A semantic model to publish open source software on the web of data

被引:0
|
作者
Mosharraf, Maedeh [1 ]
机构
[1] Shahid Beheshti Univ, Comp Sci & Engn, Tehran, Iran
关键词
Open source software; Semantic model; Web of data; Linked data; Ontology; Software model; Moodle; SELECTION; REUSE;
D O I
10.1108/AJIM-09-2021-0280
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Purpose The purpose of the paper is to propose a semantic model for describing open source software (OSS) in a machine-human understandable format. The model is extracted to support source code reusing and revising as the two primary targets of OSS through a systematic review of related documents. Design/methodology/approach Conducting a systematic review, all the software reusing criteria are identified and introduced to the web of data by an ontology for OSS (O4OSS). The software semantic model introduced in this paper explores OSS through triple expressions in which the O4OSS properties are predicates. Findings This model improves the quality of web data by describing software in a structured machine-human readable profile, which is linked to the related data that was previously published on the web. Evaluating the OSS semantic model is accomplished through comparing it with previous approaches, comparing the software structured metadata with profile index of software in some well-known repositories, calculating the software retrieval rank and surveying domain experts. Originality/value Considering context-specific information and authority levels, the proposed software model would be applicable to any open and close software. Using this model to publish software provides an infrastructure of connected meaningful data and helps developers overcome some specific challenges. By navigating software data, many questions which can be answered only through reading multiple documents can be automatically responded on the web of data.
引用
收藏
页码:685 / 707
页数:23
相关论文
共 50 条
  • [1] Semantic Web support for Open-source Software Development
    Dillon, Tharam S.
    Simmons, Gregory
    SITIS 2008: 4TH INTERNATIONAL CONFERENCE ON SIGNAL IMAGE TECHNOLOGY AND INTERNET BASED SYSTEMS, PROCEEDINGS, 2008, : 606 - +
  • [2] Publish Your Software: Introducing the Journal of Open Source Software (JOSS)
    Katz, Daniel S.
    Niemeyer, Kyle E.
    Smith, Arfon M.
    COMPUTING IN SCIENCE & ENGINEERING, 2018, 20 (03) : 84 - 88
  • [3] Romedi: An Open Data Source About French Drugs on the Semantic Web
    Cossin, Sebastien
    Lebrun, Luc
    Lobre, Gregory
    Loustau, Romain
    Jouhet, Vianney
    Griffier, Romain
    Mougin, Fleur
    Diallo, Gayo
    Thiessard, Frantz
    MEDINFO 2019: HEALTH AND WELLBEING E-NETWORKS FOR ALL, 2019, 264 : 79 - 82
  • [4] Web accessibility and open source software
    Obrenovic, Zeljko
    DISABILITY AND REHABILITATION-ASSISTIVE TECHNOLOGY, 2009, 4 (04) : 227 - 235
  • [5] Integrating Open Source Software Repositories on the Web through Linked Data
    Iqbal, Aftab
    Decker, Stefan
    2015 IEEE 16TH INTERNATIONAL CONFERENCE ON INFORMATION REUSE AND INTEGRATION, 2015, : 114 - 121
  • [6] Collecting Software Defect Data Automatically from Web Site of Open-Source Software
    Pei, Hanyu
    Ai, Jun
    PROCEEDINGS OF 2014 10TH INTERNATIONAL CONFERENCE ON RELIABILITY, MAINTAINABILITY AND SAFETY (ICRMS), VOLS I AND II, 2014, : 333 - 337
  • [7] Towards a Semantic Search Engine for Open Source Software
    Ben Sassi, Sihem
    SOFTWARE REUSE: BRIDGING WITH SOCIAL-AWARENESS, 2016, 9679 : 300 - 314
  • [8] Open Science data and the Semantic Web journal
    Hitzler, Pascal
    Janowicz, Krzysztof
    Shimizu, Cogan
    Zhou, Lu
    Eells, Andrew
    SEMANTIC WEB, 2021, 12 (03) : 401 - 402
  • [9] Using the Semantic Web as a Source of Training Data
    Bizer, Christian
    Primpeli, Anna
    Peeters, Ralph
    Datenbank-Spektrum, 2019, 19 (02) : 127 - 135
  • [10] Open Source Web Based Software for Random Assignment/Allocation Methods in Data Processing
    Arslan, A. Kadir
    Balikci Cicek, Ipek
    Colak, Cemil
    2019 INTERNATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE AND DATA PROCESSING (IDAP 2019), 2019,