motivation of a young researcher when planning the developmental structure of research work are analyzed. The blocks of erudition, range of interests and socialization of a multifaceted system of criteria for assessing personal qualities are proposed. The general scheme of the guided development of a young researcher within the framework of his educational and research activities is described on the basis of such concepts as: multifaceted systems of criteria for assessing research work and the personal characteristics of a young researcher, a mathematical model for optimizing the developmental structure of research work, a mathematical model for the formation of optimal options for personal development, a consulting system of guiding support for individual development, etc. The introduced concepts are combined in the form of a graphic diagram of the ontology of guided development of scientific abilities of young people.

Модели и методы онтологического анализа данных в задаче структурного анализа и синтеза технических решений

The article explores the potential and demonstrates the effectiveness of using some models and methods of ontological data analysis in the problem of structural analysis and synthesis of technical decisions. Morphological analysis and synthesis of layout diagrams of artifacts are considered as a working methodology for structural design. A set-theoretic model of the morphological decision space is constructed for the classical version of the methodology. The model in-cludes the functional subsystems of the designed artifact, implementation options for these subsystems, and potential technical decisions themselves. Moreover, in contrast to existing versions of such formalization, the emphasis is on the description of binary relationships between the entities of the knowledge domain of morphological analysis. It is pro-posed to describe the incompatibility of implementation options for a single functional subsystem of a designed arti-fact and the restricted compatibility of implementation options for various functional subsystems within the frame-work of a single model, known in ontological engineering and, in particular in ontological data analysis as “existence constraints”. Analysis of one of the regularities inherent in this model made it possible to strictly substantiate the ap-proach common in structural design to describing the restricted compatibility of implementation options for various functional subsystems in the form of prohibitions. The advantages of using existence constraints to describe the mor-phological decision space are shown, and the frame of a formal ontology for the knowledge domain of morphological analysis is constructed. The structural synthesis of a technical decision is compared with the method of semantic identi-fication of an object, developed within the framework of ontological data analysis for processing incomplete and in-consistent empirical information about the results of measuring object properties. A conclusion is made about the suit-ability of the latter for solving the problem of structural synthesis when correcting the concept of a “normal” subset of properties and information about the costs and/or benefits of including each individual implementation option for the functional subsystems of the designed artifact.

SNOMED CT and Basic Formal Ontology – convergence or contradiction between standards? The case of “clinical finding”

Background: SNOMED CT is a large terminology system designed to represent all aspects of healthcare. Its current form and content result from decades of bottom-up evolution. Due to SNOMED CT’s formal descriptions, it can be considered an ontology. The Basic Formal Ontology (BFO) is a foundational ontology that proposes a small set of disjoint, hierarchically ordered classes, supported by relations and axioms. In contrast, as a typical top-down endeavor, BFO was designed as a foundational framework for domain ontologies in the natural sciences and related disciplines. Whereas it is mostly assumed that domain ontologies should be created as extensions of foundational ontologies, a post-hoc harmonization of consolidated domain ontologies in use, such as SNOMED CT, is known to be challenging. Methods: We explored the feasibility of harmonizing SNOMED CT with BFO, with a focus on the SNOMED CT Clinical Finding hierarchy. With more than 100,000 classes, it accounts for about one third of SNOMED CT’s content. In particular, we represented typical SNOMED CT finding/disorder concepts using description logics under BFO. Three representational patterns were created and the logical entailments analyzed. Results: Under a first scrutiny, the clinical intuition that diseases, disorders, signs and symptoms form a homogeneous ontological upper-level class appeared incompatible with BFO’s upper-level distinction into continuants and occurrents. The Clinical finding class seemed to be an umbrella for all kinds of entities of clinical interest, such as material entities, processes, states, dispositions, and qualities. This suggests the conclusion that Clinical finding would not be a suitable upper-level class from an BFO perspective. On closer inspection of the taxonomic links within this hierarchy and the implicit meaning derived thereof, it became clear that Clinical finding classes do not characterize the entity (e.g. a fracture, allergy, tumor, pain, hemorrhage, seizure, fever) in a literal sense but rather the condition of a patient having that fracture, allergy, pain etc. This gives sense to the current characteristic of the Clinical Finding hierarchy, in which complex classes are modeled as subclasses of their constituents. Most of these taxonomic links are inferred, as the consequence of the ‘role group’ design pattern, which is ubiquitous in SNOMED CT and has often been subject of controversy regarding its semantics. Conclusion: Our analyses resulted in the proposal of (i) equating SNOMED CT’s ‘role group’ property with the reflexive and transitive BFO relation ‘has occurrent part’; and (ii) reinterpreting Clinical Findings as Clinical Occurrents, i.e. temporally extended entities in an organism, having one or more occurrents as temporal parts that occur in continuants. This re-interpretation was corroborated by a manual analysis of classes under Clinical Finding, as well as the identification of similar modeling patterns in other ontologies. As a result, SNOMED CT does not require any content redesign to establish compatibility with BFO, apart from this re-interpretation, and a suggested re-labeling. Regarding the feasibility of harmonizing terminologies with principled foundational ontologies post-hoc, our results provide support to the assumption that this does not necessarily require major redesign efforts, but rather a careful analysis of the implicit assumptions of terminology curators and users.

Integrating Data on Early Medieval Graves: Mapping the THANADOS database to the ARIADNE infrastructure with the Mortuary Data Application Profile

This article discusses the creation of an Application Profile (AP) for mortuary data and how it was used to integrate the THANADOS anthropological and archaeological database of sepultures in the ARIADNEplus infrastructure. Ontologies increase data interoperability and their potential for being reused. The CIDOC CRM is a formal ontology designed to facilitate the integration, mediation, and interchange of heterogeneous cultural heritage information. APs provide exemplary mappings that other researchers can use as an orientation. For creating and testing the ARIADNE mortuary data AP, data from early medieval cemeteries provided an ideal case study because of the high degree of similarity between sites. The mapping of early medieval cemetery data has shown that mapping to an ontology contributes to data interoperability in several ways. Firstly, it may reveal inconsistencies in the native dataset and improve data organisation for future projects. Secondly, it enables the querying of information concerning graves and cemeteries from different sources across the ARIADNE infrastructure. Finally, this article provides an example of how archaeological data can be aggregated across multiple providers.

Boundaries of the built environment: defining the significance of the material presence of spatial morphology in social life

Settled societies inhabit environments shaped by building activity. Geographic data in social scientific and geographical research are generally composed of architectural and social categories derived from commonplace lived experience and societal knowledge, thus carrying socio-culturally specific meaning. The mundane pragmatism of such categories conflate spaces and buildings with their use and may obstruct effective comparison. Here I introduce a formal redescriptive ontology for built environments that operates on the basis of how differentiation and subdivision constitute distinct occupiable spaces through boundaries. The ontology consists of formally redescriptive socio-spatial and material concepts called ‘Boundary Line Types’ (BLT). I present and photographically illustrate the definitions of the BLTs, which are formulated on a critical realist basis and rooted in a multidisciplinary body of theory concerning the development and inhabitation of built space. Considering inhabited built environments through this ontology foregrounds the emergent logic by which spaces are divided and connected, creating configurations of boundaries as material frames that afford everyday social life. Since BLTs offer transferrable empirical principles from which these material frames emerge, they also enable diachronic and cross-cultural comparative social research. My proposition to approach social scientific built environment research through constitutive material boundaries offers a comparative complement to commonplace and socio-culturally specific spatial categories that compose most geographic data, enabling formal thick redescriptions and the potential for quantitative spatial analysis.

The British Geological Survey Rock Classification Scheme, its representation as linked data, and a comparison with some other lithology vocabularies

Controlled vocabularies are critical to constructing FAIR (findable, accessible, interoperable, re-useable) data. One of the most widely required, yet complex, vocabularies in earth science is for rock and sediment type, or ‘lithology’. Since 1999 the British Geological Survey has used its own Rock Classification Scheme in many of its workflows and products including the national digital geological map. This scheme pre-dates others that have been published, and is deeply embedded in BGS’ processes. By publishing this classification scheme now as a Simple Knowledge Organisation System (SKOS) machine-readable informal ontology, we make it available for ourselves and third parties to use in modern semantic applications, and we open the future possibility of using the tools SKOS provides to align our scheme with other published schemes. These include the IUGS-CGI Simple Lithology Scheme, the European Commission INSPIRE Lithology Code List, the Queensland Geological Survey Lithotype Scheme, the USGS Lithologic Classification of Geologic Map Units, and Mindat.org. The BGS lithology classification was initially based on four narrative reports that can be downloaded from the BGS website, although it has been added to subsequently. The classification is almost entirely mono-hierarchical in nature and includes 3454 currently valid concepts in a classification 11 levels deep. It includes igneous rocks and sediments, metamorphic rocks, sediments and sedimentary rocks, and superficial deposits including anthropogenic deposits. The SKOS informal ontology built on it is stored in a triplestore and the triples are updated nightly by extracting from a relational database where the ontology is maintained. Bulk downloads and version history are available on github. The RCS concepts themselves are used in other BGS linked data, namely the Lexicon of Named Rock Units and the linked data representation of the 1:625 000 scale geological map of the UK. Comparing the RCS with the other published lithology schemes, all are broadly similar but show characteristics that reveal the interests and requirements of the groups that developed them, in terms of their level of detail both overall and in constituent parts. It should be possible to align the RCS with the other classifications, and future work will focus on automated mechanisms to do this, and possibly on constructing a formal ontology for the RCS.

An Ontology to Bridge the Clinical Management of Patients and Public Health Responses for Strengthening Infectious Disease Surveillance: Design Science Study.

Novel surveillance approaches using digital technologies, including the Internet of Things (IoT), have evolved, enhancing traditional infectious disease surveillance systems by enabling real-time detection of outbreaks and reaching a wider population. However, disparate, heterogenous infectious disease surveillance systems often operate in silos due to a lack of interoperability. As a life-changing clinical use case, the COVID-19 pandemic has manifested that a lack of interoperability can severely inhibit public health responses to emerging infectious diseases. Interoperability is thus critical for building a robust ecosystem of infectious disease surveillance and enhancing preparedness for future outbreaks. The primary enabler for semantic interoperability is ontology. This study aims to design the IoT-based management of infectious disease ontology (IoT-MIDO) to enhance data sharing and integration of data collected from IoT-driven patient health monitoring, clinical management of individual patients, and disparate heterogeneous infectious disease surveillance. The ontology modeling approach was chosen for its semantic richness in knowledge representation, flexibility, ease of extensibility, and capability for knowledge inference and reasoning. The IoT-MIDO was developed using the basic formal ontology (BFO) as the top-level ontology. We reused the classes from existing BFO-based ontologies as much as possible to maximize the interoperability with other BFO-based ontologies and databases that rely on them. We formulated the competency questions as requirements for the ontology to achieve the intended goals. We designed an ontology to integrate data from heterogeneous sources, including IoT-driven patient monitoring, clinical management of individual patients, and infectious disease surveillance systems. This integration aims to facilitate the collaboration between clinical care and public health domains. We also demonstrate five use cases using the simplified ontological models to show the potential applications of IoT-MIDO: (1) IoT-driven patient monitoring, risk assessment, early warning, and risk management; (2) clinical management of patients with infectious diseases; (3) epidemic risk analysis for timely response at the public health level; (4) infectious disease surveillance; and (5) transforming patient information into surveillance information. The development of the IoT-MIDO was driven by competency questions. Being able to answer all the formulated competency questions, we successfully demonstrated that our ontology has the potential to facilitate data sharing and integration for orchestrating IoT-driven patient health monitoring in the context of an infectious disease epidemic, clinical patient management, infectious disease surveillance, and epidemic risk analysis. The novelty and uniqueness of the ontology lie in building a bridge to link IoT-based individual patient monitoring and early warning based on patient risk assessment to infectious disease epidemic surveillance at the public health level. The ontology can also serve as a starting point to enable potential decision support systems, providing actionable insights to support public health organizations and practitioners in making informed decisions in a timely manner.

GeoFault: A well-founded fault ontology for interoperability in geological modeling

Geological modeling currently uses various computer-based applications. Data harmonization at the semantic level using ontologies is essential to make these applications interoperable. Since geo-modeling is part of several multidisciplinary projects, interoperability requires semantic harmonization to exchange information between geological applications and integrate other domain knowledge at a general level. Therefore, domain ontologies that describe geological knowledge must be based on a sound ontological background to ensure this knowledge is integrable. Faults are essential for understanding and solving structural problems but are complex to model because the concept of fault includes a group of geological entities with a distinct ontological nature. A fault can correspond to thin, deformed rock volumes or spatial arrangements resulting from the displacement of geological blocks, but at a broader scale, geologists describe faults as surfaces or components of complex fault arrays. Our work intends to harmonize these views by presenting a domain ontology, GeoFault, resting on the Basic Formal Ontology (BFO) and the GeoCore ontology. GeoCore and GeoFault support the parametric description of geological sites as a preliminary step for quantitative and qualitative analysis. We have proposed GeoFault after systematically revising the literature and several knowledge-acquisition sessions with expert structural geologists. The ontology formalizes a vocabulary for fault “sensu stricto,” excluding ductile shear deformations. It covers the regional to outcrop scales, excluding structures at the microscopic, orogenic, and tectonics scales, and it avoids interpretive language associated with geological processes as far as possible. Extending the BFO and GeoCore ontologies allows the fault concept to be related to formal ontological classes in a consistent semantic-rich framework. The ontology artifact is implemented in OWL 2, validated by competency questions with two use cases, and tested using an in-house ontology-driven data entry application. The GeoFault ontology is publicly available and provides a solid framework for clarifying fault knowledge and a foundation for many applications.

Deep Ontology Alignment Using a Natural Language Processing Approach for Automatic M2M Translation in IIoT.

The technical capabilities of modern Industry 4.0 and Industry 5.0 are vast and growing exponentially daily. The present-day Industrial Internet of Things (IIoT) combines manifold underlying technologies that require real-time interconnection and communication among heterogeneous devices. Smart cities are established with sophisticated designs and control of seamless machine-to-machine (M2M) communication, to optimize resources, costs, performance, and energy distributions. All the sensory devices within a building interact to maintain a sustainable climate for residents and intuitively optimize the energy distribution to optimize energy production. However, this encompasses quite a few challenges for devices that lack a compatible and interoperable design. The conventional solutions are restricted to limited domains or rely on engineers designing and deploying translators for each pair of ontologies. This is a costly process in terms of engineering effort and computational resources. An issue persists that a new device with a different ontology must be integrated into an existing IoT network. We propose a self-learning model that can determine the taxonomy of devices given their ontological meta-data and structural information. The model finds matches between two distinct ontologies using a natural language processing (NLP) approach to learn linguistic contexts. Then, by visualizing the ontological network as a knowledge graph, it is possible to learn the structure of the meta-data and understand the device's message formulation. Finally, the model can align entities of ontological graphs that are similar in context and structure.Furthermore, the model performs dynamic M2M translation without requiring extra engineering or hardware resources.

Using machine learning to extract information and predict outcomes from reports of randomised trials of smoking cessation interventions in the Human Behaviour-Change Project.

Background Using reports of randomised trials of smoking cessation interventions as a test case, this study aimed to develop and evaluate machine learning (ML) algorithms for extracting information from study reports and predicting outcomes as part of the Human Behaviour-Change Project. It is the first of two linked papers, with the second paper reporting on further development of a prediction system. Methods Researchers manually annotated 70 items of information ('entities') in 512 reports of randomised trials of smoking cessation interventions covering intervention content and delivery, population, setting, outcome and study methodology using the Behaviour Change Intervention Ontology. These entities were used to train ML algorithms to extract the information automatically. The information extraction ML algorithm involved a named-entity recognition system using the 'FLAIR' framework. The manually annotated intervention, population, setting and study entities were used to develop a deep-learning algorithm using multiple layers of long-short-term-memory (LSTM) components to predict smoking cessation outcomes. Results The F1 evaluation score, derived from the false positive and false negative rates (range 0-1), for the information extraction algorithm averaged 0.42 across different types of entity (SD=0.22, range 0.05-0.88) compared with an average human annotator's score of 0.75 (SD=0.15, range 0.38-1.00). The algorithm for assigning entities to study arms ( e.g., intervention or control) was not successful. This initial ML outcome prediction algorithm did not outperform prediction based just on the mean outcome value or a linear regression model. Conclusions While some success was achieved in using ML to extract information from reports of randomised trials of smoking cessation interventions, we identified major challenges that could be addressed by greater standardisation in the way that studies are reported. Outcome prediction from smoking cessation studies may benefit from development of novel algorithms, e.g., using ontological information to inform ML (as reported in the linked paper 3).

Nanoarray Enabled Size-Dependent Isolation and Proteomics Profiling of Small Extracellular Vesicle Subpopulations toward Accurate Cancer Diagnosis and Prognosis.

Small extracellular vesicles (sEVs) have emerged as noninvasive biomarkers in liquid biopsy due to their significant function in pathology and physiology. However, the phenotypic heterogeneity of sEVs presents a significant challenge to their study and has significant implications for their applications in liquid biopsies. In this study, anodic aluminum oxide films with different pore sizes (AAO nanoarray) were introduced to enable size-based isolation and downstream proteomics profiling of sEV subpopulations. The adjustable pore size and abundant Al3+ on the framework of AAOs allowed size-dependent isolation of sEV subpopulations through nanoconfined effects and Lewis acid-base interaction between AAOs and sEVs. Benefiting from the strong concerted effect, the simple AAO nanoarray enabled specific isolation of three sEV subpopulations, termed "50", "90", and "150 nm" groups, from 10 μL of complex biological samples within 10 min with high capture efficiencies and purities. Moreover, the nanopores of AAOs also acted as nanoreactors for comprehensive proteomic profiling of the captured sEV subpopulations to reveal their heterogeneity. The AAO nanoarray was first investigated on sEVs from a cell culture medium, where sEV subpopulations could be clearly distinguished, and three traditional sEV-specific proteins (CD81, CD9, and FLOT1) could be identified by proteomic analysis. A total of 3946, 3951, and 3940 proteins were identified from 50, 90, and 150 nm sEV subpopulations, respectively, which is almost twice the number compared to those obtained from the conventional approach. The concept was further applied to complex real-case sample analysis from prostate cancer patients. Machine learning and gene ontology (GO) information analysis of the identified proteins indicate that different-sized sEV subpopulations contain unique protein cargos and have distinct cellular components and molecular functions. Further receiver operating characteristic curve (ROC) analysis of the top five differential proteins from the three sEV subpopulations demonstrated the high accuracy of the proposed approach toward prostate cancer diagnosis (AUC > 0.99). More importantly, several proteins involved in focal adhesion and antigen processing and presentation pathways were found to be upregulated in prostate cancer patients, which may serve as potential biomarkers of prostate cancer. These results suggest that the sEV subpopulation-based AAO nanoarray is of great value in facilitating the early diagnosis and prognosis of cancer and opens a new avenue for sEVs in liquid biopsy.

Selecting accepted assertions in partially ordered inconsistent DL-Lite knowledge bases

The problem of inconsistency handling in knowledge bases has received considerable research attention in several settings and has met many applications. Inconsistency usually stems from the fact that the information available for reasoning, query answering and decision-making tasks is obtained from different, scattered and potentially unreliable information sources. Performing these tasks is often computationally expensive, especially for knowledge bases in which the data is endowed with preferences and/or uncertainty. In the context of formal ontologies, enriching the assertions (i.e. the data pieces) with conceptual knowledge makes the query answering task harder computationally. Tractable results have been successfully established for the lightweight languages of the DL-Lite family. In this paper, we present two tractable methods for handling inconsistency in partially ordered lightweight ontologies. In essence, both methods are inspired from the well-known Intersection of ABox Repair (IAR) semantics, which proceeds by replacing the initial inconsistent knowledge base with the intersection of all of its maximally consistent sub-bases. The first method assumes a partial preorder over the knowledge base and identifies the accepted assertions, called ‘elected’, in polynomial time in data complexity. The second method captures uncertainty quantitatively in the framework of possibility theory and identifies the so-called ‘π-accepted’ assertions, also in polynomial time in data complexity. However, both characterisations require the conflict set to be readily available. This constitutes a serious limitation in frameworks and applications where the conflict set cannot be exhibited efficiently. In this paper, we propose a new and equivalent characterisation for each method. We show that the elected assertions and also the π-accepted assertions can be identified in polynomial time in the size of the dataset for lightweight ontologies.