Resource Description Framework Model Research Articles

Using RDF Models to Create Knowledge Bases in the Kazakh Language: Comparison with Other Methods

Currently, there is a rapid development of information technologies, the amount of information on the Internet is growing very fast and it is becoming increasingly difficult to find the necessary information. A search using keywords does not give results adequate to the meaning of the information sought. Therefore, the creation of a technology for designing intelligent question answering systems in the Kazakh language based on the presentation, processing and extraction of knowledge is a very actual problem, since it is in such a system that the linguistic and semantic relationships between the texts of the request and the answer can be taken into account. This research paper focuses on the integration of the Resource Description Framework (RDF) model, a semantic web technology, and provides a detailed evaluation of data mining techniques in Kazakh. The paper examines many Kazakh language data collection methods such as online scraping, community collaboration and translation. It also explores the function of RDF models in organizing knowledge, connecting data points and adding semantic richness to datasets. The paper discusses linguistic features and challenges unique to the Kazakh language and emphasizes the need to address these challenges with domain-specific data. The need for thorough cleaning, annotation and data quality assurance is emphasized to guarantee the reliability and use of the collected datasets. Within global communications and technology, the study emphasizes the importance of languages other than English and examines how semantic web technologies can improve data representation and knowledge retrieval. The study lays the groundwork for future initiatives to address the shortage of datasets in languages with fewer resources and to create semantic web technologies for language diversity.

RDF(S) Store in Object-Relational Databases

The Resource Description Framework (RDF) and RDF Schema (RDFS) recommended by World Wide Web Consortium (W3C) provide a flexible model for semantically representing data on the web. With the widespread acceptance of RDF(S) (RDF and RDFS for short), a large number of RDF(S) is available. Databases play an important role in managing RDF(S). However, there are few studies on using object-relational databases to store RDF(S). In this paper, the authors propose the formal definitions of RDF(S) model and object-relational databases model. Then they introduce the approach for storing RDF(S) in object-relational databases based on the formal definitions. They implement a prototype system to demonstrate the feasibility of the approach and test the performance and semantic retention ability of this prototype system with the benchmark dataset.

LPG-Based Knowledge Graphs: A Survey, a Proposal and Current Trends

A significant part of the current research in the field of Artificial Intelligence is devoted to knowledge bases. New techniques and methodologies are emerging every day for the storage, maintenance and reasoning over knowledge bases. Recently, the most common way of representing knowledge bases is by means of graph structures. More specifically, according to the Semantic Web perspective, many knowledge sources are in the form of a graph adopting the Resource Description Framework model. At the same time, graphs have also started to gain momentum as a model for databases. Graph DBMSs, such as Neo4j, adopt the Labeled Property Graph model. Many works tried to merge these two perspectives. In this paper, we will overview different proposals aimed at combining these two aspects, especially focusing on possibility for them to add reasoning capabilities. In doing this, we will show current trends, issues and possible solutions. In this context, we will describe our proposal and its novelties with respect to the current state of the art, highlighting its current status, potential, the methodology, and our prospect.

A RDF model-based predictive model on dengue patients' diagnostic PDF reports

Resource description framework (RDF) models are increasingly used for storing data in the dengue healthcare domain on account of its explicit knowledge representation capability and ability of keeping provenance. This machine-interpretable graph-structured data model leads to new challenges for the knowledge engineering community to discover unobvious information leading to the necessity of designing RDF-model-based predictive model. The prerequisite is to extract diagnostic information from the patients' diagnostic reports available in portable document format (PDF) format in the hospital and generate RDF model facilitating machine interpretability. Hence, in this work, RDF model is generated as a prerequisite by extracting the clinical information available in the respective PDF reports of 118 dengue patients' using Docparser and RDF123 open source software. A classifier using artificial neural network (ANN) is designed on the top of the RDF model developed providing healthcare domain with an interoperable predictive model. The proposed classifier is evaluated using parameters such as accuracy, specificity, sensitivity (recall), precision, F1-score, etc. along with confidence interval and p-value. It offers a viable solution towards clinical decision making in semantic web-based dengue healthcare domain.

Updating Linked Data practices for FAIR Digital Object principles

Updating Linked Data practices for FAIR Digital Object principles

A Spatio-Temporal Resource Description Framework Schema Model for Aeronautical Dynamic Information Based on Semantic Analysis

Aeronautical information service (AIS) involves manifold correlations among aeronautical events. The data mining technology has been used to extract the characteristics of aeronautical information. With the aeronautical dynamic information of the notice to airmen (NOTAM) as the study case, this paper carries out semantic analysis on NOTAMs, and establishes a spatio-temporal resource description framework (RDF) schema model by combining a three-tuple RDF model and semantic analysis to extract features of aeronautical information. The new model is constructed by Protégé and NOTAM texts are employed to verify the model. Experiments showed that our proposed model could effectively match the samples of NOTAM information and extract the characteristic data from the NOTAM information. The study is expected to provide a basis for further aeronautical information mining based on knowledge graph.

A Fuzzy Query Method Oriented to Knowledge Graph

With the widespread application of Knowledge Graphs, Knowledge Graph query technology has received widespread attention. The daily use of natural language usually contains fuzzy terms. The use of fuzzy terms in queries is more in line with natural language habits. Therefore, fuzzy questions have been widely discussed and applied in relational data models, and their use in NoSQL data models has also begun to be affected in recent years. RDF (Resource Description Framework) is a representative model of the knowledge graph, and SPARQL is the standard query language of RDF. To realize the fuzzy query of the knowledge graph, this paper introduces the unclear expansion syntax of aggregate function based on summarizing and analyzing the existing SPARQL fuzzy expansion. Supplements the qualitative preference analysis method then proposes and SPARQL1. 1 Corresponding fuzzy SPARQL1.1 (f-SPARQL1.1) simultaneously provides strategies and techniques for rewriting ambiguous queries into classic SPARQL queries. In addition, this article also provides a visual interface for defining the membership functions of fuzzy terms and provides three interactive methods for setting fuzzy membership functions. Compared with the previous fuzzy query methods for RDF models, the method proposed in this paper has more vital fuzzy query ability and more flexible universality. The experimental results show the usability of the method proposed in this paper.

PG-explorer: Resource Description Framework data exploration with property graphs

The Resource Description Framework (RDF) has been widely applied to represent and exchange domain information because of its machine-readable characteristic. With a huge amount of RDF data available, retrieving RDF data is essential, so that many RDF query approaches have been developed. But many traditional approaches generally require users to know RDF model and query language, and this seriously prevents a large number of common non-expert users from obtaining information in RDF datasets. In this paper, we propose an approach that users can explore massive RDF datasets by interactively manipulating property graphs. Our approach provides users with a series of operations in interactively constructing property graphs to describe their query intents. To efficiently explore massive RDF datasets, we convert RDF data into property graphs for storage. This can greatly reduce the size of massive RDF datasets and more importantly the converted property graphs can instruct users to understand the underlying structure of RDF datasets, which is very useful in users’ construction of their query property graphs. The constructed query property graphs are finally transformed into expressions with the query language of graph databases. With high-performance graph databases as query engines, we developed an RDF data exploration system – PG-Explorer. Through experiments over real-world datasets, we evaluated the effectiveness and superiority of our approach. The user study demonstrates that the proposed approach simplifies users’ exploration of RDF data and can satisfy their exploration needs.

Constituent vs Dependency Parsing-Based RDF Model Generation from Dengue Patients’ Case Sheets

Electronic Health Record (EHR) systems in healthcare organisations are primarily maintained in isolation from each other that makes interoperability of unstructured(text) data stored in these EHR systems challenging in the healthcare domain. Similar information may be described using different terminologies by different applications that can be evaded by transforming the content into the Resource Description Framework (RDF) model that is interoperable amongst organisations. RDF requires a document’s contents to be translated into a repository of triplets (subject, predicate, object) known as RDF statements. Natural Language Processing (NLP) techniques can help get actionable insights from these text data and create triplets for RDF model generation. This paper discusses two NLP-based approaches to generate the RDF models from unstructured patients’ documents, namely dependency structure-based and constituent(phrase) structure-based parser. Models generated by both approaches are evaluated in two aspects: exhaustiveness of the represented knowledge and the model generation time. The precision measure is used to compute the models’ exhaustiveness in terms of the number of facts that are transformed into RDF representations.

Spatiotemporal RDF Data Query Based on Subgraph Matching

Resource Description Framework (RDF), as a standard metadata description framework proposed by the World Wide Web Consortium (W3C), is suitable for modeling and querying Web data. With the growing importance of RDF data in Web data management, there is an increasing need for modeling and querying RDF data. Previous approaches mainly focus on querying RDF. However, a large amount of RDF data have spatial and temporal features. Therefore, it is important to study spatiotemporal RDF data query approaches. In this paper, firstly, we formally define spatiotemporal RDF data, and construct a spatiotemporal RDF model st-RDF that is used to represent and manipulate spatiotemporal RDF data. Secondly, we present a spatiotemporal RDF query algorithm stQuery based on subgraph matching. This algorithm can quickly determine whether the query result is empty for queries whose temporal or spatial range exceeds a specific range by adopting a preliminary query filtering mechanism in the query process. Thirdly, we propose a sorting strategy that calculates the matching order of query nodes to speed up the subgraph matching. Finally, we conduct experiments in terms of effect and query efficiency. The experimental results show the performance advantages of our approach.

Fixing the inconsistencies in fuzzy spatiotemporal RDF graph

Since an increasing number of applications have urgent requirements for the management of fuzzy spatiotemporal information, it is challenging to model fuzzy spatiotemporal data. On the other hand, because Resource Description Framework (RDF) benefits greatly from web data, linked data, and knowledge graph, it has been widely accepted and has rapidly become a new choice to model and represent data in many application domains. The advent of RDF seems to provide an opportunity for dealing with fuzzy spatiotemporal data. However, RDF data is subject to continuously changing operations so that they may violate the integrity constraints and cause inconsistencies. In order to solve these problems, we propose an extended RDF model that can represent fuzzy spatiotemporal data, and then present the structure of fuzzy spatiotemporal data in RDF graph. After studying the constraint conditions of fuzzy spatiotemporal data in RDF graph, we propose the corresponding fixing rules and algorithms to detect and repair the fuzzy spatiotemporal RDF graph. Finally, the experimental results demonstrate the effectiveness of our approach which can significantly solve the inconsistency problem of fuzzy spatiotemporal RDF graphs.

Automatic construction of RDF with web tables

Being a metadata model, the Resource Description Framework (RDF) has been widely used in the context of semantic extraction, unified organization and intelligent processing of a large number of data because RDF’s machine understandability. Knowledge graph based on RDF model, for example, is predominantly generated from massive data with various models and formats. While many efforts have been carried out in converting databases and XML into RDF, the generation of RDF from Web tables currently receives less attention due to their flexible and complex structure as well as difficult understanding. Given that billions of Web tables are widely used in real-world applications, in this paper, we devote to the automatic construction of RDF with diverse Web tables. We recognize total six representative types of Web tables that have been extensively applied in Web-based applications, including Web tables with simple structure and complex structure. For each type of Web tables, we propose our approach for converting it into RDF. Our approaches can well identify the semantics of Web tables with complex structure and then generate the corresponding RDF. We implement an RDF generation system and experimental results show the feasibility of our proposed approaches.

RDF for temporal data management – a survey

Over the years, a large amount of temporal data needs to be shared and exchanged on the Web. Resource Description Framework (RDF) has been widely accepted and has rapidly gained popularity to al RDF) model as mentioned in represent and share data in many application domains (e.g., the Data of Web, Linked Data, and Knowledge Graph). Accordingly, efficient management of temporal data based on RDF is of increasing importance. Much work has been devoted to the representation, querying, storage, and management of temporal RDF data. Therefore, to investigate and more importantly serve as helping researchers grasp the main ideas and results of temporal RDF data management, and to highlight an ongoing research on them, in this paper, we provide a full up-to-date overview of the current state of the art in temporal RDF data management, including representation, querying, storage, and other directions. In detail, regarding to the representation of temporal RDF models, we first classify the existing temporal RDF models into two main categories according to their focuses (including the new RDF extension models and the original RDF models based on timestamp). We summarize and compare each model from the perspectives of syntax, semantics, and etc. Then, we further summarize the temporal RDF querying and storage techniques in detail. Moreover, to well introduce the main idea of each approach, we use some examples to explain each approach. In addition, the directions for future research and some comparisons and analyses are discussed in our whole survey. This survey will help readers understand and catch some key techniques about the issue and also identify some future research directions.

Semantic Scene Graph Generation Using RDF Model and Deep Learning

Over the last several years, in parallel with the general global advancement in mobile technology and a rise in social media network content consumption, multimedia content production and reproduction has increased exponentially. Therefore, enabled by the rapid recent advancements in deep learning technology, research on scene graph generation is being actively conducted to more efficiently search for and classify images desired by users within a large amount of content. This approach lets users accurately find images they are searching for by expressing meaningful information on image content as nodes and edges of a graph. In this study, we propose a scene graph generation method based on using the Resource Description Framework (RDF) model to clarify semantic relations. Furthermore, we also use convolutional neural network (CNN) and recurrent neural network (RNN) deep learning models to generate a scene graph expressed in a controlled vocabulary of the RDF model to understand the relations between image object tags. Finally, we experimentally demonstrate through testing that our proposed technique can express semantic content more effectively than existing approaches.

RDF Model Generation for Unstructured Dengue Patients' Clinical and Pathological Data

The automatic extraction of triplets from unstructured patient records and transforming them into resource description framework (RDF) models has remained a huge challenge so far, and would provide significant benefit to potential applications like knowledge discovery, machine interoperability, and ontology design in the health care domain. This article describes an approach that extracts semantics (triplets) from dengue patient case-sheets and clinical reports and transforms them into an RDF model. A Text2Ontology framework is used for extracting relations from text and was found to have limited capability. The TypedDependency parsing-based algorithm is designed for extracting RDF facts from patients' case-sheets and subsequent conversion into RDF models. A mapping-driven semantifying approach is also designed for mapping clinical details extracted from patients' reports to its corresponding triplet components and subsequent RDF model generations. The exhaustiveness of the RDF models generated are measured based on the number of axioms generated with respect to the facts available.

Mapping fuzzy RDF(S) into fuzzy object‐oriented databases

The Resource Description Framework (RDF) and RDF Schema (RDFS), being a recommendation of World Wide Web Consortium (W3C), have been widely used to exchange and reason information about resources on the web. It is a common case that information about resources may be uncertain under an environment of open web. To deal with uncertainties in resource information, we propose, in this paper, a general fuzzy RDF(S) model, which contains a fuzzy RDFS layer and a fuzzy RDF layer. In particular, we investigate how to formally map the fuzzy RDF(S) model to the fuzzy object-oriented database model in the paper. We develop mapping rules and implement a prototype system to demonstrate the feasibility of our approach.

Teaching Learning Based Optimization in Semantic Web of Distributed RDF

Semantic web data use as a unified data model in various areas, such as Bioinformatics, media data, Wikipedia, social networks, and government open data. Sharing information among people using semantic web helps to understand and manipulation of information. In the semantic web, the Resource Description Framework (RDF) denotes the linked data. The logical data is represented as RDF model to manage the unformatted data and it provides an ability to machine interpretability of data. The major problem on the web is to handle the large volume of the data that also has other challenges like query processing and optimization over widely distributed RDF data. In this research, the Teacher Learning based Optimization (TLBO) algorithm is proposed for the query optimization to reduce query cost, and optimize the computation time of the query. The TLBO technique select the suitable location and size of the population based on the data that effectively provide the solution for the distributed data i.e.., triple pattern of semantic web. The experimental result showed that the TLBO in query optimization performed well in the manner of query computation time compared to existing methods like MARVEL. Additionally, the results showed that the proposed TLBO model achieved nearly 4.93 seconds for executing the multiple queries in LUBM dataset.

Rethinking the University of Maryland Authority File for the Linked Data Environment

The development and implementation of a new digital collections system built on the Linked Data Platform has provided University of Maryland Libraries with an ideal opportunity to prototype and test ways to model local corporate name authorities in resource description framework (RDF). This includes assessing the local corporate names metadata, reconciling these names against existing authorities, and devising and executing an RDF model for unreconciled names in support of the new linked data environment.

Link key candidate extraction with relational concept analysis

Linked data aims at publishing data expressed in RDF (Resource Description Framework) at the scale of the worldwide web. These datasets interoperate by publishing links which identify individuals across heterogeneous datasets. Such links may be found by using a generalisation of keys in databases, called link keys, which apply across datasets. They specify the pairs of properties to compare for linking individuals belonging to different classes of the datasets. Here, we show how to recast the proposed link key extraction techniques for RDF datasets in the framework of formal concept analysis. We define a formal context, where objects are pairs of resources and attributes are pairs of properties, and show that formal concepts correspond to link key candidates. We extend this characterisation to the full RDF model including non functional properties and interdependent link keys. We show how to use relational concept analysis for dealing with cyclic dependencies across classes and hence link keys. Finally, we discuss an implementation of this framework.

Fuzzy data modeling and algebraic operations in RDF

The Resource Description Framework (RDF), a language proposed by the World Wide Web Consortium (W3C), is a flexible model for representing information about resources on the Web and has been applied in many application areas. At the same time, information is often imprecise and uncertain in many real-world applications. The existing works of RDF fall short in the ability of modeling imprecise and uncertain data in RDF. In this paper, we introduce some basic notions of fuzzy RDF graph model based on fuzzy sets. Along with the fuzzy RDF model, a fuzzy RDF algebra is formally proposed and a set of algebraic operations are developed. We also present some algebraic equivalences based on data graph isomorphism which can be used to rewrite algebra expressions in a form that satisfies certain needs. Finally, we show how to apply the proposed algebraic operations to capture the queries expressed in SPARQL query language. Based on the proposed data model and algebraic operations, we discuss the implementation issues.