Domain-specific Ontology Research Articles

Physical Activity, Sedentary Behavior, and Sleep on Twitter: Multicountry and Fully Labeled Public Data Set for Digital Public Health Surveillance Research.

BackgroundAdvances in automated data processing and machine learning (ML) models, together with the unprecedented growth in the number of social media users who publicly share and discuss health-related information, have made public health surveillance (PHS) one of the long-lasting social media applications. However, the existing PHS systems feeding on social media data have not been widely deployed in national surveillance systems, which appears to stem from the lack of practitioners and the public’s trust in social media data. More robust and reliable data sets over which supervised ML models can be trained and tested reliably is a significant step toward overcoming this hurdle. The health implications of daily behaviors (physical activity, sedentary behavior, and sleep [PASS]), as an evergreen topic in PHS, are widely studied through traditional data sources such as surveillance surveys and administrative databases, which are often several months out-of-date by the time they are used, costly to collect, and thus limited in quantity and coverage.ObjectiveThe main objective of this study is to present a large-scale, multicountry, longitudinal, and fully labeled data set to enable and support digital PASS surveillance research in PHS. To support high-quality surveillance research using our data set, we have conducted further analysis on the data set to supplement it with additional PHS-related metadata.MethodsWe collected the data of this study from Twitter using the Twitter livestream application programming interface between November 28, 2018, and June 19, 2020. To obtain PASS-related tweets for manual annotation, we iteratively used regular expressions, unsupervised natural language processing, domain-specific ontologies, and linguistic analysis. We used Amazon Mechanical Turk to label the collected data to self-reported PASS categories and implemented a quality control pipeline to monitor and manage the validity of crowd-generated labels. Moreover, we used ML, latent semantic analysis, linguistic analysis, and label inference analysis to validate the different components of the data set.ResultsLPHEADA (Labelled Digital Public Health Dataset) contains 366,405 crowd-generated labels (3 labels per tweet) for 122,135 PASS-related tweets that originated in Australia, Canada, the United Kingdom, or the United States, labeled by 708 unique annotators on Amazon Mechanical Turk. In addition to crowd-generated labels, LPHEADA provides details about the three critical components of any PHS system: place, time, and demographics (ie, gender and age range) associated with each tweet.ConclusionsPublicly available data sets for digital PASS surveillance are usually isolated and only provide labels for small subsets of the data. We believe that the novelty and comprehensiveness of the data set provided in this study will help develop, evaluate, and deploy digital PASS surveillance systems. LPHEADA will be an invaluable resource for both public health researchers and practitioners.

A Review of NLIDB With Deep Learning: Findings, Challenges and Open Issues

Relational databases are storage for a massive amount of data. Knowledge of structured query language is a prior requirement to access that data. That is not possible for all non-technical personals, leading to the need for a system that translates text to SQL query itself rather than the user. Text to SQL task is also crucial because of its economic and industrial value. Natural Language Interface to Database (NLIDB) is the system that supports the text-to-SQL task. Developing the NLIDB system is a long-standing problem. Previously they were built based on domain-specific ontologies via pipelining methods. Recently a rising variety of Deep learning ideas and techniques brought this area to the attention again. Now end to end Deep learning models is being proposed for the task. Some publicly available datasets are being used for experimentation of the contributions, making the comparison process convenient. In this paper, we review the current work, summarize the research trends, and highlight challenging issues of NLIDB with Deep learning models. We discussed the importance of datasets, prediction model approaches and open challenges. In addition, methods and techniques are also summarized, along with their influence on the overall structure and performance of NLIDB systems. This paper can help future researchers start having prior knowledge of findings and challenges in NLIDB with Deep learning approaches.

A Compositional Adaptation-based Approach for Recommending Learning Resources in Software Development

In this paper, we discussed the application of a compositional adaptation approach to recommend learning resources to users in the area of software development. This approach makes use of a domain-specific ontology in this area to find those words, which are used in the technical description of the stored cases. A point peculiar with representing cases in the proposed approach is to take into account the characteristics of included learning resources, which justify the way they support the essential operations in the case of solution. In this way, only those components that comply with user’s request would be considered in the final solution. In the paper, the performance of the proposed approach for recommending learning resources together with the status of user experience in his/ her interaction with the resulted recommending system, have been evaluated. Results demonstrate the fact that the learning resources through this approach are sufficiently beneficial for the users. Although the proposed approach has been applied for recommending learning resources in the area of software development, it can be equally applied to any technological area through developing domain-specific ontology for that area. This is mainly because any technological area has its own specific objects/ entities holding their own semantic similarities that finally lead to forming a domain-specific ontology for that area.

Reorganizing Educational Institutional Domain using Faceted Ontological Principles

The purpose of this work is to find out how different library classification systems and linguistic ontologies arrange a particular domain of interest and what are the limitations for information retrieval. We use knowledge representation techniques and languages for construction of a domain specific ontology. This ontology would help not only in problem solving, but it would demonstrate the ease with which complex queries can be handled using principles of domain ontology, thereby facilitating better information retrieval. Facet-based methodology has been used for ontology formalization for quite some time. Ontology formalization involves different steps such as, Identification of the terminology, Analysis, Synthesis, Standardization and Ordering. Firstly, for purposes of conceptualization OntoUML has been used which is a well-founded and established language for Ontology driven Conceptual Modelling. Phase transformation of “the same mode” has been subsequently obtained by OWL-DL using Protégé software. The final OWL ontology contains a total of around 232 axioms. These axioms comprise 148 logical axioms, 76 declaration axioms and 43 classes. These axioms glue together classes, properties and data types as well as a constraint. Such data clustering cannot be achieved through general use of simple classification schemes. Hence it has been observed and established that domain ontology using faceted principles provide better information retrieval with enhanced precision. This ontology should be seen not only as an alternative of the existing classification system but as a Knowledge Base (KB) system which can handle complex queries well, which is the ultimate purpose of any classification system or indexing system. In this paper, we try to understand how ontology-based information retrieval systems can prove its utility as a useful tool in the field of library science with a particular focus on the education domain.

Ontology-aware dynamically adaptable free-form natural language agent interface for querying databases

Studying the literature, one can see a large number of systems that provide natural language interfaces to databases. Despite their importance, these interfaces address only one part of the problem: transforming natural language queries to SQL queries and perhaps executing them against the underlying database. To truly handle the problem, it should be possible to develop dynamically adaptable database interfaces that can (1) adjust their functional behavior on the fly using only domain knowledge and (2) dynamically bind themselves to arbitrary databases and interface with them with no (or very little) human intervention. In particular, the interfaces should have a fixed process (algorithms and codes) and rely only on domain knowledge for adapting their functionality to any arbitrary database. This paper addresses this problem by offering a free-form natural language interface agent that, given domain ontologies, can bind itself to a target database and provide a natural language interface to it. The agent system uses its ontologies to establish mappings on-the-fly between a specific domain ontology and the underlying database’s metadata, and then to transform free-form natural language queries to formal SQL queries that can execute against the underlying database. The preliminary simulations using our proof-of-concept prototype showed that our system successfully attached itself to databases and achieved high recall and precision in transforming natural language queries to formal ones.

Semantic Description of Explainable Machine Learning Workflows for Improving Trust

Explainable Machine Learning comprises methods and techniques that enable users to better understand the machine learning functioning and results. This work proposes an ontology that represents explainable machine learning experiments, allowing data scientists and developers to have a holistic view, a better understanding of the explainable machine learning process, and to build trust. We developed the ontology by reusing an existing domain-specific ontology (ML-SCHEMA) and grounding it in the Unified Foundational Ontology (UFO), aiming at achieving interoperability. The proposed ontology is structured in three modules: (1) the general module, (2) the specific module, and (3) the explanation module. The ontology was evaluated using a case study in the scenario of the COVID-19 pandemic using healthcare data from patients, which are sensitive data. In the case study, we trained a Support Vector Machine to predict mortality of patients infected with COVID-19 and applied existing explanation methods to generate explanations from the trained model. Based on the case study, we populated the ontology and queried it to ensure that it fulfills its intended purpose and to demonstrate its suitability.

Content extraction of historical Malay manuscripts based on Event Ontology Framework

This article aims to explore representation of the content knowledge of historical Malay manuscripts by extracting the event features using an event ontology framework. The manuscript used during the testing is Sulalatus Salatin (Sejarah Melayu ) by Abdul Ahmad Samad and it was published at University of Malaya Digital Library database. In aligning to a domain-specific ontology, the Simple Event Model (SEM) model is adopted and an event-based ontology for historical Malay manuscripts is designed. Information extraction approach is done manually to extract events from the manuscript and mapped into Protégé editor. Competency questions were constructed and submitted to the Protégé editor using SPARQL to check the ontology capability of providing answers as well as to examine its correctness. Event-based ontology model assists in discovering and representing the content knowledge of historical Malay manuscripts and supports organisation of knowledge. All the main concepts are extracted from selected Malay manuscript and 17 concepts used to develop the event-based ontology model. The knowledge was verified by three domain experts in Malay manuscript. In the findings, the interrater reliability for Event and Actor instances is 84%, which means 16% of instances and its type are incorrect and need amendment. For Place, interrater reliability is 95% and 99% for Role. Meanwhile, the experts achieved 100% agreement for Time. In addition, the experts agreed that the concepts, properties and instances for Malay Manuscript Ontology and complied with the criteria of consistency, completeness, conciseness, expandability and ease of use. The development of the event-based model of an ontology-based system with a high level of semantic granularity reflects the various cultural riches and intellectual aspect stored in Malay manuscripts. This will enable systematic research of the knowledge embedded in the manuscripts and make it widely and easily accessible by everyone.

Outer-Tuning: an Ontology-based Extensible Framework for Supporting Database Automatic Tuning

This paper presents the Outer-Tuning framework, which aims to support the (semi) automatic tuning of relational database systems through a domain-specific ontology. Ontologies have shown themselves to be increasingly promising, adding semantics and standardizing the different terms used in a domain. Thereby, our framework seeks to explain and make explicit the tuning heuristics reasoning while enabling the evaluation of new ontology-inferred methods. In this paper we focus on the main aspects of the Outer-Tuning component-based architecture. We also give an overview of our tool in practice. Finally, we show two useful extensions, concerning new DBMSs and a way of dockerizing into a container.

The DiSSCo Knowledgebase: A trusted information hub for the natural science collection community worldwide

In the last two decades, various projects and initiatives have conducted research on how to share, exchange, and link information from natural science collection objects. This profound (technical) knowledge, standards, tools, and best practices are essential to the development of any new research infrastructure facilitating research on bio- and geodiversity. However, the knowledge and research results are usually not easily accessible at a single point and particularly not in a well-curated form. Here, the Knowledgebase developed for the Distributed System of Scientific Collections (DiSSCo) comes into play. This information hub will act as trusted source for project outcomes and other relevant resources (e.g., web services, Persistent Identifier Systems, controlled vocabularies, domain-specific ontologies and standards) for users and developers of DiSSCo and other research infrastructures worldwide. In this talk, we will present the current version of the DiSSCo Knowledgebase, its developmental approach, and the opportunity for this source to act as an e-service for various stakeholder groups interested in and working with natural science collections worldwide.

Markup: A Web-Based Annotation Tool Powered by Active Learning.

Across various domains, such as health and social care, law, news, and social media, there are increasing quantities of unstructured texts being produced. These potential data sources often contain rich information that could be used for domain-specific and research purposes. However, the unstructured nature of free-text data poses a significant challenge for its utilisation due to the necessity of substantial manual intervention from domain-experts to label embedded information. Annotation tools can assist with this process by providing functionality that enables the accurate capture and transformation of unstructured texts into structured annotations, which can be used individually, or as part of larger Natural Language Processing (NLP) pipelines. We present Markup (https://www.getmarkup.com/) an open-source, web-based annotation tool that is undergoing continued development for use across all domains. Markup incorporates NLP and Active Learning (AL) technologies to enable rapid and accurate annotation using custom user configurations, predictive annotation suggestions, and automated mapping suggestions to both domain-specific ontologies, such as the Unified Medical Language System (UMLS), and custom, user-defined ontologies. We demonstrate a real-world use case of how Markup has been used in a healthcare setting to annotate structured information from unstructured clinic letters, where captured annotations were used to build and test NLP applications.

GOVT_EXAM ONTOLOGY IN SEMANTIC WEB FOR QUERY RETRIEVAL

The World wide web is growing day by day exponentially and it becomes difficult to find the relevant information even using efficient search engines. Search results always give lots of irrelevant data which often misled to actual needed information. One solution of this problem is semantic web which was proposed by Sir Tim Berner’s Lee. Semantic web is an extended version of World Wide Web that has opened the new doors for efficient information retrieval process. Ontology is one of the methodologies to implement the semantic web. In this paper govt exam ontology is created with the help of protégé ontology creation tool. In the given ontology various relationship between different govt exams of central and state level is provided and also the general information about these exams like exam type, subject level of exam and institutes for coaching is given. This domain specific ontology can help the potential information seeker to get the accurate information which can help to crack the exam.

Design of Relation Extraction Framework to Develop Knowledge Base

Background: Web documents display information in the form of natural language text which is not understandable by machines. To search specific information from sea of web documents has become very challenging as it shows many unwanted non relevant documents along with relevant documents. To retrieve relevant information semantic knowledge can be stored in the domain specific ontology which helps in understanding user’s need to retrieve relevant information. Methods: In this paper, framework for extracting and visualising semantic knowledge has been designed. Proposed approach is based on the assumption that semantics of text can be extracted by creating syntactic structure of the text. To extracts syntactic structure Stanford parser has been used. Parsing of corpus text is done to obtain morphological structures which is in more machine readable format, and thus provides a better structure for constructing syntactic-semantic rules manually. The tagged form of each sentence is taken and set of rules based on dependency relationship are build manually. Sentence level analysis is performed for concepts generation, for properties and for hierarchical relation extraction using dependency parse tree as a means for relation extraction. Results: Extracted concepts and relation among various entities constitute knowledge base in the form of ontology. Conclusion: Proposed information extraction model successfully filter the desired information from the large ocean of internet and create semantic structure to represent data in standard machine understandable format which explain the details about entities along with their properties and their relationship.

STO: Stroke Ontology for Accelerating Translational Stroke Research.

IntroductionOntology-based annotation of evidence, using disease-specific ontologies, can accelerate analysis and interpretation of the knowledge domain of diseases. Although many domain-specific disease ontologies have been developed so far, in the area of cardiovascular diseases, there is a lack of ontological representation of the disease knowledge domain of stroke.MethodsThe stroke ontology (STO) was created on the basis of the ontology development life cycle and was built using Protégé ontology editor in the ontology web language format. The ontology was evaluated in terms of structural and functional features, expert evaluation, and competency questions.ResultsThe stroke ontology covers a broad range of major biomedical and risk factor concepts. The majority of concepts are enriched by synonyms, definitions, and references. The ontology attempts to incorporate different users’ views on the stroke domain such as neuroscientists, molecular biologists, and clinicians. Evaluation of the ontology based on natural language processing showed a high precision (0.94), recall (0.80), and F-score (0.78) values, indicating that STO has an acceptable coverage of the stroke knowledge domain. Performance evaluation using competency questions designed by a clinician showed that the ontology can be used to answer expert questions in light of published evidence.ConclusionsThe stroke ontology is the first, multiple-view ontology in the domain of brain stroke that can be used as a tool for representation, formalization, and standardization of the heterogeneous data related to the stroke domain. Since this is a draft version of the ontology, the contribution of the stroke scientific community can help to improve the usability of the current version.

A Theoretical Approach For Developing A Semantic Analysis IIR Tool

An IIR (intelligent information retrieval) is a machine learning system used by users to retrieve the massive volume of information available through the internet. IIR offers personalization and efficiency in the current internet development allowing users to examine and acquire appropriate information. The paper will focus on the keyword searching issues of the current digital library system. The proposal is to choose a model that solves the issue using metadata case base and concept-based approach. The aim is to introduce a set of categories and concepts in the domain field that demonstrates some relations between them. As a result, poor quality of information retrieval in digital library system will be solved. The experiment has demonstrated that the already developed domain-specific ontology can be efficient for query advancement. Many researchers have used semantic retrieval technology using concepts to solve problems that lack semantics in traditional retrieval technology. Using concepts in ontology enhances search results.

Use of a domain-specific ontology to support automated document categorization at the concept level: Method development and evaluation

Voluminous, conveniently accessible textual documents, created and disseminated by modern information technology, makes automated document organization increasingly important for both individuals and organizations. Many existing techniques rely on document content analysis that classifies new, unlabeled documents by examining the similarity based on the overlap between their important features and the representative features of each document category. However, the performance of feature-based techniques can be significantly hindered by word mismatch and ambiguity problems. As a remedy, this study takes a concept-based approach and propose a text categorization method that incorporates a domain-specific ontology to support automated document categorization more effectively. The proposed method classifies documents according to their respective range of relevant concepts. We empirically evaluate our method versus several prevalent benchmarks that include feature-based k-nearest neighbors (kNN) and semantic-based techniques. The results show the proposed method more effective than the benchmark techniques; it achieves better performances when using a complete concept hierarchy without considering the hierarchical relationships among concepts. The proposed method illustrates how to incorporate a domain-specific ontology to improve document classification. Our method is computationally efficient because it produces a concept space of relatively few dimensionalities and does not require semantic space reconstruction as new documents arrive. Moreover, the relationships and patterns for classifying documents, generated by our method, are explicit and comprehensible.

APPROACH OF ENSURING INTEROPERABILITY OF MULTI-DIMENSIONAL DATA WAREHOUSES FOR MONITORING OF WATER RESOURCES

The realization of really working, on-line system for evaluation of pollution of water processes requires to represent the complexity of such phenomenon. The multi-layered structure of distributed information systems under different responsibilities identify some problems for developing of an adequate decision support system (DSS) working on-line. The aim of this research is to develop the interoperable infrastructure of multi-dimensional data warehouses working on-line for integration of monitoring data, enabling the conditions for more adequate decisions. The design approach includes the constructions of the knowledge base with some layers of representation, including the domain specific ontology, which are needful for evaluation of impacts of water pollution. The results are integrated with the structures of the ISMA database, EuroWaterNet, Water Resource Management Information System – WRMIS, following the requirements of EU Water Framework Directive (2004). The presented results on integration of information sources and collaboration workflows help in searching of suitable indicators for revealing the situations of water pollution from wastewater bodies and can help to retrieve the main objects which influence the monitoring of effluxes in the Baltic Sea.

Concept Based Intelligent Information Retrieval Within Digital Library

A digital library is a type of information retrieval (IR) system. The existing information retrieval methodologies generally have problems on keyword-searching. We proposed a model to solve the problem by using concept-based approach (ontology) and metadata case base. This model consists of identifying domain concepts in user’s query and applying expansion to them. The system aims at contributing to an improved relevance of results retrieved from digital libraries by proposing a conceptual query expansion for intelligent concept-based retrieval. We need to import the concept of ontology, making use of its advantage of abundant semantics and standard concept. Domain specific ontology can be used to improve information retrieval from traditional level based on keyword to the lay based on knowledge (or concept) and change the process of retrieval from traditional keyword matching to semantics matching. One approach is query expansion techniques using domain ontology and the other would be introducing a case based similarity measure for metadata information retrieval using Case Based Reasoning (CBR) approach. Results show improvements over classic method, query expansion using general purpose ontology and a number of other approaches.

Model for the classification of scheduling problems based on ontology

Abstract In this paper, we propose an ontology-based architecture for the classification of a scheduling problem. In fact, planning and scheduling is a major concern for the production enterprise as it contributes to its profit. Most of the time, researchers propose algorithms that relax some reel situations’ constraints; given that reel shop description are not taken directly from the floor supervisor, or given the complexity of the problem. The objective of ontology for the classification of scheduling problem is to build a bridge between practitioners and researchers in order to identify the reel classification of the problem. Thus, in this paper, we start by presenting a general model for the classification of scheduling problem. On this model, we build specific domain ontology. The specific domain ontology functions as a knowledge base and a data access point for the scheduling problem classification system.

Re-Ranking System with BERT for Biomedical Concept Normalization

In recent years, various neural network architectures have been successfully applied to natural language processing (NLP) tasks such as named entity normalization. Named entity normalization is a fundamental task for extracting information in free text, which aims to map entity mentions in a text to gold standard entities in a given domain-specific ontology; however, the normalization task in the biomedical domain is still challenging because of multiple synonyms, various acronyms, and numerous lexical variations. In this study, we regard the task of biomedical entity normalization as a ranking problem and propose an approach to rank normalized concepts. We additionally employ two factors that can notably affect the performance of normalization, such as task-specific pre-training (Task-PT) and calibration approach. Among five different biomedical benchmark corpora, our experimental results show that our proposed model achieved significant improvements over the previous methods and advanced the state-of-the-art performance for biomedical entity normalization, with up to 0.5% increase in accuracy and 1.2% increase in F-score.

A Layered Approach for Merging Application Ontologies: A Case Study from Agriculture Domain

Ontology is a machine interpretable way of representing knowledge in a precise and complete way favorable to solve many problems in the field of knowledge engineering. Different knowledge areas evolve with the time and the applications which use ontologies should be updated with new knowledge accordingly. It is more effective to combine the ontologies with new knowledge with the existing application ontologies rather than designing a new ontology from scratch. When combining ontologies, keeping the original usability of the initial ontologies and the heterogeneity of the components of the ontologies are the main obstacles. As there are no universal standard for naming of ontology components, this is a major reason for the heterogeneity problem. Methods for overcoming these problems are needed. In this research, we have proposed two algorithms to overcome the aforementioned problems. These algorithms for finding correspondences of ontology concepts and merging domain specific application ontologies keeping the original usability of the initial ontologies are the main outcomes of this research. The proposed algorithms are evaluated in terms of accuracy by comparing the resultant ontology merged using the proposed algorithm and the resultant ontology merged by an expert. The evaluation results prove that the proposed methodology merges the domain specific application ontologies very similar to the ontology merged by human intervention.