Semantic Web Software

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Browse free open source Semantic Web software and projects below. Use the toggles on the left to filter open source Semantic Web software by OS, license, language, programming language, and project status.

  • Red Hat Ansible Automation Platform on Microsoft Azure Icon
    Red Hat Ansible Automation Platform on Microsoft Azure

    Red Hat Ansible Automation Platform on Azure allows you to quickly deploy, automate, and manage resources securely and at scale.

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    Top-Rated Free CRM Software

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  • 1
    Sesame

    Sesame

    Java RDF Framework

    This project is no longer actively maintained. It is succeeded by the Eclipse RDF4J project, which can be found at GitHub and at http://www.rdf4j.org/. Sesame is a de-facto standard framework for processing RDF data. This includes parsing, scalable storage, reasoning and full SPARQL 1.1 query/update support. Sesame offers a fully modular toolkit and an easy-to-use Java API that can be connected to all leading RDF storage solutions.
    Downloads: 3 This Week
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  • 2
    TRAK Metamodel

    TRAK Metamodel

    Tuples (triples) for TRAK architecture viewpoints and views

    The definition of the metamodel for TRAK (defines allowed AD elements and relationships i.e. tuples/ triples for the TRAK viewpoints and views). TRAK is a general systems-thinkers'/system engineering enterprise architecture framework. It is simple, user-friendly, pragmatic and not limited to IT. 100% triple-centric and semantically-sound. Forms basis for RDF + OWL ontology description - see https://trakmetamodel.sourceforge.io/vocab/TRAK_metamodel.html
    Downloads: 6 This Week
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  • 3
    SmartSlog (Smart Space Ontology) is a software/application development kit (SDK or ADK) for programming Smart-M3 agents (Knowledge Processors, KPs) that consume/produce smart space content according with its high-level ontological representation. SmartSlog applies the code generation approach: given an OWL ontology description, the ontology programming library is produced. The latter provides API to access the smart space via a Smart-M3 Semantic Information Broker (SIB) and data structures and functions to represent and maintain locally in KP code all ontology classes, relations, properties, and individuals. Since 2012 the project is supported by grant KA179 of Karelia ENPI programme, http://kareliaenpi.eu/ More information about project: http://oss.fruct.org/wiki/SmartSlog Smart-M3 on Wikipedia: http://en.wikipedia.org/wiki/Smart-M3
    Downloads: 2 This Week
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  • 4
    Smart-M3 is a functional platform that provides a cross domain search extent for triple based information. Smart-M3 enables smart cross domain applications that rely on information level interoperability.
    Downloads: 2 This Week
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  • Gain insights and build data-powered applications Icon
    Gain insights and build data-powered applications

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  • 5

    rdf2rdf

    This Java tool converts RDF data from any RDF format to any other.

    This Java tool converts your RDF files from any RDF format to any other format. It is based on the openRDF.org Sesame project and is wrapped into one single jar file for easy usage.
    Downloads: 2 This Week
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  • 6
    The CDM-Core ontology (CREMA Data Model - core module) is a manufacturing ontology, specialised in the two application sub-domains of 1) exhaust car manufacturing and 2) metallic press maintenance The CDM-Core ontology was developed by: Dr. Luca Mazzola, Msc. Patrick Kapahnke, Marko Vujic, and PD Dr. Matthias Klusch at the German Research Center for Artificial Intelligence DFKI GmbH (http://www.dfki.de ) in Saarbrücken, Germany. Copyright: DFKI, 2016, licence CC BY-SA see http://creativecommons.org/licenses/by-sa/3.0/ For bug reports, technical problems and feature requests please contact: Luca Mazzola: luca.mazzola@dfki.de or mazzola.luca@gmail.com Patrick Kapahnke: patrick.kapahnke@dfki.de
    Downloads: 1 This Week
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  • 7
    DODDLE-OWL

    DODDLE-OWL

    a Domain Ontology rapiD DeveLopment Environment – OWL extension

    DODDLE-OWL is a domain ontology development tool for the Semantic Web. DODDLE-OWL makes reuse of existing ontologies and supports the semi-automatic construction of taxonomic and other relationships in domain ontologies from documents.
    Downloads: 1 This Week
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  • 8
    Kinetic Simulation Algorithm Ontology
    The Kinetic Simulation Algorithm Ontology (KiSAO; http://co.mbine.org/standards/kisao) is an ontology of algorithms for simulating and analyzing biological models, as well as the characteristics of these algorithms, their input parameters, and their outputs. In addition, KiSAO captures relationships among algorithms, their parameters, and their outputs. Development of KiSAO has moved to https://github.com/SED-ML/KiSAO/.
    Downloads: 1 This Week
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  • 9
    OntoModel is a UML-based editor that works with OWL ontologies. The usage of the UML extension mechanism makes it possible to develope and maintain OWL ontologies with MDA technologies. Implemented as NeOn Toolkit (http://www.neon-toolkit.org) plugin.
    Downloads: 1 This Week
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  • Pimberly PIM - the leading enterprise Product Information Management platform. Icon
    Pimberly PIM - the leading enterprise Product Information Management platform.

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    Drive amazing product experiences with quality product data.
  • 10
    ROO is an (OWL) ontology construction tool based on Protégé 4. ROO makes the ontology building process easier as it provides guidance about the steps involved and it allows to enter knowledge using an easy to learn controlled natural language.
    Downloads: 1 This Week
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  • 11

    XML to RDF converter xml2rdf.xsl

    Generic XML to RDF converter

    This is a generic XML to RDF converter which uses XSLT transformations to convert any XML document into RDF format. The transformation uses an XSLT processor like xsltproc. The command line for the Bash shell is: xsltproc xml2rdf3.xsl document.xml > document.rdf Reference: Breitling, F. 2009: A standard transformation from XML to RDF via XSLT, Astronomical Notes, Vol 330 Issue 7, DOI: 10.1002/asna.200811233, http://onlinelibrary.wiley.com/doi/10.1002/asna.200811233/abstract http://adsabs.harvard.edu/abs/2009AN....330..755B https://arxiv.org/abs/0906.2291
    Downloads: 1 This Week
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  • 12
    This project is a reasoner for the description logic EL+. It computes the concept subsumption hierarchy. It is an OWL 2 EL reasoner.
    Downloads: 1 This Week
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  • 13
    ASI to Smart-M3 SIB synchronization agent
    Downloads: 0 This Week
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  • 14
    AnnoCultor: porting cultural repositories to the Semantic Web.
    Downloads: 0 This Week
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  • 15
    Arastreju is a Java based engine for ontology based information management.
    Downloads: 0 This Week
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  • 16

    BACnet Ontology

    An OWL ontology for BACnet

    This project is an effort to create an ontology for BACnet written in OWL. BACnet is an ASHRAE sponsored protocol for building automation and control networks. OWL is the ontology language for the Semantic Web.
    Downloads: 0 This Week
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  • 17
    Buscador semántico y ontológico: está compuesto de distintas partes programas en diferentes lenguajes: a. Ficheros PHP b. Ficheros en JavaScript (AJAX) c. Hojas de estilo CSS d. Necesidad de instalación de servidor APACHE y TomCat para su correcto funcionamiento. e. Descripción breve del funcionamiento: Mediante los ficheros PHP, con formularios que recogen la información a través variables POST y GET actúa en conjunto con los ficheros en JavaScript (AJAX) y envían la petición al servidor en SOLR.
    Downloads: 0 This Week
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  • 18
    A C# implementation of the Smart Space Access Protocol to access Smart-M3 (http://sourceforge.net/projects/smart-m3/). Hosted by the University of Bologna and under development within the ARTEMIS SOFIA project (http://www.sofia-project.eu/)
    Downloads: 0 This Week
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  • 19
    The Common Lisp Reasoner extends the Common Lisp Object System (CLOS) to incorporate a powerful rule language suitable for all kinds of reasoning tasks, vanilla XML and RDF/XML interfaces, and support for a variety of AI-related applications, such as scheduling, planning and diagnosis.
    Downloads: 0 This Week
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  • 20
    Conversor de ficheros en un formato de etiquetado origen a un formato de etiquetado destino: el parser que permite convertir ficheros de un formato a otro, está herramienta fue creada por la necesidad de convertir formatos en fichero RDF:DC a formato OWL para poder ser utilizado en Protègè. Posee un sistema de configuración de etiquetas origen y etiquetas destino, siendo posible la conversión de múltiples formatos diferentes. El programa está realizado en lenguaje JAVA.
    Downloads: 0 This Week
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  • 21
    DB2RDF is a a software tool that will convert data from relational data model to semantic data model (in the form of RDF and RDFS). A SPARQL endpoint for querying the converted data. For querying the semantic data, SPARQL query language is used.
    Downloads: 0 This Week
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  • 22
    DynamiCoS is a framework to support user-centric service composition. It uses semantic web techniques to describe and discover services, which then are used in compositions that aim at satisfying user objectives. The current version is still an alpha version. Further developments are to be released soon.
    Downloads: 0 This Week
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  • 23
    A lightweight IDE for Artificial Intelligence. Started as GUI for the Euler reasoning engine. The sources can be N3, RDF, OWL, UML, eCore, plain XML or XSD, files or URL's. Wraps Drools (or CWM, FuXi) as N3 rules engines. Model based app. generation.
    Downloads: 0 This Week
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  • 24
    A set of tools (command line and GUI) to provide a complete digital photo workflow for Unixes. EXIF headers are used as the central information repository, so users may change their software at any time without loosing any data.
    Downloads: 0 This Week
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  • 25
    Microsoft .NET connector for the Fedora Commons repository system. Developed by Jeroen Suijkerbuijk, commissioned and sponsored by Maastricht University Library.
    Downloads: 0 This Week
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Open Source Semantic Web Software Guide

Open source semantic web software is a type ofprogram which allows users to organize, store, and share data in an easy-to-understand format. This type of software makes use of what are known as semantics or meanings that describe how things and concepts relate to each other. Data stored with open source semantic web software is organized into relationships such as classes, properties, and instances which makes it easier for computers to understand the data.

One advantage of open source semantic web software is that users can create specific schemas, or sets of rules describing how all the data must be organized in order to make sense of it. This feature helps maintain consistency in the quality of the stored information regardless if different people are entering the data or if multiple databases are being used. Another benefit is that new information can easily be added without needing to change existing database structures making it easier for organizations to expand their database capabilities over time.

Additionally, open source semantic web software gives organizations more control over their data by allowing them to define any number of domain models for different purposes including but not limited to customer relations management (CRM), enterprise resource planning (ERP), finance operations and analytics systems, among others. This flexibility empowers users by allowing them tailor their databases according to their individual preferences while still keeping compatibility with industry standards like OWL and RDFa languages.

Finally, another great benefit provided by open source semantic web technology is its security features which protect all confidential user data from unauthorized access or manipulation. By using standard encryption protocols based on public/private key pairs between sender/recipient nodes any transmission over the network will remain secure at all times guaranteeing privacy for anyone who participates in this system.

Features Offered by Open Source Semantic Web Software

  • Resource Description Framework (RDF): RDF is a data model used to represent information as web-based resources. It uses triples, which are statements that consist of a subject, predicate, and object. This allows information to be linked together and accessed in a more organized manner.
  • SPARQL Protocol and RDF Query Language (SPARQL): SPARQL is the language used for querying data from an RDF database. The queries can be run over the web using commands like SELECT, ASK, CONSTRUCT, DESCRIBE and UPDATE. This makes it possible to access large amounts of semantic data quickly and easily.
  • Ontologies: Ontologies are used to describe entities within a certain domain or knowledge area by creating hierarchies of class structure and relationships between classes. This provides more structure for understanding how different types of data connect and helps automate tasks such as classification and reasoning.
  • Reasoners: Reasoners are programs that can process an ontology and make deductions based on the facts stated in it. They can also detect any inconsistencies in the ontology so they can be corrected before use.
  • Natural Language Processing (NLP): NLP tools analyze text written in natural languages like English or Spanish in order to extract structured information from it. For example, they might recognize named entities such as people or organizations mentioned within the text which could then be stored in an ontology or queried using SPARQL commands later on.

Different Types of Open Source Semantic Web Software

  • RDF/XML Parsers: These components of open source semantic web software allow for the conversion of data from one form to another, such as from HTML to XML, or from DBMS to XML.
  • Sparql Query Engines: These components enable Semantic Web developers to write queries in a language (typically SPARQL) that can match up content in distributed databases.
  • Ontology Development Tools: Ontology tools are used to create and maintain network graphs of related concepts, producing a visual representation of knowledge and relationships.
  • Natural Language Processing Libraries: NLP libraries enable developers to process natural language expressions; they also enable machines to interpret human language and generate responses with some degree of understanding.
  • Semantic Reasoning Engines: A reasoning engine helps users build and manage rules that give computers instructions regarding how to act upon certain conditions or events.
  • Knowledge Representation Software: This type of software helps store information in a structured way so it can be easily understood by people, machines, or both. It is especially useful for representing complex information about relationships between different entities like people or organizations.
  • Content Management Systems: Content management systems provide ways for managing digital assets including documents, images, audio and video files on a website or intranet environment while allowing easy collaboration between multiple parties. They also often integrate with other business systems such as those dealing with customer relationship management and enterprise resource planning solutions .

Advantages Provided by Open Source Semantic Web Software

  1. Cost Savings: Open source semantic web software is available for free or at a very low cost compared to proprietary software solutions. This can provide significant savings in both the short and long term, as there are no ongoing licensing fees or maintenance costs associated with open source software.
  2. Scalability: Semantic web technology offers flexibility and scalability that allows developers to quickly grow an application by adding new features and capabilities as needed. For organizations with rapidly changing requirements, the ability to easily scale up and down makes open source semantic web software an attractive option.
  3. Quality Assurance: Many open source projects go through rigorous code reviews, which helps ensure quality code releases. This can be especially beneficial for larger projects where multiple developers may need to access and maintain the same codebase.
  4. Collaboration: The collaborative nature of many open source communities means that you have access to thousands of experienced professionals who can help troubleshoot any issues you may face while developing your semantic web project. Additionally, individuals can contribute their own ideas to improve existing projects or even create entirely new ones.
  5. Security: Open source security measures such as strong encryption protocols are often used in order to protect sensitive data from malicious actors. Since these protocols are typically built into the software’s code base, they make it much more difficult for attackers to bypass them in order obtain private information stored on a server running this type of application.

Types of Users That Use Open Source Semantic Web Software

  • Developers: Use open source semantic web software to create applications, websites, and other programs. They are possible contributors to the software as well.
  • Researchers: Utilize open source semantic web software for research purposes, such as better understanding of data structures or extracting usable information from large datasets.
  • Students: Open source semantic web software is used by students in classes to study different aspects related to computing sciences. The practical exercises they run with this technology can help them develop a deeper understanding of its underlying structure and principles.
  • Hobbyists: People who use open source semantic web software as a passion project or just out of curiosity are hobbyists who want to explore new technologies without any commercial-oriented goals or ambitions.
  • Integrators: Organizations and companies looking for comprehensive solutions make use of open source semantic web software due to its scalability and flexibility in integration with existing systems and platforms.
  • Educators/Instructors: Instructors teaching computer science courses often introduce open source semantic web software into their curriculums to provide students with the opportunity of learning about new technologies in action rather than just theoretical explanations.

How Much Does Open Source Semantic Web Software Cost?

Open source semantic web software is completely free to use. There are no upfront costs associated with any of the open source semantic web software options available. This means you don't have to pay any licensing fees, or purchase a license in order to use the software. Furthermore, since open source software is developed collaboratively by communities of developers, anyone can make improvements and additions to the codebase which can be freely shared with others. As such, there is no need to invest time and resources into developing custom solutions for your particular application or platform. With open source semantic web software you get access to an ever-evolving set of tools that are constantly being optimized and improved without additional cost or effort on your part.

What Software Does Open Source Semantic Web Software Integrate With?

Open source semantic web software can be integrated with a variety of software, including content management systems (CMS), text editors, and search engines. CMS can be used to create and manage webpages, which simplifies the process of building and maintaining websites for those who do not want to write HTML code. Text editors can be used to modify existing texts or documents developed according to Semantic Web standards in an easier way than writing HTML code. Finally, search engines can utilize the data added into Semantic Web databases to develop more complex searches that lead users directly to more relevant results.

What Are the Trends Relating to Open Source Semantic Web Software?

  1. Increased accessibility: Open source semantic web software is becoming more accessible to a wider range of users due to its open source nature. This allows users to customize and modify the software to their needs and requirements, as well as providing a platform for collaboration between developers and users.
  2. Streamlined integration: Open source semantic web software makes it easier for developers to integrate various services, applications, and databases quickly and efficiently. This means that organizations can develop applications faster and with greater scalability.
  3. Enhanced scalability: As open source software continues to become more popular, scalability becomes an increasingly important factor in the development of applications. Scalability refers to the ability of a system to grow and adapt in response to increased demand or load. Open source solutions are highly scalable, allowing developers to create large-scale solutions without significant additional cost or complexity.
  4. Improved customization: Open source solutions allow users to make changes as needed, allowing them to tailor their solutions precisely according to their needs. This means that organizations can create customized solutions without having to invest heavily in developing from scratch.
  5. Greater interoperability: Open source solutions provide better interoperability between different components of the system, making it easier for developers to integrate various pieces into a cohesive whole. This improved interoperability leads to greater flexibility for organizations as they can easily switch components or add new ones without incurring extra costs or effort.

How Users Can Get Started With Open Source Semantic Web Software

Getting started with open source semantic web software is relatively straightforward. Many of the most popular applications are free to access, making it easy to get up and running quickly.

The first step is to find a good platform for hosting your application. You can choose from a variety of options, such as Sandbox or Heroku, or you can host your own server. Once you have chosen a platform, ensure that the necessary software packages are installed, including an RDF parser and query language implementation like Jena or Sesame.

Next, create an ontology for your app's data model by building its classes and relationships in an editor like Protégé. This will provide structure and organization for your application’s data so that it can be understood by other systems or applications. You should also define labels and comments for each class and relationship using natural-language terms that make sense in context; this makes data easier to understand by humans.

Once the ontology is built out, you will need to generate some instance data - sample objects that populate the ontology graph with real-world values - through either manual entry or automated scripts (such as SPARQL). Then you'll want to load this instance data into your chosen triple store solution (e.g., Apache Jena Fuseki). Finally, use one of many available libraries (such as Javascript/NodeJS) to write code which interact with underlying semantic web services in order to utilize their full power in your applications.