Workshop - Ontology Engineering and Knowledge Graphs tool ecosystem

About the workshop

The OntoCommons project aims to use ontology-based technologies to support standardization and interoperability in the industry. As part of this, one of our aims is to review existing tools supporting the whole of the ontology lifecycle. The  "OntoCommons OE&KG tool ecosystem Workshop" will focus on tools used during critical ontology development projects and knowledge graph generation as well as presentations of end-to-end ontology and knowledge graphs developments.  

We invite you to join the workshop, by registering for free at the link below.

Draft Agenda (CET time)

• 11:00 - 11:15 Welcome
• 11:15 - 12:15 Ontology development tools - Part 1
  • Validating ontologies through requirements with Themis (Alba Fernández-Izquierdo - BASF)
    • The validation of ontologies, whose aim is to check whether an ontology matches the conceptualization it is meant to specify, is a key activity for guaranteeing the quality of ontologies by assuring, both the domain experts and ontology developers, that the ontologies they are building or using are complete regarding their needs. Inspired by software engineering testing processes, Themis validates ontologies by means of the application of test expressions which, following lexico syntactic patterns, represent the desired behaviour that an ontology will exhibit if a requirement is satisfied.
  • Ontology drafting in PURO (Vojtěch Svátek / Marek Dudáš - Prague University of Economics and Business)
    • A dilemma of the early drafting phase of ontological modeling is whether to start with OWL-conformant structures (albeit nicely wrapped in graphs or constrained ‘natural language’) from the onset, or using purely informal representation means such as ad hoc diagrams and free text. We hypothesize that the former approach may lead to pre-mature, arbitrary fixing of one of several possible modeling variants for the same reality, while the latter often leaves too much ambiguity in the model. We propose, as a partial solution to the dilemma, the PURO modeling language and an associated toolset (in particular, a graph-based editor named PURO Modeler, see https://protegeserver.cz/purom5). The key principles of PURO are as follows: 1) modeling is carried out through a link-node diagram, which primarily expresses the instance (example) level, and then the type level as secondary; 2) the modeling allows for arbitrary meta-typing, relationship arity and meta-relationships. To bridge the gap between such a relatively unconstrained representation and computable ontological models, transformation patterns can be subsequently applied. Via the patterns, the user can semi-automatically generate alternative skeletons of models in the target language. The supported target languages are currently OWL and OntoUML. With respect to OWL, the alternative transformation patterns can produce ontologies biased towards, e.g., modeling universals exclusively as classes (as common in biomedicine) vs. meta-modeling them with individuals (as common in some other families of knowledge graphs), towards using object properties vs. data properties (the latter being frequent in e-commerce schemas), or towards reifying all relationships including the binary ones.
• 12:15 - 12:30 << small break >>
• 12:30 - 13:30 Ontology development tools - Part 2
  • Reasonable Ontology Templates (OTTR) (Martin Georg Skjæveland - University of Oslo)
    • Reasonable Ontology Templates (OTTR) is a framework for pattern-based ontology engineering OTTR is designed to improve tasks for building, using, and maintaining knowledge basis in a systematic and scalable way.  It provides languages and tools with which RDF and OWL ontology modelling patterns and their instantiations may be expressed in a precise and intention revealing manner. The talk will introduce the basics of OTTR and discuss and demonstrate how OTTR's pattern-based approach may be used at different stages of a ontology's lifecycle. For more information on OTTR, visit http://ottr.xyz. 
  • Democratisation of Enterprise Knowledge Graphs (EKG) (Katariina Kari - IKEA)
    • When starting a Knowledge Graph project in an Enterprise, the biggest problem to solve is not reasoning nor is it necessarily the performance of the graph database. The biggest problem is selling the EKG to various stakeholders to start using it and/or contributing to it. In this talk Katariina Kari discusses the true gaps in KG technology development and the reality of developing EKGs.
• 13:30 - 14:45 << Lunch break >>
• 14:45 - 16:15 Ontology development end-to-end
  • Methodology and tools for ontology development in BASF (Alba Fernández-Izquierdo / Iker Esnaola - BASF)
    • To support ontology development and consumption across multiple teams, BASF have developed OMF (Ontology Management Framework). OMF integrates a set of tools to facilitate the evaluation, documentation and publication of ontologies, with the aim of helping users to develop FAIR ontologies that can be reused by anyone in the BASF community. Likewise, BASF has developed the OCT (Ontology Curation Tool) to ease and optimize the maintenance and update of ontologies for the agricultural domain.
  • OLS - The Ontology Lookup Service (Henriette Harmse - European Bioinformatics Institute | EMBL-EBI) 
    • OLS is a service that indexes ontologies in such a way to enable text based searches across ontologies and graph based traversal within ontologies. It provides a web frontend for user access and a REST API for programmatic access. In the biomedical community OLS is used extensively with the EMBL-EBI instance of OLS currently indexing 277 biomedical ontologies consisting of 7.2 million concepts, about 40000 properties and 500000 individuals. OLS is open source (https://github.com/EBISPOT/OLS), customisable (https://github.com/EBISPOT/OLS#customisation) and can be run using Docker (https://github.com/EBISPOT/OLS#deploying-with-docker). 
  • Building an Enterprise Ontology Management System for the Pharmaceutical Industry (Simon Jupp - SciBite)
    • SciBite offers a modern, API-based software stack for building a FAIR data organisation. At the core is CENtree: a user-friendly ontology management platform, which helps users adopt and deal with change in public standards and provides full governance and auditing of ontologies. We’ll present some of the challenges in building and managing ontologies at scale, across a large enterprise, and some of the consideration in the tooling that ultimately lead to the development of CENtree.  
• 16:15 - 16:30 << small break >>
• 16:30 - 17:30 Ontologies in use: Knowledge graph generation - Part 1
  • Declarative Knowledge Graph Construction: A practical introduction (David Chaves-Fraga - Universidad Politécnica de Madrid - KULeuven)
    • One of the most well-known uses for a Knowledge Graph is to integrate multiple and heterogeneous data sources into a common and unique layer. This task, known as Knowledge Graph Construction, has been usually treated as a pure scripting-engineering process where data engineers program ad-hoc software for transforming data into RDF. This procedure has a lot of drawbacks such as reproducibility, reusability, sustainability, or maintainability of the KG. On the contrary, mapping rules such as the W3C recommendation R2RML or its extensions (e.g., RML, xR2RML, etc.) give support to all these features while the generation of high-quality RDF data is also ensured. During this talk, an overview of current tools and mapping languages will be presented, and a basic example for creating a real KG using a python notebook.
  • Applying Ontotext GraphDB and Ontotext Refine for building industrial Knowledge Graphs (Miroslav Chervenski / Vladimir Alexiev - Ontotext)
    • Ontotext GraphDB is W3C standards compliant, platform-independent RDF triple store, that provides a high-availability cluster, enterprise-grade security, reasoning, and consistency check to its clients. Semantic Graph Databases are the foundation of Enterprise Knowledge Graphs, numerous industrial applications, and Knowledge Organization Management systems (thesaurus and ontology management systems), such as VocBench, SWC PoolParty, and Synaptica Semaphore or Metaphact Metapahctory. Ontotext Refine is a free application for automating the conversion of messy string data into a Knowledge Graph. The tool helps analyze the input data, applies various data cleaning and transformation algorithms, maps the string values into knowledge graph concepts, and imports the generated model into GraphDB. Ontotext GraphDB and Ontotext Refine are used in various commercial projects and research domains for building industrial Knowledge Graphs, such as: data marketplaces, business process management, enterprise data integration, master data management, engineering, smart cities, sensor networks, materials science, chemistry, geographic information, maritime data, construction and building information management, academic/research data, etc.
• 17:30 - 19:00 Ontologies in use: Knowledge graph generation - Part 2
  • MatVis - A Framework to Visually Represent Material Science Engineering Methods and RDF Knowledge Graph Creation (Andre Valdestilhas - BAM Bundesanstalt für Materialforschung)
    • An approach for Visual representation of Materials Science Methods and Knowledge Graph generation, which has become highly attractive for boosting research endeavors in the materials science and Semantic Web domain. The work is presented in a framework format to visually organize the Material Science Methods and generate an RDF Knowledge Graph to allow users to obtain essential information, such as provenance, and reproduce the experiments. The framework is built over free platforms, open-source and free software, such as diagrams.net and Chowlk.
  • Health application (Jaan Altosaar - Columbia University)
  • Open Semantic Lab - Bringing ontologies into everyday science (Simon Stier - Fraunhofer)
    • In science and technology, complex relationships exist between the properties of products and their composition and processing. For this reason, digital transformation and acceleration in this domain represents a particularly big challenge. Although it is generally agreed that data must be linked together by means of semantics and ontologies to form holistic data spaces, there is still a lack of suitable tools for integrating the necessary structures into the everyday work of scientists and engineers. Open Semantic Lab a holistic approach to address this deficiency: An interactive web platform that can capture all our data and ideas and link them precisely - no matter if it' s inventory (LIMS), lab notes (ELN), procedures (SOP) or general knowledge. Each piece of information can also be machine-read, blockchain-signed and written to automatically transfer (Lab 4.0) and analyze (AI) data. As an open (source) system, Open Semantic Lab can be easily adapted without in-depth programming knowledge and without losing the uniform structure. In this way, we can contribute to everyone's knowledge individually and yet in a standardized way.
• Closing

About Ontology Engineering and Knowledge Graphs tools 

OLS - The Ontology Lookup Service

OLS is a service that indexes ontologies in such a way to enable text based searches across ontologies and graph based traversal within ontologies. It provides a web frontend for user access and a REST API for programmatic access. In the biomedical community OLS is used extensively with the EMBL-EBI instance of OLS currently indexing 277 biomedical ontologies consisting of 7.2 million concepts, about 40000 properties and 500000 individuals. OLS is open source (https://github.com/EBISPOT/OLS), customisable (https://github.com/EBISPOT/OLS#customisation) and can be run using Docker (https://github.com/EBISPOT/OLS#deploying-with-docker). 

SciBite

SciBite offers a modern, API-based software stack for building a FAIR data organisation. At the core is CENtree: a user-friendly ontology management platform, which helps users adopt and deal with change in public standards and provides full governance and auditing of ontologies. We’ll present some of the challenges in building and managing ontologies at scale, across a large enterprise, and some of the consideration in the tooling that ultimately lead to the development of CENtree.  

Knowledge Graph Construction

One of the most well-known uses for a Knowledge Graph is to integrate multiple and heterogeneous data sources into a common and unique layer. This task, known as Knowledge Graph Construction, has been usually treated as a pure scripting-engineering process where data engineers program ad-hoc software for transforming data into RDF. This procedure has a lot of drawbacks such as reproducibility, reusability, sustainability, or maintainability of the KG. On the contrary, mapping rules such as the W3C recommendation R2RML or its extensions (e.g., RML, xR2RML, etc.) give support to all these features while the generation of high-quality RDF data is also ensured. During this talk, an overview of current tools and mapping languages will be presented, and a basic example for creating a real KG using a python notebook.

MatVis

An approach for Visual representation of Materials Science Methods and Knowledge Graph generation, which has become highly attractive for boosting research endeavors in the materials science and Semantic Web domain. The work is presented in a framework format to visually organize the Material Science Methods and generate an RDF Knowledge Graph to allow users to obtain essential information, such as provenance, and reproduce the experiments. The framework is built over free platforms, open-source and free software, such as diagrams.net and Chowlk.