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The processes of service discovery and composition are crucial tasks in application development driven by Web Services. However, with RESTful Web Service replacing SOAP-based Web Service as the dominant service-providing approach, the research on service discovery and composition should also shift its focus from SOAP-based Web Service to RESTful Web Service. The unstructured, resource-oriented and unified interface characteristics of RESTful Web Service pose challenges to its discovery and composition process. In this work, a framework for implementing RESTful Web Service discovery and automatic composition based on semantic technology is proposed. Firstly, the framework uses the OpenAPI Specification (OAS), which is extended by resource attributes, as the RESTful Web Service description specification, and then supports semantic-based matching discovery and automatic composition by attaching the concepts of domain ontology to the extended OAS. Secondly, the framework is fully adapted to REST features and provides a method for building service composition dependencies during registration, which is used to generate composition schemes during the service discovery process. Finally, the framework provides a discovery method that can return RESTful Web services to the requester in the form of single-point services or service composition schemes according to the magnitude of the semantic similarity with the requester's requirements. We applied the proposed methods to experiment with RESTful Web services in three different fields, and the results show that the methods effectively calculate the similarity between RESTful single-point Web services or composite Web services and service requests with the support of domain ontology. [ABSTRACT FROM AUTHOR]

Multi-example query over knowledge graphs allows users to provide multiple example fragments to express their intention, overcoming the shortage of single example query asks users to give a specific complete query example. Unfortunately, the existing multi-example query ignores the semantic relevance of entities, and query results are inconsistent in compactness, which causes it difficult for users to check their interesting results. Therefore, we first define a multi-example query over ontology-label knowledge graphs to facilitate users to express their query intentions and improve the semantic relevance of query results. Secondly, we propose a matching pattern to analyze users' query intention to prioritize the more compact query results to improve users' satisfaction. Specifically, we first use the OELIndex to filter the search space and improve query efficiency. Then, we construct a connected matching pattern according to query examples to get a more compact query result. After that, we select the pattern fragments with the smallest amount for priority matching by assessing the number of candidate sets, significantly reducing the isomorphic computations. Finally, extensive experiments are carried out on real data sets to verify the effectiveness and practicability of our proposed method. [ABSTRACT FROM AUTHOR]

Background: Despite the clear clinical diagnostic criteria for necrozoospermia in andrology, the fundamental mechanisms underlying it remain elusive. This study aims to profile the lipid composition in seminal plasma systematically and to ascertain the potential of lipid biomarkers in the accurate diagnosis of necrozoospermia. It also evaluates the efficacy of a lipidomics-based random forest algorithm model in identifying necrozoospermia. Methods: Seminal plasma samples were collected from patients diagnosed with necrozoospermia (n = 28) and normozoospermia (n = 28). Liquid chromatography–mass spectrometry (LC–MS) was used to perform lipidomic analysis and identify the underlying biomarkers. A lipid functional enrichment analysis was conducted using the LION lipid ontology database. The top 100 differentially significant lipids were subjected to lipid biomarker examination through random forest machine learning model. Results: Lipidomic analysis identified 46 lipid classes comprising 1267 lipid metabolites in seminal plasma. The top five enriched lipid functions as follows: fatty acid (FA) with ≤ 18 carbons, FA with 16–18 carbons, monounsaturated FA, FA with 18 carbons, and FA with 16 carbons. The top 100 differentially significant lipids were subjected to machine learning analysis and identified 20 feature lipids. The random forest model identified lipids with an area under the curve > 0.8, including LPE(20:4) and TG(4:0_14:1_16:0). Conclusions: LPE(20:4) and TG(4:0_14:1_16:0), were identified as differential lipids for necrozoospermia. Seminal plasma lipidomic analysis could provide valuable biochemical information for the diagnosis of necrozoospermia, and its combination with conventional sperm analysis may improve the accuracy and reliability of the diagnosis. [ABSTRACT FROM AUTHOR]

With the massive amount of data available on the Internet, many users face problems when they need access to the information and goods they need. Adapting information to user needs has become a complex and time-consuming process. Recommender systems are powerful tools for guiding users in electronic settings to information, services, and goods of interest. With the ability to identify users and predict their preferences, recommender systems can extract information likely to be of interest to users from vast amounts of data and save time and energy by providing them with relevant recommendations. Therefore, in this paper, a framework for a recommender system is proposed, which consists of four main phases. In the first phase, the preprocessing operation is performed. The purpose of this phase is to create an ontology model for products based on the convolutional neural network method. Data collection operations are carried out in the second phase. The purpose of this phase is to receive and store user information based on behavior and different criteria. The third phase is related to creating an ontology. The purpose of this phase is to create an ontology model for user behavior using OWL libraries. Finally, in the last phase, finding similarities and presented a proposal. The purpose of this phase is the necessary calculations of the proposed framework in the similarity finding section using ontology models. Simulation results show that the proposed framework measures MAE (more than 10 and 15%), RMSE (more than 12 and 16%), precision (less than 10 and 13%), recall (more than 25 and 28%), F1-score (higher than 25 and 30%) and Score of User (higher than 15 and 17%) outperform two related approaches, namely CF and CF+ Ontology, respectively. [ABSTRACT FROM AUTHOR]

Ontologies extraction is the cornerstone for a meaningful knowledge representation. Ontologies represent the semantic relations repository in a readable format with a clear representation of the domain knowledge. This made the automated ontologies construction a promising research objective with a direct and clear impact in many related fields, including knowledge base systems, text classification, etc. In this research, a workflow is set up for successful ontology learning from Arabic textual data. One of the bottlenecks for the text analytics field is the continuous requirement of up-to-date resources such as lexicons. This challenge is one of the main focuses of the current research, which proposes an automated ontology extraction method with no use of pre-defined resources. The research proposes a novel generic ontology learning and document classification model based on no utilization of prior text analysis resources. Moreover, a self-enrichment approach is proposed to ensure continuous knowledge construction. The research extends the ontology learning process to include the ontologies' semantic relationships, targeting a higher level of extraction and model enrichment. Two experiments have been applied with two different datasets that belong to different fields to ensure the generality of the proposed model. The results of the two experiments confirmed the high accuracy of the proposed model and its positive contribution to the classification task. The results of the ontology learning task reached 95%, while the classification task revealed the advancement of the Bagging algorithm over other machine learning algorithms with an accuracy equal to 97.92%. [ABSTRACT FROM AUTHOR]

In our societally extractive age, sport science risks being swept up in the intensifying desire to commodify the experiences of those that scientists proclaim to study. Coupled with the techno-digital revolution, this stems from a vertical (onto)logic that frames the sporting landscape as a static space filled with discrete objects waiting for us to capture, analyse, re-present and sell on as knowledge. Not only does this commodification degrade primary experience in the false hope of epistemological objectivity, it reinforces the unidirectionality of extractivism by setting inquirer apart from, and above of, inquiry. Here, we advocate for a different, more sentient logic grounded in the relationality of gifting as understood in indigenous philosophies. This foregrounds an ecological orientation to scholarship that sets out neither to objectify or describe that which is of concern, but to correspond with its becoming. On this, there are three threads we cast forward. First, in a corresponsive sport science, inhabitants are not objects of analysis, but lines in-becoming, who in answering to others, form knots in a meshwork. These knots constitute communal places in which inhabitants have joined with the differentiating coming-into-being of others. Second, knowledge is not authoritatively (re)cognitive, but humbly ecological; not produced vertically through imposition, but grown longitudinally in responsively moving from place to place. Third, research does not follow a vertically extractive (onto)logic, but is a practice of participant observation. This perspective appreciates that we, sport scientists, are also lines in-becoming that form parts of the knots in which we seek to know. In coda, our thesis is not a call for more qualitative or applied research in the sport sciences. It is a call to response-ably open up to that which sparks our curiosity, answering to what is shared with care, sensitivity and sincerity. [ABSTRACT FROM AUTHOR]

Large-scale association analyses using whole-genome sequence data have become feasible, but understanding the functional impacts of these associations remains challenging. Although many tools are available to predict the functional impacts of genetic variants, it is unclear which tool should be used in practice. This work provides a practical guide to assist in selecting appropriate tools for variant annotation. We conducted a MEDLINE search up to November 10, 2023, and included tools that are applicable to a broad range of phenotypes, can be used locally, and have been recently updated. Tools were categorized based on the types of variants they accept and the functional impacts they predict. Sequence Ontology terms were used for standardization. We identified 118 databases and software packages, encompassing 36 variant types and 161 functional impacts. Combining only three tools, namely SnpEff, FAVOR, and SparkINFERNO, allows predicting 99 (61%) distinct functional impacts. Thirty-seven tools predict 89 functional impacts that are not supported by any other tool, while 75 tools predict pathogenicity and can be used within the ACMG/AMP guidelines in a clinical context. We launched a website allowing researchers to select tools based on desired variants and impacts. In summary, more than 100 tools are already available to predict approximately 160 functional impacts. About 60% of the functional impacts can be predicted by the combination of three tools. Unexpectedly, recent tools do not predict more impacts than older ones. Future research should allow predicting the functionality of so far unsupported variant types, such as gene fusions. URL: https://cardio-care.shinyapps.io/VEP%5fFinder/. Registration: OSF Registries on November 10, 2023, https://osf.io/s2gct. [ABSTRACT FROM AUTHOR]

Ontologies serve as comprehensive frameworks for organizing domain-specific knowledge, offering significant benefits for managing clinical data. This study presents the development of the Fall Risk Management Ontology (FRMO), designed to enhance clinical text mining, facilitate integration and interoperability between disparate data sources, and streamline clinical data analysis. By representing major entities within the fall risk management domain, the FRMO supports the unification of clinical language and decision-making processes, ultimately contributing to the prevention of falls among older adults. We used Ontology Web Language (OWL) to build the FRMO in Protégé. Of the seven steps of the Stanford approach, six steps were utilized in the development of the FRMO: (1) defining the domain and scope of the ontology, (2) reusing existing ontologies when possible, (3) enumerating ontology terms, (4) specifying the classes and their hierarchy, (5) defining the properties of the classes, and (6) defining the facets of the properties. We evaluated the FRMO using four main criteria: consistency, completeness, accuracy, and clarity. The developed ontology comprises 890 classes arranged in a hierarchical structure, including six top-level classes with a total of 43 object properties and 28 data properties. FRMO is the first comprehensively described semantic ontology for fall risk management. Healthcare providers can use the ontology as the basis of clinical decision technology for managing falls among older adults. [ABSTRACT FROM AUTHOR]

The concept of dignity is not, as some scholars claim, an unnecessary moral idea, and nor need it have religious overtones or be characterised by speciesism. In this article, I try to show that dignity can be defined and recognised. The starting point for the argumentation is the four typologies of dignity, which show that the term 'dignity' can denote significantly different concepts, and that the different concepts of dignity can have significantly different ontological senses. A unified typology of dignity allows for five categories to be distinguished: inherent dignity, dignity based on changeable qualities, moral dignity, bestowed dignity and comportment dignity. I take the first two categories of dignity as the object of the analysis, with which I seek to formulate a philosophical response to the charge of speciesism and to show on what basis it can be maintained that all human beings possess dignity. To this end, I distinguish between existential dignity, actual dignity, and potential dignity. Distinguishing these types of dignity becomes possible in the light of Aquinas' and Aristotle's views. In the final section, I point to two ways of recognising dignity. The first is based on certain narratives and emotional states ('ecumenical model of dignity'), while the second is related to a specific moral experience developed within ethical personalism. [ABSTRACT FROM AUTHOR]

Background: Molecular-based approaches to understanding concussion pathophysiology provide complex biological information that can advance concussion research and identify potential diagnostic and/or prognostic biomarkers of injury. Objective: The aim of this study was to identify gene expression changes in peripheral blood that are initiated following concussion and are relevant to concussion response and recovery. Methods: We analyzed whole blood transcriptomes in a large cohort of concussed and control collegiate athletes who were participating in the multicenter prospective cohort Concussion Assessment, Research, and Education (CARE) Consortium study. Blood samples were collected from collegiate athletes at preseason (baseline), within 6 h of concussion injury, and at four additional prescribed time points spanning 24 h to 6 months post-injury. RNA sequencing was performed on samples from 230 concussed, 130 contact control, and 102 non-contact control athletes. Differential gene expression and deconvolution analysis were performed at each time point relative to baseline. Results: Cytokine and immune response signaling pathways were activated immediately after concussion, but at later time points these pathways appeared to be suppressed relative to the contact control group. We also found that the proportion of neutrophils increased and natural killer cells decreased in the blood following concussion. Conclusions: Transcriptome signatures in the blood reflect the known pathophysiology of concussion and may be useful for defining the immediate biological response and the time course for recovery. In addition, the identified immune response pathways and changes in immune cell type proportions following a concussion may inform future treatment strategies. [ABSTRACT FROM AUTHOR]

Faced with the challenges of intelligently managing the entire product lifecycle, traditional management techniques no longer meet the complex demands of modern enterprises. To address it, we employ an ontology modeling method based on a multidimensional view of product information for a knowledge graph, culminating in a comprehensive PLM knowledge system. To better serve PLM users, we propose an intelligent recommendation model based on context and user behavior for knowledge recommendations. This model seamlessly integrates the knowledge storage structure, user preference attributes, and the rich semantics of the knowledge graph. To improve the recommendation capability of the model, we design an improved multi-hop neighbor node sampling algorithm and use a graph convolutional neural network to quantify the target user preference attributes layer by layer. After a series of rigorous validation experiments, our method consistently outperformed competing models across key performance metrics, such as accuracy and recall, underscoring its efficacy and practicality in knowledge recommendation within PLM systems. [ABSTRACT FROM AUTHOR]

We use ontology to capture the knowledge about domains. There are considerable number of ontologies which have been developed. To develop a new ontology, we use the existing concepts of the ontologies according to the domain needs. The reusability of the concept maintains shareability of knowledge across the domains and prevents multiple interpretation of the defined concept. An Internationalized Resource Identifier (IRI) of the concept is used to connect the various concepts with each other. This work aims to provide an IRI-Debug tool that enables the ontologist to validate their crafted ontology against the standard ontologies using IRI and gauging to what extent their ontology's concepts or properties, if reused, compliant to the standard ontologies. This tool allows user to select a desired appropriate ontology from the available ontologies and validate the developed ontology with respect to standard ontologies. [ABSTRACT FROM AUTHOR]

Ontologies are domain-specific metadata that describe relationships between a specific field's properties, sample data of this field, and properties developed for many different purposes. Also, ontologies can be defined by other names within the same domain. Ontology matching algorithms eliminate definition differences and find similarities between existing ontologies. Ontology matching algorithms are used especially for information management, data integration, information extraction, etc. In this study, a graph-based framework is proposed to match large ontologies. This framework is aimed to divide the large ontologies into small pieces and then matches them using sub-graph mining algorithms. Karger algorithm and CP (clique percolation and nearest neighbor) algorithm are used to divide large ontologies. Both algorithms were applied to ontologies for the first time. In the next step, these obtained sub-parts are matched by using sub-graph mining algorithms. GraMi and gSpan algorithms were selected and were used for the first time in the field of ontology matching. We validated our framework using Anatomy and Conference data sets. Also, the proposed framework is compared with widely used in the literature AML and Falcon-AO matching algorithms. According to obtained the results, it is seen that GraMi is better than matching algorithms. [ABSTRACT FROM AUTHOR]

Clinical abbreviation disambiguation is a crucial task in the biomedical domain, as the accurate identification of the intended meanings or expansions of abbreviations in clinical texts is vital for medical information retrieval and analysis. Existing approaches have shown promising results, but challenges such as limited instances and ambiguous interpretations persist. In this paper, we propose an approach to address these challenges and enhance the performance of clinical abbreviation disambiguation. Our objective is to leverage the power of Large Language Models (LLMs) and employ a Generative Model (GM) to augment the dataset with contextually relevant instances, enabling more accurate disambiguation across diverse clinical contexts. We integrate the contextual understanding of LLMs, represented by BlueBERT and Transformers, with data augmentation using a Generative Model, called Biomedical Generative Pre-trained Transformer (BIOGPT), that is pretrained on an extensive corpus of biomedical literature to capture the intricacies of medical terminology and context. By providing the BIOGPT with relevant medical terms and sense information, we generate diverse instances of clinical text that accurately represent the intended meanings of abbreviations. We evaluate our approach on the widely recognized CASI dataset, carefully partitioned into training, validation, and test sets. The incorporation of data augmentation with the GM improves the model's performance, particularly for senses with limited instances, effectively addressing dataset imbalance and challenges posed by similar concepts. The results demonstrate the efficacy of our proposed method, showcasing the significance of LLMs and generative techniques in clinical abbreviation disambiguation. Our model achieves a good accuracy on the test set, outperforming previous methods. [ABSTRACT FROM AUTHOR]

Manual term extraction is similar to literal meaning: A translator browses text, classifies words, and prepares for translation. Terminology, as a centralized carrier of expertise, creation, popularization, and disappearance, dynamically reflects the development and evolution of an industry. The automatic extraction of terminology is a key technology for creating a professional terminology database, and it is also a key topic in the field of natural language processing. The purpose of this paper is to study how to analyse a term extraction algorithm based on machine learning and a comprehensive feature strategy. Focusing on the problems of poor generality and single statistical features of current term extraction algorithms, this paper proposes an improved domain ontology term extraction algorithm based on a comprehensive feature strategy. Moreover, automatic term extraction experiments based on a word-based maximum entropy model and a conditional random field model based on machine learning are conducted in this paper. Its word-based conditional random field model outperforms the maximum entropy model. The experimental results show that the algorithm based on the comprehensive feature strategy improves the accuracy by 8.6% compared with the TF-IDF algorithm and the C-value term extraction algorithm. This algorithm can be used to effectively extract the terms in a text and has good generality. [ABSTRACT FROM AUTHOR]

As scientific digitization advances it is imperative ensuring data is Findable, Accessible, Interoperable, and Reusable (FAIR) for machine-processable data. Ontologies play a vital role in enhancing data FAIRness by explicitly representing knowledge in a machine-understandable format. Research data in catalysis research often exhibits complexity and diversity, necessitating a respectively broad collection of ontologies. While ontology portals such as EBI OLS and BioPortal aid in ontology discovery, they lack deep classification, while quality metrics for ontology reusability and domains are absent for the domain of catalysis research. Thus, this work provides an approach for systematic collection of ontology metadata with focus on the catalysis research data value chain. By classifying ontologies by subdomains of catalysis research, the approach is offering efficient comparison across ontologies. Furthermore, a workflow and codebase is presented, facilitating representation of the metadata on GitHub. Finally, a method is presented to automatically map the classes contained in the ontologies of the metadata collection against each other, providing further insights on relatedness of the ontologies listed. The presented methodology is designed for its reusability, enabling its adaptation to other ontology collections or domains of knowledge. The ontology metadata taken up for this work and the code developed and described in this work are available in a GitHub repository at: https://github.com/nfdi4cat/Ontology-Overview-of-NFDI4Cat. [ABSTRACT FROM AUTHOR]

Cloud Computing has revolutionized the way applications are developed, deployed, and maintained. Over the past decade, we have observed dynamically growing interest in Cloud Computing. The benefits of the cloud approach caused the increasing popularity of Cloud-native applications. Cloud-native is an approach to developing and deploying applications according to the concepts of DevOps, Continuous Integration/Continuous Delivery (CI/CD), containers and microservices. The knowledge about Cloud Computing has become extensive and complex. Fortunately, before Cloud-native applications development, there was a great deal of effort to develop tools for effective knowledge representation. Ontologies are a convenient way to show the relations between domain-specific concepts. In this paper, we propose an ontology named CNOnt that describes the state-of-the-art of Cloud-native applications. CNOnt covers aspects from the clusterization perspective. First, this paper presents the engineering perspective of building the CNOnt ontology. Second, we demonstrate a use case of our ontology that proves the correctness of CNOnt development. This ontology is exhausted in CNOnt Broker. It is a system that applies the information in the OWL file into the Kubernetes cluster and in reverse. The knowledge representation makes Cloud-native applications understandable to third-party systems and increases interoperability between different microservices. [ABSTRACT FROM AUTHOR]

Model-driven engineering (MDE) uses models as first-class artefacts during the software development lifecycle. MDE often relies on domain-specific languages (DSLs) to develop complex systems. The construction of a new DSL implies a deep understanding of a domain, whose relevant knowledge may be scattered in heterogeneous artefacts, like XML documents, (meta-)models, and ontologies, among others. This heterogeneity hampers their reuse during (meta-)modelling processes. Under the hypothesis that reusing heterogeneous knowledge helps in building more accurate models, more efficiently, in previous works we built a (meta-)modelling assistant called Extremo. Extremo represents heterogeneous information sources with a common data model, supports its uniform querying and reusing information chunks for building (meta-)models. To understand how and whether modelling assistants—like Extremo—help in designing a new DSL, we conducted an empirical study, which we report in this paper. In the study, participants had to build a meta-model, and we measured the accuracy of the artefacts, the perceived usability and utility and the time to completion of the task. Interestingly, our results show that using assistance did not lead to faster completion times. However, participants using Extremo were more effective and efficient, produced meta-models with higher levels of completeness and correctness, and overall perceived the assistant as useful. The results are not only relevant to Extremo, but we discuss their implications for future modelling assistants. [ABSTRACT FROM AUTHOR]

With the advent of ICT-based healthcare applications, various formats of health data are generated every day in huge volume. Such data, consisting of unstructured, semi-structured and structured data, has every characteristic of Big data. NoSQL databases are generally preferred for storing such type of health data with the objective of improving query performance. However, for efficient retrieval and processing of Big Health Data and for resource optimization, suitable data models and design of the NoSQL databases are important requirements. Unlike relational databases, no standard methods or tools exist for NoSQL database design. In this work, we adopt an ontology-based schema design approach. We propose that an ontology, which captures the domain knowledge, be used for developing a health data model. An ontology for primary healthcare is described in this paper. We also propose an algorithm for designing the schema of a NoSQL database, keeping in mind the characteristics of the target NoSQL store, using a related ontology, a sample query set, some statistical information of the queries, and performance requirements of the query set. The ontology proposed by us for primary healthcare domain and the above mentioned algorithm along with a set of queries are used for generating a schema targeting MongoDB datastore. The performance of the proposed design is compared with a relational model developed for the same primary healthcare data and the effectiveness of our proposed approach is demonstrated. The entire experiment has been carried out on MongoDB cloud platform. [ABSTRACT FROM AUTHOR]

The paper presents an integrated approach that combines neural network and ontolinguistic paradigms. The method encompasses methodological underpinnings, information technology, and the MedRehabBot system. Collectively, they embody the core principles of meta-learning and structured prompts, ultimately enhancing the efficiency of information system interaction with Chatbots and information retrieval rooted in ontologies. The method also offers the flexibility to adapt the MedRehabBot system for utilization within different Large Language Model (LLM) systems. [ABSTRACT FROM AUTHOR]

Ontology is a significant data model for identifying semantic information for Coronavirus disease (COVID-19) discovery. Large-scale biological and general datasets have recently been more readily available for study and to support the development of COVID-19 repositioning, but efficiently utilizing and standard metadata modeling of these datasets still remains challenging. Coronavirus disease is a deadly disease. Researchers have focused on the different aspects of COVID-19, such as studies about COVID-19, symptoms, treatment, prevention, resources, infected cases, patient information, global impact, and related research. Compilation and analysis of metadata by knowledge gathered in various field studies can help in efficient data sharing and better decision-making of data related to COVID-19. However, considering the heterogeneous nature of data sources, it is challenging and innovative to make the COVID-19 information human and machine understandable and to provide answers to user queries at all possible aspects of the COVID-19 pandemic. This paper propose a COVID-19 ontology named as CovidO metadata model that provides a common conceptual model to facilitate interoperability between metadata ontologies from various heterogeneous data sources. The CovidO has the following objectives: (1) The ontology serves as a reference schema for reporting COVID-19 data. (2) It also offer a customized schema of the existing ontologies to build a standard global data model. (3) It covers all the possible aspects/dimensions of COVID-19. (4) the proposed model aims to utilize findability, accessibility, interoperability, and reusability (FAIR) principles by providing common conceptualization metadata and thereby abstracting from heterogeneous structures of existing sources of (static) data. The experiments are conducted over CovidO using OOPS, OntoMetric, and RDF/SPARQL query to evaluate the efficiency of the proposed model. The result shows that the proposed model has a broad scope with fewer pitfalls than existing COVID-19 ontologies. [ABSTRACT FROM AUTHOR]

In big data era, multi-source heterogeneous data become the biggest obstacle to data sharing due to its high dimension and inconsistent structure. Using text classification to solve the ontology construction and mapping problem of multi-source heterogeneous data can not only reduce manual operation, but also improve the accuracy and efficiency. This paper proposes an ontology construction and mapping scheme based on hybrid neural network and autoencoder. Firstly, the proposed text classification method uses the multi-core convolutional neural network to capture local features and uses the improved Bidirectional Long Short-Term Memory network to compensate for the shortcomings of the convolutional neural network that cannot obtain context-related information. Secondly, a similarity matching method is used for ontology mapping, which integrate autoencoder to improve anti-interference ability. We have carried out several sets of experiments to test the validity of the proposed ontology construction and mapping scheme. [ABSTRACT FROM AUTHOR]

Agile software development companies considered small entities (VSE) face a new reality of remote development. However, remote communication has generated many videos because video calls are often recorded for later reference. The architectural knowledge contained in videos, derived from virtual meetings, is essential for companies facing the knowledge vaporization problem. However, only some proposals in the literature can potentially manage AK in videos. The present article proposes a solution to recover this architectural knowledge contained in videos, using an ontology as a classification scheme. We based our proposal on the concept of architectural knowledge condensation and proposed a condensation cycle from it. Finally, we validated our ontology to manage architectural knowledge following the Methontology guidelines. Implementing an ontology as a classification scheme represents a step forward to achieving the condensation of architectural knowledge in an agile development environment for VSE. [ABSTRACT FROM AUTHOR]

These days, exploring information retrieval models is one of the most essential aspects of English sentence retrieval research. These models are driven by diverse retrieval mechanisms that offer varying similarity calculations and directly influence the final result ranking. However, despite decades of work due to technical constraints, deep semantic analysis has been challenging. This gap emphasizes the importance of a precise semantic understanding of information acquisition through learning approaches. Based on the above opening, this paper establishes a fast retrieval model of English sentences based on the statistical language model (SLM). First, the proposed method utilizes SLM to extract significant feature words from the corpus. These feature words are identified by analyzing co-occurrence patterns and frequency distributions within the standard. Second, it employs the N-gram model to calculate the probabilities of word occurrences based on their contextual dependencies. This framework represents feature words and their associated probabilities in a structured manner by capturing the intricate nuances of language semantics. Third, the model integrates ontology to bridge the gap between human language and machine understanding by enabling the mapping natural language expressions to conceptual entities. Finally, the suggested model retrieves English sentences through semantic matching by leveraging the comprehensive semantic framework and ontology-based search. The experimental study revealed that the proposed model demonstrated an impressive retrieval ratio of 98.5% by outperforming existing models in the comparison. Moreover, these results show that the proposed algorithm performs better than the Term Frequency-Inverse Document Frequency (TF-IDF) algorithm, and the accuracy of this algorithm is improved by 7.52% compared with TF-IDF. When the labelled corpus is very small and the unlabeled corpus is relatively large, the algorithm enhances the classifier's performance by 12.6%. This shows that the algorithm used in this paper reduces the influence of the synonym processing stage on the overall performance while retaining the advantages of high precision and accuracy of calculation results. [ABSTRACT FROM AUTHOR]

The advancements in sensors, processing, storage and networking technologies have turned smartphone into de facto lifelogging device. Realizing the lifelogging potential of smartphone, researchers have postulated with smartphone-based lifelogging systems with varying capabilities of capturing and storing information about peoples' daily life activities, behaviors, interactions, locations, environments, etc., into their lifelog archives. However, the systems are lacking with several capturing, storing, retrieving and design issues, importantly not organizing the lifelog information with the same semantics as they exist in the real world and encoded in human episodic memory. The semantic organization of lifelog information will develop a true digital prosthetic memory to solve problems in different real-world domains including memory augmentation. In this paper, we have presented the architecture and implementation of Semantic LifelOGging (SLOG) framework for smartphone-based semantic lifelogging using Semantic Web technologies. The SLOG framework incorporates smartphone sensors and processing capabilities, and Semantic Web technologies to develop a semantically enriched digital prosthetic memory on smartphone. The semantic lifelog data model is developed by extending SmartOntoSensor ontology with relevant concepts and properties to represent, store and relate lifelog and contextual information. The semantic lifelog data model will provide contextual semantics to organize, annotate and relate lifelog information like human episodic memory and provide enough contextual cues to recall lifelog information like associative recall in human memory. The proof-of-concept applications are developed to show practicality of the SLOG framework. The quantitative and empirical evaluations have shown the SLOG framework and system has improved features and is acceptable in usability and effectiveness. [ABSTRACT FROM AUTHOR]

The article presents the discussion on resilient and sustainable practice of African archaeology in future decades and centuries. Topics include Western students of Africa see the engagement with African ontologies as treacherous, undermining historical protocols and methods; and science-based facts opened a new understanding of the riches both sides gained by such alliances, with the Persian traders gaining ready-made networks.

Recently, we noticed the emergence of several data management architectures to cope with the challenges imposed by big data. Among them, data lakehouses are receiving much interest from industrial and academic fields due to their ability to hold disparate multi-structured batch and streaming data sources in a single data repository. Thus, the heterogeneous and complex aspect of the data requires a dedicated process to improve their quality and retrieve value from them. Therefore, data curation encompasses several tasks that clean and enrich data to ensure it continues to fit the user requirements. Nevertheless, most existing data curation approaches need more dynamics, flexibility, and customization in constituting the data curation pipeline to align with end user requirements that may vary according to her/his decision context. Moreover, they are dedicated to curating only a single type of structure of batch data sources (e.g., semi-structured). Considering the changing requirements of the user and the need to build a customized data curation pipeline according to the users and the data source characteristics, we propose a service-based framework for adaptive data curation in data lakehouses that encompasses five modules: data collection, data quality evaluation, data characterization, curation service composition, and data curation. The proposed framework is built upon new data characterization and evaluation modular ontology and a curation service composition approach that we detail in the following paper. The experimental findings validate the contributions' performance in terms of effectiveness and execution time. [ABSTRACT FROM AUTHOR]

Carbonate rocks record essential information on changes in paleoclimate and paleoceanography. Abundant geological and geochemical data of carbonate rocks have been accumulated over the past decades; however, most of the data are stored in the published literature with highly unstructured forms, and are thus difficult to reuse. The ontology is a standard knowledge model for data integration, which can promote data storage and reutilization. This study proposes a carbonate ontology that represents the concepts in carbonate microfacies. The carbonate ontology constructed by the top-down process contains 215 terms of classifications and petrographic descriptions of carbonate rocks. Furthermore, carbonate microfacies of the Cretaceous (Aptian) carbonate platform in the Betic Cordillera and Jurassic carbonate platform in Tibet provide the data from case studies for the testing and initial validation of the proposed ontology. The carbonate ontology is under continuous expansion following the bottom-up approach and open access on the website of the deep-time digital Earth (DDE) program. [ABSTRACT FROM AUTHOR]

As data size grows and computing power evolves, artificial intelligence has become one of the most important tools for assisting data-intensive scientific discoveries. The development of artificial intelligence applications in geoscience requires the understanding of enormous quantities of concepts and thus requires the organization of knowledge into a structured form, which is ontology. Compared with common-sense ontologies, the concepts in geoscience are extremely abstract and difficult to understand. It is challenging to use natural language processing technologies to build ontologies in geoscience from the bottom up. Meanwhile, applications of ontology in deep learning and data integration also reveal the importance of constructing a geoscience ontology. Because of the complexity and transdisciplinary nature, this study focuses on the field of tectonic geomorphology. Based on the understanding and experience of experts in geoscience, a top-down approach is used to construct a tectonic geomorphology ontology as part of the geoscience ontology. This research started with the proposal of a method for constructing ontologies, then built a tectonic geomorphology ontology, and finally checked, validated, and applied the ontology, covering common concepts in geoscience and dedicated concepts in tectonic geomorphology. The tectonic geomorphology ontology is an important part of the whole geoscience ontology. [ABSTRACT FROM AUTHOR]

Many detailed data on past geological hazard events are buried in geological hazard reports and have not been fully utilized. The growing developments in geographic information retrieval and temporal information retrieval offer opportunities to analyse this wealth of data to mine the spatiotemporal evolution of geological disaster occurrence and enhance risk decision making. This study presents a combined NLP and ontology matching information extraction framework for automatically recognizing semantic and spatiotemporal information from geological hazard reports. This framework mainly extracts unstructured information from geological disaster reports through named entity recognition, ontology matching and gazetteer matching to identify and annotate elements, thus enabling users to quickly obtain key information and understand the general content of disaster reports. In addition, we present the final results obtained from the experiments through a reasonable visualization and analyse the visual results. The extraction and retrieval of semantic information related to the dynamics of geohazard events are performed from both natural and human perspectives to provide information on the progress of events. [ABSTRACT FROM AUTHOR]

The occurrence of geological disasters can have a large impact on urban safety. Protecting people's safety is the most important concern when disasters occur. Safety improvement requires a large amount of comprehensive and representative risk analysis and a large collection of information related to geological hazards, including unstructured knowledge and experience. To address the relevant information and support safety risk analysis, a geological hazard knowledge graph is developed automatically based on computer vision and domain-geoscience ontology to identify geological hazards from input images while obeying safety rules and regulations, even when affected by changes. In the implementation of the knowledge graph, we design an ontology schema of geological disasters based on a top-down approach, and by organizing knowledge as a logical semantic expression, it can be shared using ontology technologies and therefore enable semantic interoperability. Computer vision approaches are then used to automatically detect a set of entities and attributes, using the data from input images, and object types and their attributes are identified so that they can be stored in Neo4j for reasoning and searching. Finally, a reasoning model for geological hazard identification was developed using the Neo4j database to create nodes, relationships, and their properties for modeling, and geological hazards in the images can be automatically identified by searching the Neo4j database. An application on geological hazard is presented. The results show the effectiveness of the proposed approach in terms of identifying possible potential hazards in geological hazards and assisting in formulating targeted preventive measures. [ABSTRACT FROM AUTHOR]

Content generation that is both relevant and up to date with the current threats of the target audience is a critical element in the success of any cyber security exercise (CSE). Through this work, we explore the results of applying machine learning techniques to unstructured information sources to generate structured CSE content. The corpus of our work is a large dataset of publicly available cyber security articles that have been used to predict future threats and to form the skeleton for new exercise scenarios. Machine learning techniques, like named entity recognition and topic extraction, have been utilised to structure the information based on a novel ontology we developed, named Cyber Exercise Scenario Ontology (CESO). Moreover, we used clustering with outliers to classify the generated extracted data into objects of our ontology. Graph comparison methodologies were used to match generated scenario fragments to known threat actors' tactics and help enrich the proposed scenario accordingly with the help of synthetic text generators. CESO has also been chosen as the prominent way to express both fragments and the final proposed scenario content by our AI-assisted Cyber Exercise Framework. Our methodology was assessed by providing a set of generated scenarios for evaluation to a group of experts to be used as part of a real-world awareness tabletop exercise. [ABSTRACT FROM AUTHOR]

Merging data from several databases is called heterogeneous database integration. Integrating diverse databases in the same area faces three major challenges that make solving the heterogeneity problem difficult. Semantic, syntactic, and structural heterogeneity are the three concerns. It also makes it tough to cope with semantic heterogeneity issues. In practice, difficulties such as missing sensory data and aberrant values caused by device failure still exist when assessing existing heterogeneous integrated data. This research study presents a Semantic Integration of Heterogeneous Healthcare Data based on Hybrid Root Linked Health Record (LHR) Ontology to overcome the drawbacks. We used semantic web technologies to connect various healthcare data from multiple devices. In addition, this study proposes a hybrid root LHR ontology, which samples health data from various databases. This ontology modeling has two different stages. In the first stage, the ontology rules were to translate the database rules to find an abstract ontology model and entropy mapping. Secondly, to expand the abstract ontology model according to the databases. This approach enables searching databases using SPARQL queries. As a result, the API is utilized to find semantically comparable records. The proposed experimental result has a accuracy of 99%, which is comparable to the existing GLAV accuracy of 92%, Fuzzy accuracy of 91%, and the LHR and SYMP accuracy of 95% and 93%, respectively. [ABSTRACT FROM AUTHOR]

As scientists start to adopt machine learning (ML) as one research tool, the security of ML and the knowledge generated become a concern. In this paper, I explain how supervised ML can be improved with better data ontology, or the way we make categories and turn information into data. More specifically, we should design data ontology in such a way that is consistent with the knowledge that we have about the target phenomenon so that such ontology can help us make the inductive leap. I do so by thinking through a thought experiment, Goodman's New Riddle of Induction (Fact, fiction, and forecast, Harvard University Press, 1955). Goodman's riddle helps flesh out three problems of induction: (1) the problem of equal goodies, that there are often too many equally good inductive results given the same data; (2) the problem of diverging performance, that these equally good results can give opposite predictions in the future; and (3) the problem of mediocrity, that when averaged across all equally possible datasets and tasks, no inductive algorithm outperforms any other. I show that all these three problems are manifested as real obstacles in ML practice, namely, the Rashomon effect (Breiman in Stat Sci 16(3):199–231, 2001), the problem of underspecification (D'Amour et al. in J Mach Learn Res, 2020, https://doi.org/10.48550/arXiv.2011.03395), and the No Free Lunch theorem (Wolpert in Neural Comput 8(7):1341–90, 1996, https://doi.org/10.1162/neco.1996.8.7.1341). Lastly, I argue that proper data ontology can help mitigate these problems and I demonstrate how using concrete examples from climate science. This research highlights the links between philosophers' discussions of induction and implications in ML practice. [ABSTRACT FROM AUTHOR]

The stages of creating systems with artificial intelligence, functional requirements for them, principles of organizing the architecture of cybernetic systems with artificial intelligence, and approaches to the implementation of the components of such a system are considered, in particular, the use of heuristics, evolutional approach, the methods of planning experiments, increments in knowledge, of ontologies for describing and excreting knowledge from texts, system analysis for structuring problems, etc. The efficiency of using principles of ring and linear organization of receptive fields of neurons, typical modes of functioning of the human visual analyzer, principles of hierarchical management in intellectual channels of information perception and processing in the brain and their interaction, and the ability to learn by experience and adaptation is shown. [ABSTRACT FROM AUTHOR]

The airfreight industry of shipping goods with special handling needs, also known as special cargo, often deals with non-transparent data and outdated technology, resulting in significant inefficiency. A special cargo ontology is a means of extracting, structuring, and storing domain knowledge and representing the concepts and relationships that can be processed by computers. This ontology can be used as the base of semantic data retrieval in many artificial intelligence applications, such as planning for special cargo shipments. Domain information extraction is an essential task in implementing and maintaining special cargo ontology. However, the absence of domain information makes instantiating the cargo ontology challenging. We propose a relation representation learning approach based on a hierarchical attention-based multi-task model and leverage it in the special cargo domain. The proposed relation representation learning architecture is applied for identifying and categorizing samples of various relation types in the special cargo ontology. The model is trained with domain-specific documents on a number of semantic tasks that vary from lightweight tasks in the bottom layers to the heavyweight tasks in the top layers of the model in a hierarchical setting. Therefore, it conveys complementary input features and learns a rich representation. We also train a domain-specific relation representation model that relies only on an entity-linked corpus of cargo shipment domain. These two relation representation models are then employed in a supervised multi-class classifier called Special Cargo Relation Extractor (SCRE). The results of the experiments show that the proposed relation representation models can represent the complex semantic information of the special cargo domain efficiently. [ABSTRACT FROM AUTHOR]

Sentence similarity is used in various fields, such as the mining of text, information retrieval from the web, and dialogue-based system. This research mainly focuses on calculating the sentence-length similarity between very brief texts. It provides a method that works on the implicit word order and contextual relations in the phrases. A combination of data from the corpus statistics and hierarchical database is used to determine the computation of similarity between sentence pairs. Our technique can simulate human sensible knowledge according to the usage of a lexical database, and it may be applied to other areas according to the incorporation of corpora statistics. Numerous applications that involve the representation and finding of text knowledge can make use of the suggested approach. Studies done on two sets of chosen sentence pairings reveal that the suggested approach offers a similarity metric that significantly correlates with human intuition. [ABSTRACT FROM AUTHOR]

The digital twin workshop is a new workshop operation paradigm that enables precise decision-making by fusing virtual and physical space. As a complex manufacturing system, the digital twin model of the workshop must integrate models from different domains in order to provide personalized services. The interoperability of multi-domain models and the dynamic update of parameters become obstacles. In this paper, a knowledge graph (KG)-based multi-domain model integration method for digital twin workshops is proposed. The multi-domain model integration architecture based on KG is consisted of model element, model ontology, model data, semantic integration, and network connection. Then, the KG of multi-domain model for design, manufacturing, and simulation is constructed through ontology modeling and knowledge extraction. On this basis, multi-domain model integration is realized through semantic inference and knowledge query. The model parameters are updated through file exchange during the dynamic simulation. Finally, multiple scenarios in the subassembly workshop for hull construction are used to verify the efficacy of the proposed method. During the assembly and welding of hull parts, the integration of the product model, equipment model, and simulation model is realized, which assists in meeting the service requirements of multiple business scenarios. [ABSTRACT FROM AUTHOR]

Automating ontology construction and curation is an important but challenging task in knowledge engineering and artificial intelligence. Prediction by machine learning techniques such as contextual semantic embedding is a promising direction, but the relevant research is still preliminary especially for expressive ontologies in Web Ontology Language (OWL). In this paper, we present a new subsumption prediction method named BERTSubs for classes of OWL ontology. It exploits the pre-trained language model BERT to compute contextual embeddings of a class, where customized templates are proposed to incorporate the class context (e.g., neighbouring classes) and the logical existential restriction. BERTSubs is able to predict multiple kinds of subsumers including named classes from the same ontology or another ontology, and existential restrictions from the same ontology. Extensive evaluation on five real-world ontologies for three different subsumption tasks has shown the effectiveness of the templates and that BERTSubs can dramatically outperform the baselines that use (literal-aware) knowledge graph embeddings, non-contextual word embeddings and the state-of-the-art OWL ontology embeddings. [ABSTRACT FROM AUTHOR]

Inferring aliasing and buffer-size information is important to understanding a C program's memory layout, which is critical to program analysis and security-related tasks. However, traditional static and dynamic program analysis methods suffer from certain limitations: static alias analysis methods suffer from precision loss and have poor scalability. Meanwhile, although dynamic analysis can achieve high precision, there is no soundness guarantee, and an online analysis may cause non-negligible runtime overhead. Besides, the current methods can only capture aliasing information. As for the buffer-size relational information, which is the specific variable storing the size of the buffer pointed by the pointers, it is tough to analyze by traditional methods. Moreover, we observe that most methods are designed for specific information. To address these limitations, we present ABSLearn, a deep learning framework that is capable of retrieving both aliasing and buffer-size information from C programs. The core idea of ABSLearn is to formulate the information retrieval as a link prediction problem, where a Graph Neural Network (GNN) model is applied to solve the problem. We developed the first related dataset that contains 285 C program samples to train ABSLearn. Then, the trained model is applied to infer the information on three practical benchmarks: Gzip-1.2.4, Make-3.80, and Tar-1.15.1. The results show that ABSLearn achieves comparable performance and excellent runtime performance. As the first attempt at applying GNN to infer aliasing and buffer-size information, ABSLearn can potentially benefit future program analysis frameworks. [ABSTRACT FROM AUTHOR]

The vision of the semantic web is to give a semantic perspective to the data so that data become a real-world entity rather than a string of characters. The most important step for achieving this vision is defining and describing the relations among the available data on the web. This is the place where ontologies serve as a backbone for the semantic web. An ontology is a knowledge representation scheme that offers enriched semantic meaning of data. Various ontologies are available on the web within the same or different domains with some common information among them that create a hinder during the mapping of the data due to their heterogeneous nature. The ontology alignment is a core solution to resolve this issue; hence, it is demanded to provide a sophisticated ontology alignment approach for semantic mapping. This paper defines a universal and Smart Ontology Alignment (SOA) approach for finding relations between entities by dividing the set of attributes of an entity into 'distinctive features' and 'cancellable features'. The SOA approach is termed smart because of the smart knowledge representation scheme it is based upon. State-of-the-art ontology alignment tools do not use this effect and offer wrong relations between the entities. The proposed structure of knowledge is believed to be more natural and comprehensible, and the relations found using SOA increase the performance of the system. The proposed SOA approach is tested with respect to five benchmarks, and the results show that the performance of our approach is near to optimal. [ABSTRACT FROM AUTHOR]

Expertise is an activity carried out by experts that contributes to societal progress, as it helps to elucidate unknown situations. For example, accident expertise eases accident understanding by describing how it happened and by identifying its causes and consequences. As a result, the design of accident expertise in a convenient human–machine structure will enable the querying, reasoning, and reuse of accident knowledge in other tools, such as safety and decision-making systems. However, existing representations of accident knowledge, such as documents, relational databases, or accident ontologies, do not fulfill accident expertise expectations. Moreover, these representations are unlikely to provide the appropriate use of accident expertise knowledge. This study presents a base ontology for accident expertise knowledge representation designed with a model-driven methodology and implemented with semantic web technologies. The study obtained satisfactory results from the evaluation and application of extension and reuse of this ontology with aircraft accident expertise taken from the French bureau of Enquiries and Analysis (BEA) for civil aviation safety. [ABSTRACT FROM AUTHOR]

Electronic coaching (eCoach) facilitates goal-focused development for individuals to optimize certain human behavior. However, the automatic generation of personalized recommendations in eCoaching remains a challenging task. This research paper introduces a novel approach that combines deep learning and semantic ontologies to generate hybrid and personalized recommendations by considering "Physical Activity" as a case study. To achieve this, we employ three methods: time-series forecasting, time-series physical activity level classification, and statistical metrics for data processing. Additionally, we utilize a naïve-based probabilistic interval prediction technique with the residual standard deviation used to make point predictions meaningful in the recommendation presentation. The processed results are integrated into activity datasets using an ontology called OntoeCoach, which facilitates semantic representation and reasoning. To generate personalized recommendations in an understandable format, we implement the SPARQL Protocol and RDF Query Language (SPARQL). We evaluate the performance of standard time-series forecasting algorithms [such as 1D Convolutional Neural Network Model (CNN1D), autoregression, Long Short-Term Memory (LSTM), and Gated Recurrent Units (GRU)] and classifiers [including Multilayer Perceptron (MLP), Rocket, MiniRocket, and MiniRocketVoting] using state-of-the-art metrics. We conduct evaluations on both public datasets (e.g., PMData) and private datasets (e.g., MOX2-5 activity). Our CNN1D model achieves the highest prediction accuracy of 97 % , while the MLP model outperforms other classifiers with an accuracy of 74 % . Furthermore, we evaluate the performance of our proposed OntoeCoach ontology model by assessing reasoning and query execution time metrics. The results demonstrate that our approach effectively plans and generates recommendations on both datasets. The rule set of OntoeCoach can also be generalized to enhance interpretability. [ABSTRACT FROM AUTHOR]

On account of prevalence of mobile devices, a variety of mobile-based services have been conducted recently. In this situation, it is possible to serve the information that fits to users' requests according to the users' context using mobile devices. Therefore, it is necessary to research data mining techniques with mobile devices. Therefore, we researched information retrieval system in mobile environment. We applied semantic data mining method with context information ontology. These techniques of semantic information retrieval can provide the information according to users' context information such as location, weather, user preference etc. exactly and rapidly anytime and anyplace. In modern society, it is essential to develop mobile information retrieval system which is efficient to users. Also, the system can provide users' current location and the location information with Google map APIs in mobile devices. Therefore, in this paper, we researched semantic data mining technique with context information ontology using mobile techniques. And we compared and evaluated our proposed model and the existing model in three areas and showed our proposed model has higher performance. [ABSTRACT FROM AUTHOR]

Agricultural development in Colombia has been characterized by being carried out in a local and traditional way, where important basic aspects are not always considered for the best performance of crops. These characteristics are presented in official government documentation distinguished by its heterogeneity. Ontologies in the domain of agriculture allow the organization and structuring of information to represent knowledge in such a way that the homogeneity of agricultural data dispersed in different types of documents such as manuals, weather reports, and official technical sheets is achieved. In accordance with the above, this work presents the development of an ontology in the agricultural domain to facilitate the identification of cultivation areas and improve land use, relating the basic concepts for an effective crop development according to the specifications and recommendations proposed in Colombian government documentation, using the Methontology methodology. This is achieved with the application of descriptive logic that, based on rules, generates inferences to identify the cultivation options and cultivable areas that present the highest performance. The interaction and use of the ontological model and inference rules are done through a web application made with Python and Flask. The precision of the model is evaluated using historical data of crops produced, making a comparison between the real data and the results obtained through the ontological model, obtaining as a result 80% reliability. [ABSTRACT FROM AUTHOR]

Recent research uncovering extraordinary organismal complexity (e.g., extent of symbiotic associations, genetic exchange between organisms) has led to empirical challenges concerning the recognition of individuality in biological organisms. Our ability to distinguish between organisms, parts of organisms, and groups of organisms is now more problematic. Further, this has led to philosophical challenges concerning the concept of biological individuality. The holobiont, or metaorganism, can be characterized as a host organism having important associations with numerous species of organismal exo- and endosymbionts, many of which are required for the proper function of the complex whole. The holobiont (indeed, all organisms) are also exposed to potential horizontal gene exchange throughout their life cycle. The blurring of organismality continues to the level of colonial organisms and other socially cooperating entities. Epistemologically, we recognize the metaorganism as a processual system subject to continuous change in itself and in relation to its environment. Whereas the metaorganism resists individuation in terms of its intrinsic properties of mixed origin, it acts as an individual by engaging in causal interactions that are predicated on its integrative nature and emergent properties. In terms of the biological individuality debate, the question is not "is the metaorganism a biological individual?" The question is rather "what individuating processes does the metaorganism (or any organism) engage in?" When explained in terms of the causal properties and propensities of its parts (which are of potentially mixed origin), the underlying conception of the metaorganism is that of a homeostatic property cluster. Thus, for metaorganisms, a hierarchical dialectic between natural kindness and individuality exists, depending on the epistemic project being pursued. A process ontology of organisms recognizes their fuzzy boundaries and life's dynamic nature while preserving the organism as a fundamental explanatory concept in biology. [ABSTRACT FROM AUTHOR]

Artificial intelligence is likely to undermine the anthropocentrism of humanism, the master narrative that undergirds the modern world. Humanity will need a new story to structure our beliefs and cooperation around. As different regions explore posthumanist alternatives through fiction, they bring with them distinct traditions of thought. The Swedish TV series Real Humans (2012–2014) and its British remake, Humans (2015–2018), dramatize the challenge of freeing oneself from cultural presumptions. When negotiating personhood with humanoid robots, the Swedish protagonist family presupposes a social-democratic ethos, which is a trace of Scandinavian humanism that carries into the family's posthumanist beliefs. Using Heideggerian and related perspectives, I analyze these series to make a case for a dataist ontology with potential to re-enchant the modern world and bring forth a new epoch of being. Such a master narrative of algorithmic universality could be facilitated by a new level of interconnectedness made possible by AI. This ontology fulfills the requirements of Robbins and Horta (Introduction, Cosmopolitanisms. New York University Press, New York, pp 1–17, 2017) who call for a cosmopolitanism of inclusivity with room for overlapping conceptions to remedy our present era's international dysfunction. The Swedish and British TV series suggest that even if humanity's uniting around a dataist master narrative were to be driven by intercultural competition, the decisive choice might be out of human hands. Paradoxically, such disempowering in terms of agency is portrayed as necessary for human re-enchantment. [ABSTRACT FROM AUTHOR]

Query expansion is an important approach utilized to improve the efficiency of data retrieval tasks. Numerous works are carried out by the researchers to generate fair constructive results; however, they do not provide acceptable results for all kinds of queries particularly phrase and individual queries. The utilization of identical data sources and weighting strategies for expanding such terms are the major cause of this issue which leads the model unable to capture the comprehensive relationship between the query terms. In order to tackle this issue, we developed a novel approach for query expansion technique to analyze the different data sources namely WordNet, Wikipedia, and Text REtrieval Conference. This paper presents an Improved Aquila Optimization-based COOT(IAOCOOT) algorithm for query expansion which retrieves the semantic aspects that match the query term. The semantic heterogeneity associated with document retrieval mainly impacts the relevance matching between the query and the document. The main cause of this issue is that the similarity among the words is not evaluated correctly. To overcome this problem, we are using a Modified Needleman Wunsch algorithm algorithm to deal with the problems of uncertainty, imprecision in the information retrieval process, and semantic ambiguity of indexed terms in both the local and global perspectives. The k most similar word is determined and returned from a candidate set through the top-k words selection technique and it is widely utilized in different tasks. The proposed IAOCOOT model is evaluated using different standard Information Retrieval performance metrics to compute the validity of the proposed work by comparing it with other state-of-art techniques. [ABSTRACT FROM AUTHOR]

The query processing on knowledge graphs has attracted significant attention in the past years. Different from the traditional query processing on knowledge graphs, the example query method can capture the users' query intentions by providing examples. But regrettably, it does not consider the semantic relevance of entities. Therefore, we first define an example query on the ontology-labels knowledge graph to better capture the query interest of users and improve the semantic relevance of query results. Second, a Filter-Refine Strategy-based method is proposed to solve the example queries. Specifically, we propose the ontology-labels tree index to reduce the search space and the bidirectional index to improve query efficiency. Then, an effective candidate results combination technology is used to return top-k results directly. Extensive experiments over two real-world data sets have shown our proposed algorithm is superior to three existing algorithms in terms of efficiency and effectiveness. [ABSTRACT FROM AUTHOR]