Querying Semantic Web Contents

Semantic mashup of biomedical data

Semantic Web for Health Care and Life Sciences: a review of the state of the art

Advent of internet made abundant of medical data available in various formats. Emerging technologies like AI, big data, machine learning and sematic web are increasingly applied in various sectors. Healthcare is the essential field which requires technological support in order to administer healthcare workforce, to improve the quality of healthcare services in resource constrained region, to manage the chronic diseases and pandemics like Covid-19 and so on. The supply of precise data to the technical system plays a significant role in providing better healthcare support than the available unorganized data. With the help of ontologies, semantic web represents the conceptual relationship between data more precisely. The accurate semantic rule and query engine provides effective definition of rules and knowledge retrieval from ontologies. In the medical field ontologies are used to represent medical terms, interoperability between systems with lexically different but semantically similar terms. It enhances the reuse and sharing of medical information and patient data. Also increases the percentage of support to integration of data and knowledge. The proposed case study considered Covid-19 ontology with 158 classes and maximum depth of 12 levels. Information retrieval from the knowledgebase is done using Simple Protocol and RDF Query Language (SPARQL), and Semantic Web Rule Language (SWRL). Simple queries can be formulated using Natural language based template. Additional knowledge can also be generated from the available information. This chapter ensures the effective utilization of information thereby enrich interoperability, integration and information retrieval.

Proteins are considered the central compound necessary for life, as they play a crucial role in governing several life processes by performing the most essential biological and chemical functions in every living cell. Understanding protein structures and functions will lead to a significant advance in life science and biology. Such knowledge is vital for various fields such as drug development and synthetic biofuels production. Most proteins have definite shapes that they fold into, which are the most stable state they can adopt. Due to the fact that the protein structure information provides important insight into its functions, many research efforts have been conducted to determine the protein 3-dimensional structure from its sequence. The experimental methods for protein 3-dimensional structure determination are often time-consuming, costly, and even not feasible for some proteins. Accordingly, recent research efforts focus more and more on computational approaches to predict protein 3-dimensional structures. Template-based modeling is considered one of the most accurate protein structure prediction methods. The success of template-based modeling relies on correctly identifying one or a few experimentally determined protein structures as structural templates that are likely to resemble the structure of the target sequence as well as accurately producing a sequence alignment that maps the residues in the target sequence to those in the template. In this work, we aim at improving the template-based protein structure modeling by enhancing the correctness of identifying the most appropriate templates and precisely aligning the target and template sequences. Firstly, we investigate employing inter-residue contact score to measure the favorability of a target sequence fitting in the folding topology of a certain template. Secondly, we design a multi-objective alignment algorithm extending the famous Needleman-Wunsch algorithm to obtain a complete set of alignments yielding Pareto optimality. Then, we use protein sequence and structural information as objectives and generate the complete Pareto optimal front of alignments between target sequence and template. The alignments obtained enable one to analyze the trade-offs between the potentially conflicting objectives. These approaches lead to accuracy enhancement in template-based protein structure modeling.

In this paper, we proposed a semantic integration framework of heterogeneous data based on domain ontology construction. The mechanism of domain ontology construction and mapping for heterogeneous data are studied, the purposes of which are to enhance the capability of dynamic adaptability and optimization of domain ontology construction, to resolve the problems of data heterogeneity in the processing of semantic information integration, and to promote the flexibility of the semantic integration process. Next, as to the semantic query, the theory and methods of specification or dynamic expansion of semantic query based on social annotation and ontology, and the duplicate removal and aggregating optimization of semantic query results are explored, the purpose of which is to realize the usability of semantic integration system and the credibility of the query results. Finally, an experimental prototype system of semantic integration of oil and gas exploration data based on domain ontology construction is constructed, the purpose of which is to verify the feasibility and correctness of the proposed theories and methods.KeywordsSemantic integrationOntology constructionDomain ontology

The role of the Semantic Web in Web 3.0, the next evolution of the internet, is significant. Web 3.0 is often referred to as the ‘intelligent web’ or the ‘Semantic Web’. The Semantic Web is an essential topic in Web 3.0 because it enables data integration, enhances data search on the World Wide Web, and promotes the development of knowledge graphs that allow computers to derive new insights and generate knowledge. Teaching the Semantic Web to undergraduate students offers numerous benefits. Students gain a deep understanding of cutting-edge web technologies, technical skills in data integration (the ability to develop systems with diverse datasets and linked data), and capabilities in information retrieval and knowledge management (data modelling and linked data querying). Moreover, they are able to design and develop different types of web-based applications such as intelligent data-driven decision-making apps, AI apps, or personalised-recommendation apps. In this article, we present ways to effectively integrate the Semantic Web as a subject in the current Computing Systems curriculum offering at Eastern Institute of Technology (EIT). We also propose a recommended list of theory topics, practical labs and practical projects that should be designed and developed to teach this paper effectively for undergraduate students.

Semantic Web technologies play an increasingly important role in the integration of heterogeneous biomedical information as well as the generation of meaningful links between biological and clinical information resources, which are at the basis of both, translational and personalized, medicine. A critical component of the emergence of these technologies is the need to represent domain knowledge, e.g. biological and medical knowledge, in the systems that we build in order to facilitate not just the low level integration of resources, but to enable knowledge-driven analysis and reasoning across them. As is true for many other scientific disciplines, in spite of the potential benefits to be gained, we are still far from seeing a widespread adoption of the Semantic Web for the Life Sciences. Part of the problem is of course the large investment of effort needed to develop sufficiently formal representations of enough Life Science knowledge in the form of ontologies and other knowledge bases, but there are also issues with the maturity of the tools and applications that are currently available to those who want to develop and use Semantic Web applications and tools in the Life Sciences. The purpose of the SWAT4LS workshops http://www.swat4ls.org is to create a space for discussions involving experts from the Computing as well as the Life Sciences, filling the gap between the possibilities and the practicalities of the Semantic Web in the Life Sciences domain. The first of these took place in Edinburgh on 28 November 2008 and at its heart was the question as to what is hindering a more rapid adoption of Semantic Web technologies in this domain. To reflect this, the panel discussion at the Edinburgh workshop was held under the heading of If the Semantic Web is so good, why is everybody using OBO for ontologies and PERL for data integration?. The original call for the SWAT4LS 2008 workshop resulted in the total of 40 submissions which were peer reviewed: 8 were accepted as full papers, 4 as poster papers and one as a demo paper. In addition, a number of posters were accepted. The workshop took place at the e-Science Institute in Edinburgh and was attended by about 80 participants. Following the actual workshop, all contributors to SWAT4LS'08 (papers as well as posters) were invited to submit extended versions of their contributions, resulting in the submission of 21 papers, which underwent another complete peer review cycle. Of these, 13 papers were selected and are included in this BMC Bioinformatics supplement. We are delighted with the collection of high quality papers that are the result of this endeavor and are confident that the readers will find them helpful in their own work on Semantic Web technologies for the Life Sciences. We would like to thank all who have contributed to the SWAT4LS 2008 workshop and this BMC Bioinformatics supplement. Specifically, we thank all the authors for their contributions, all the reviewers who served on the program committee for the original workshop call and then again for the submissions for this supplement, the invited speakers (Prof. Michael Krauthammer, Dr. M. Scott Marshall and Prof. Mark D. Wilkinson) and all the participants who made the workshop a memorable event. The support from the e-Science Institute in Edinburgh, which has hosted the workshop and covered some of the costs for our invited speakers, is also gratefully acknowledged. We also thank our sponsors: OMII-UK, LabAge SA, Leaf Bioscience and Textensor Limited for their support., Finally, we thank the staff at BMC Bioinformatics, specifically Ms. Isobel Peters, for their help with editing and processing this supplement. Albert Burger, Paolo Romano, Adrian Paschke and Andrea Splendiani (SWAT4LS Supplement Editors and Co-chairs of SWAT4LS 2008)

In the Semantic Web, items are represented by uniform resource identifiers (URIs) on the Web in order to describe relationships between things in resource description framework (RDF) graphs. Large scale open RDF data (linked data) describing information about various things are available. Hence, relational discovery research has been conducted using RDF data to explore relationships between things. The SPARQL protocol and RDF query language (SPARQL) is usually employed to process RDF data; however, SPARQL is not suitable for relational discovery using linked data because it is difficult to describe unknown vocabularies and relationships in SPARQL queries. In this paper, we propose an aggregation path search for RDF graphs as an extension of shortest path keyword search. The aggregation path search is devised to find RDF paths by merging multiple resource URIs into commonly reachable nodes in an RDF graph. This search method can discover new relationships between items that are connected by a common node. We present a method for treating equivalent resource URIs as a single resource URI; these equivalent URIs essentially represent the same thing based on equivalence relations in different RDF datasets. In our experiments, we show in the integration of multiple large scale RDF datasets that the integration of equivalent resources enables richer retrieval of aggregated paths.

Chapter 10 Ontology-based Multi-source Data Integration for Digital Soil Mapping

Data cubes for OLAP (On‐Line Analytical Processing) often need to be constructed from data located in several distributed and autonomous information sources. Such a data integration process is challenging due to semantic, syntactic, and structural heterogeneity among the data. While XML (extensible markup language) is the de facto standard for data exchange, the three types of heterogeneity remain. Moreover, popular path‐oriented XML query languages, such as XQuery, require the user to know in much detail the structure of the documents to be processed and are, thus, effectively impractical in many real‐world data integration tasks. Several Lowest Common Ancestor (LCA)‐based XML query evaluation strategies have recently been introduced to provide a more structure‐independent way to access XML documents. We shall, however, show that this approach leads in the context of certain—not uncommon—types of XML documents to undesirable results. This article introduces a novel high‐level data extraction primitive that utilizes the purpose‐built Smallest Possible Context (SPC) query evaluation strategy. We demonstrate, through a system prototype for OLAP data cube construction and a sample application in informetrics, that our approach has real advantages in data integration.

In this paper we explore some of the opportunities and challenges for machine learning on the Semantic Web. The Semantic Web provides standardized formats for the representation of both data and ontological background knowledge. Semantic Web standards are used to describe meta data but also have great potential as a general data format for data communication and data integration. Within a broad range of possible applications machine learning will play an increasingly important role: Machine learning solutions have been developed to support the management of ontologies, for the semi-automatic annotation of unstructured data, and to integrate semantic information into web mining. Machine learning will increasingly be employed to analyze distributed data sources described in Semantic Web formats and to support approximate Semantic Web reasoning and querying. In this paper we discuss existing and future applications of machine learning on the Semantic Web with a strong focus on learning algorithms that are suitable for the relational character of the Semantic Web's data structure. We discuss some of the particular aspects of learning that we expect will be of relevance for the Semantic Web such as scalability, missing and contradicting data, and the potential to integrate ontological background knowledge. In addition we review some of the work on the learning of ontologies and on the population of ontologies, mostly in the context of textual data.

Since archaeology in particular and humanities in general are interdisciplinary disciplines, there is an imperious need to enhance the accessibility and harmonise data integration, given their varied resource types (books, archives, scientific data, GIS, 3D models etc) and their different conceptual and technological structures and standards. A factor that further reduces accessibility and query performances is related to storing, such as physical location of data, language, and interface. The advent of Semantic Web technology represents an important advance in creating networks of archaeological knowledge based on various resources available on-line. While the valuable use of CIDOC-CRM (Comite International pour la Documentation -Conceptual Reference Model) ontology for specific CH (Cultural Heritage) domains (e.g. museums, libraries, etc) has been partially demonstrated, the interdisciplinary implementations are limited. In this article, we explore the potential of the CIDOC-CRM for a cross-domain implementation of CH data integration. We conducted tha mappings of different on-line resources related to Stonehenge to CIDOC-CRM, and evaluated them in the MAD database, a web-based application that manages natively XML-based datasets. The result ensures the validity of mapping mechanism and the semantic integration of CH data from different sources.

<span lang="EN-US">Data integration enables combining data from various data sources in a standard format. Internet of things (IoT) applications use ontology approaches to provide a machine-understandable conceptualization of a domain. We propose a unified ontology schema approach to solve all IoT integration problems at once. The data unification layer maps data from different formats to data patterns based on the unified ontology model. This paper proposes a middleware consisting of an ontology-based approach that collects data from different devices. IoT middleware requires an additional semantic layer for cloud-based IoT platforms to build a schema for data generated from diverse sources. We tested the proposed model on real data consisting of approximately 160,000 readings from various sources in different formats like CSV, JSON, raw data, and XML. The data were collected through the file transfer protocol (FTP) and generated 960,000 resource description framework (RDF) triples. We evaluated the proposed approach by running different queries on different machines on SPARQL protocol and RDF query language (SPARQL) endpoints to check query processing time, validation of integration, and performance of the unified ontology model. The average response time for query execution on generated RDF triples on the three servers were approximately 0.144 seconds, 0.070 seconds, 0.062 seconds, respectively.</span>

Availability of publications data is significant in research development and a global publications data in semantic web will help research community in great manner. W3C has provided a semantic web standard termed as RDB to RDF Mapping Language(R2RML). R2RML allows us to express mappings to be customized from relational database to RDF database. This paper discusses a convergence approach-PubWorld using R2RML that generate r2rml mapping files from three disparate relational databases- two for publication and one for world database. Publications data are made shareable directly from mapping file by converting into local ontologies and merging the local ontologies along with one existing ontology into one global ontology. The Header-Dictionary-Triples(HDT) compression technique is used for storing the global ontology to achieve large spatial savings. Simple Protocol and RDF Query Language(SPARQL) queries using Jena ARQ(And RDF Query) on both RDF and HDT version shows similar running time.

The exponential growth of information on the web far exceeds the capacity of present day information retrieval systems and search engines, making information integration on the web difficult. In order to overcome this, semantic web technologies were proposed by the World Wide Web Consortium (W3C) to achieve a higher degree of automation and precision in information retrieval systems. Semantic web, with its promise to deliver machine understanding to the traditional web, has attracted a significant amount of research from academia as well as from industries. Semantic web is an extension of the current web in which data can be shared and reused across the internet. RDF and ontology are two essential components of the semantic web architecture which support a common framework for data storage and representation of data semantics, respectively. Ontologies being the backbone of semantic web applications, it is more relevant to study various approaches in their application, usage, and integration into web services. In this article, an effort has been made to review the research work being undertaken in the area of design and development of ontology supported information systems. This paper also briefly explains the emerging semantic web technologies and standards.