scholarly journals Linking Web Resources in Web of Data to Encyclopedic Knowledge Base

2020 ◽  
Vol 10 (1) ◽  
pp. 357-368
Author(s):  
Farzam Matinfar
Keyword(s):  
Knowledge Base ◽  
High Performance ◽  
Web Resources ◽  
Additional Information ◽  
Web Resource ◽  
The Core ◽  
Knowledge Based ◽  
Database Evaluation ◽  
Web Of Data ◽  
The Web

AbstractThis paper introduces Wikipedia as an extensive knowledge base which provides additional information about a great number of web resources in the semantic web, and shows how RDF web resources in the web of data can be linked to this encyclopedia. Given an input web resource, the designed system identifies the topic of the web resource and links it to the corresponding Wikipedia article. To perform this task, we use the core labeling properties in web of data to specify the candidate Wikipedia articles for a web resource. Finally, a knowledge based approach is used to identify the most appropriate article in Wikipedia database. Evaluation of the system shows the high performance of the designed system.

OPGEN: A System to Identify and Evaluate Potential Applications of Knowledge Based Systems

1992 ◽  
Author(s):  
Samir Rohatgi ◽  
James H. Oliver ◽  
Stuart S. Chen
Keyword(s):  
Knowledge Acquisition ◽  
Knowledge Base ◽  
Knowledge Based Systems ◽  
The Core ◽  
Knowledge Based ◽  
Evaluation Functions ◽  
Potential Applications ◽  
Computer Based ◽  
Suitable System ◽  
Overall Effectiveness

Abstract This paper describes the development of OPGEN (Opportunity Generator), a computer based system to help identify areas where a knowledge based system (KBS) might be beneficial, and to evaluate whether a suitable system could be developed in that area. The core of the system is a knowledge base used to carry out the identification and evaluation functions. Ancillary functions serve to introduce and demonstrate KBS technology to enhance the overall effectiveness of the system. All aspects of the development, from knowledge acquisition through to testing are presented in this paper.

scholarly journals Knowledge Base Commons (KBCommons) v1.1: a universal framework for multi-omics data integration and biological discoveries

BMC Genomics ◽  
2019 ◽  
Vol 20 (S11) ◽  
Author(s):  
Shuai Zeng ◽  
Zhen Lyu ◽  
Siva Ratna Kumari Narisetti ◽  
Dong Xu ◽  
Trupti Joshi
Keyword(s):  
Knowledge Base ◽  
Data Storage ◽  
High Performance ◽  
Data Retrieval ◽  
Omics Data ◽  
Web Interface ◽  
Web Resource ◽  
Privilege Management ◽  
Multi Level ◽  
Comprehensive Framework

Abstract Background Knowledge Base Commons (KBCommons) v1.1 is a universal and all-inclusive web-based framework providing generic functionalities for storing, sharing, analyzing, exploring, integrating and visualizing multiple organisms’ genomics and integrative omics data. KBCommons is designed and developed to integrate diverse multi-level omics data and to support biological discoveries for all species via a common platform. Methods KBCommons has four modules including data storage, data processing, data accessing, and web interface for data management and retrieval. It provides a comprehensive framework for new plant-specific, animal-specific, virus-specific, bacteria-specific or human disease-specific knowledge base (KB) creation, for adding new genome versions and additional multi-omics data to existing KBs, and for exploring existing datasets within current KBs. Results KBCommons has an array of tools for data visualization and data analytics such as multiple gene/metabolite search, gene family/Pfam/Panther function annotation search, miRNA/metabolite/trait/SNP search, differential gene expression analysis, and bulk data download capacity. It contains a highly reliable data privilege management system to make users’ data publicly available easily and to share private or pre-publication data with members in their collaborative groups safely and securely. It allows users to conduct data analysis using our in-house developed workflow functionalities that are linked to XSEDE high performance computing resources. Using KBCommons’ intuitive web interface, users can easily retrieve genomic data, multi-omics data and analysis results from workflow according to their requirements and interests. Conclusions KBCommons addresses the needs of many diverse research communities to have a comprehensive multi-level OMICS web resource for data retrieval, sharing, analysis and visualization. KBCommons can be publicly accessed through a dedicated link for all organisms at http://kbcommons.org/.

scholarly journals МЕТОДИКА АВТОМАТИЗОВАНОГО ОЦІНЮВАННЯ РЕЗУЛЬТАТІВ ВИКОНАННЯ ЛАБОРАТОРНИХ РОБІТ З ПРИКЛАДНОЇ МАТЕМАТИКИ ЗДОБУВАЧАМИ ВИЩОЇ ОСВІТИ

2018 ◽  
Vol 66 (4) ◽  
pp. 162
Author(s):  
Victor V. Oliynyk ◽  
Oleksandr М. Samoilenko ◽  
Nataliia S. Ruchynska
Keyword(s):  
Higher Education ◽  
Academic Discipline ◽  
Educational Process ◽  
Laboratory Work ◽  
Automated Assessment ◽  
Knowledge And Skills ◽  
Web Resources ◽  
Web Resource ◽  
Automated Knowledge ◽  
The Web

In the context of reforming education in Ukraine, electronic systems for managing the learning process and web resources of educational disciplines as components of these systems are widely introduced into the educational process. The need to improve the quality of education and the effectiveness of monitoring the knowledge of applicants for higher education have led to an increase of interest in automated knowledge assessment. Laboratory classes play a leading role in developing skills and application of the acquired knowledge. The effectiveness of using computer technology in laboratory classes depends on the qualitative methodology for conducting them through the academic discipline web resource tools and the automated evaluation of learners’ knowledge and acquired skills. The paper justifies the relevance of automated assessment of students’ knowledge and skills for higher education, describes the methodology for conducting laboratory classes using the web resource tools of the academic discipline and the technology of automated knowledge and skills assessment based on the results of students’ laboratory assignments. The key idea of the proposed methodology is phased approach to the laboratory work implementation inside and outside the classroom through the academic discipline web resource tools, which includes the preparation manual and automatic access to the laboratory work, implementation and defence of laboratory work with the automated assessment of the corresponding reports. The technology of automated laboratory work assessment is understood as a technology of quality assessment in higher education. To determine whether the answer is correct the relevant algorithms have been incorporated into the web resources of the training course.

SPedia

2018 ◽  
pp. 1576-1596
Author(s):  
Muhammad Ahtisham Aslam ◽  
Naif Radi Aljohani
Keyword(s):  
Knowledge Base ◽  
Open Data ◽  
Data Publishing ◽  
High Quality ◽  
Scientific Publications ◽  
Sparql Endpoint ◽  
Web Of Data ◽  
Intelligent Searching ◽  
The Web

Producing the Linked Open Data (LOD) is getting potential to publish high-quality interlinked data. Publishing such data facilitates intelligent searching from the Web of data. In the context of scientific publications, data about millions of scientific documents published by hundreds and thousands of publishers is in silence as it is not published as open data and ultimately is not linked to other datasets. In this paper the authors present SPedia: a semantically enriched knowledge base of data about scientific documents. SPedia knowledge base provides information on more than nine million scientific documents, consisting of more than three hundred million RDF triples. These extracted datasets, allow users to put sophisticated queries by employing semantic Web techniques instead of relying on keyword-based searches. This paper also shows the quality of extracted data by performing sample queries through SPedia SPARQL Endpoint and analyzing results. Finally, the authors describe that how SPedia can serve as central hub for the cloud of LOD of scientific publications.

DISCOVERY OF RDFS:SEEALSO PATTERNS IN SEMANTIC WEB

2014 ◽  
Vol 28 (02) ◽  
pp. 1450003 ◽  
Author(s):  
FARZAM MATINFAR ◽  
MOHAMMAD ALI NEMATBAKHSH ◽  
GEORG LAUSEN
Keyword(s):  
Semantic Web ◽  
The Other ◽  
Web Based ◽  
Web Resources ◽  
Additional Information ◽  
Web Of Data ◽  
The Subject

The rdfs:seeAlso predicate plays an important role in linking web resources in semantic web. Based on the W3C definition, it shows that the object resource provide additional information about the subject resource. Since providing additional information can take various forms, the definition is generic. In the other words, the rdfs:seeAlso link can present different meanings to the users and it can represents different kind of patterns and relationships between web resources. These patterns are unknown and have to be specified to help organizations, and individuals to interlink, and publish their datasets on Web of Data using the rdfs:seeAlso link. In this paper, we investigate to the traditional usages of seealso and then present a methodology to specify the patterns of rdfs:seeAlso usages in Semantic Web. The results of our investigation show that the discovered patterns constitute a significant portion of rdfs:seeAlso usages in Web of Data.

scholarly journals A survey of large-scale reasoning on the Web of data

2018 ◽  
Vol 33 ◽  
Author(s):  
Grigoris Antoniou ◽  
Sotiris Batsakis ◽  
Raghava Mutharaju ◽  
Jeff Z. Pan ◽  
Guilin Qi ◽  
...  
Keyword(s):  
Systematic Review ◽  
Social Media ◽  
Parallel Algorithms ◽  
High Performance ◽  
Large Scale ◽  
Open Problems ◽  
Reasoning Systems ◽  
Web Of Data ◽  
Computational Properties ◽  
The Web

AbstractAs more and more data is being generated by sensor networks, social media and organizations, the Web interlinking this wealth of information becomes more complex. This is particularly true for the so-called Web of Data, in which data is semantically enriched and interlinked using ontologies. In this large and uncoordinated environment, reasoning can be used to check the consistency of the data and of associated ontologies, or to infer logical consequences which, in turn, can be used to obtain new insights from the data. However, reasoning approaches need to be scalable in order to enable reasoning over the entire Web of Data. To address this problem, several high-performance reasoning systems, which mainly implement distributed or parallel algorithms, have been proposed in the last few years. These systems differ significantly; for instance in terms of reasoning expressivity, computational properties such as completeness, or reasoning objectives. In order to provide a first complete overview of the field, this paper reports a systematic review of such scalable reasoning approaches over various ontological languages, reporting details about the methods and over the conducted experiments. We highlight the shortcomings of these approaches and discuss some of the open problems related to performing scalable reasoning.

scholarly journals Reasoning with Very Expressive Fuzzy Description Logics

2007 ◽  
Vol 30 ◽  
pp. 273-320 ◽  
Author(s):  
G. Stoilos ◽  
G. Stamou ◽  
J. Z. Pan ◽  
V. Tzouvaras ◽  
I. Horrocks
Keyword(s):  
Knowledge Representation ◽  
Knowledge Base ◽  
High Performance ◽  
Description Logics ◽  
Decision Procedures ◽  
Satisfiability Problem ◽  
Fuzzy Description Logics ◽  
Knowledge Based ◽  
Fuzzy Description

It is widely recognized today that the management of imprecision and vagueness will yield more intelligent and realistic knowledge-based applications. Description Logics (DLs) are a family of knowledge representation languages that have gained considerable attention the last decade, mainly due to their decidability and the existence of empirically high performance of reasoning algorithms. In this paper, we extend the well known fuzzy ALC DL to the fuzzy SHIN DL, which extends the fuzzy ALC DL with transitive role axioms (S), inverse roles (I), role hierarchies (H) and number restrictions (N). We illustrate why transitive role axioms are difficult to handle in the presence of fuzzy interpretations and how to handle them properly. Then we extend these results by adding role hierarchies and finally number restrictions. The main contributions of the paper are the decidability proof of the fuzzy DL languages fuzzy-SI and fuzzy-SHIN, as well as decision procedures for the knowledge base satisfiability problem of the fuzzy-SI and fuzzy-SHIN.

Approximating Numeric Role Fillers via Predictive Clustering Trees for Knowledge Base Enrichment in the Web of Data

2016 ◽  
pp. 101-117
Author(s):  
Giuseppe Rizzo ◽  
Claudia d’Amato ◽  
Nicola Fanizzi ◽  
Floriana Esposito
Keyword(s):  
Knowledge Base ◽  
Web Of Data ◽  
Predictive Clustering Trees ◽  
The Web

Data transforms, patterns and profiles for 21st century Cultural Heritage

2015 ◽  
Author(s):  
Uche Ogbuji ◽  
Mark Baker
Keyword(s):  
Cultural Heritage ◽  
Linked Data ◽  
High Performance ◽  
Large Scale ◽  
Public Libraries ◽  
Public Library ◽  
Web Pages ◽  
Dramatic Improvement ◽  
Web Resource ◽  
The Web

If you search for books and other media on the Web, you find Amazon, Wikipedia, and many other resources long before you see any libraries. This is a historical problem of librarians' having started ahead of the state of the art in database technologies, and yet unable to keep up with mainstream computing developments, including the Web. As a result, libraries are left with extraordinarily rich catalogs in formats which are unsuited to the Web, and which need a lot of work to adapt for the Web. A first step towards addressing this problem, BIBFRAME is a model developed for representing metadata from libraries and other cultural heritage institutions in linked data form. Libhub is a project building on BIBFRAME to convert traditional library formats, especially MARC/XML, to Web resource pages using BIBFRAME and other vocabulary frameworks. The technology used to implement Libhub transforms MARC/XML to a semi-structured, RDF-like metamodel called Versa, from which various outputs are possible, including data-rich Web pages. The authors developed a pipeline processing technology in Python in order to address the need for high performance and scalability as well as a prodigious degree of customization to accommodate a half century of variations and nuances in library cataloging conventions. The heart of this pipelining system is in the open-source project pybibframe, and the main way to customize the transform for non-technical librarians is a pattern microlanguage called marcpatterns.py. Using marcpatterns.py recipes specialized for the first Libhub participant, Denver Public Library, further specialized from common patterns among public libraries, (FIXME - not quite sure what is being said here) The first prerelease of linked data Web pages has already demonstrated the dramatic improvement in visibility for the library and quality, curated content for the Web, made possible through the adaptive, semistructured transform from notoriously abstruse library catalog formats. This paper discusses an unorthodox approach to structured and heuristics-based transformation from a large corpus of XML in a difficult format which doesn't well serve the richness of its content. It covers some of the pragmatic choices made by developers of the system who happen to be pioneering advocates of The Web, markup, and standards around these, but who had to subordinate purity to the urgent need to effect large-scale exposure of dark cultural heritage data in difficult circumstances for a small development and maintenance team. This is a case study of where proper knowledge of XML and its related standards must combine with agile techniques and "worse-is-better" concessions to solve a stubborn problem in extracting value from cultural heritage markup.

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