Exploring app-building in metaphactory with the metaphacts Nobel Prize assets bundle

Application Building

Dmitry Pavlov, Pauline Leoncio



Reading time: approx. 7 minutes

Exploring app-building in metaphactory with the metaphacts Nobel Prize assets bundle

App-building in metaphactory using Nobel Prize assets


In this blog post, we discuss the metaphacts Nobel Prize App, where you can explore information and gain deeper insights into the awards, prize winners and uncover any relations between them — all while experiencing the full range of app-building capabilities metaphactory has to offer.


How to build applications in metaphactory by exploring the metaphacts Nobel Prize assets bundle

The metaphacts Nobel Prize App enables you to interact with Nobel Prize data through an interactive, visual interface. A semantic model underpins the app to enrich the data with metadata, layers of context and expose relations within the data. The data is presented through a user-friendly interface for easy end-user interaction, thereby facilitating deeper exploration of the data available.


If you’re unsure of how metaphactory can support your use case or meet end-user needs and enterprise branding requirements, you can explore the existing demo or use the assets in our Nobel Prize bundle to reproduce the app in your own metaphactory instance for experimentation. Keep reading to learn more about how the app works and how you can extend it and test it for yourself!


Table of contents



What is the Nobel Prize App?

The Nobel Prize App is an application created in metaphactory that enables you to easily search, explore and analyze information about Nobel Prize laureates and the awards they have received through semantic search, filtering, enhanced front-end components and more. The dataset used is based on data provided by the Nobel Foundation, which includes a historical archive of the Nobel Prize awards and its winners and is regularly updated with the most recent data, in addition to public data integrated from Wikidata.


 Image: Nobel Prize App start page

The app’s components and semantic features enable you to interact with the data and uncover additional information about the prizes and their winners. The interlinking in the semantic model makes patterns and links explicit, which can be used to surface deeper insights and statistics from the existing data. For instance, you can answer questions like, "How many individuals born in Germany have been awarded the Nobel Prize?" by using filters to focus on entity types such as [Laureate] + [country]. By doing so, you would discover that there are 97 Nobel Prize Laureates from Germany. 


Image: Searching for Nobel Prize laureates from Germany


To enable these additional functionalities, we extended the ontology that was publicly released by the Nobel Foundation with a place hierarchy based on the patterns observed in the dataset and with an additional SKOS vocabulary to store the categories of Nobel prizes (e.g., Physics, Chemistry, etc.). These extensions enable you to uncover information beyond what is immediately available in the dataset. In our GitHub repository, you can find multiple assets to help you explore the Nobel Prize app and recreate it in your own metaphactory instance. 


  • The Nobel Prize bundle, which can be downloaded through the Amazon S3 bucket, consists of:
    • The original data dump nobel-prize-dataset.trig' - retrieved and cached from https://data.nobelprize.org/sparql
    • nobel-prize-dataset-place-hierachy-extension.trig - created as an extension by metaphacts taking nobel-prize-dataset.trig as input. The place hierarchy distinguishes between two levels (e.g., country vs. city) to attribute data levels like Spain or Madrid to the correct levels. It was constructed utilizing the knowledge within the data and with the help of federation to Wikidata/DBpedia.
    • nobel-prize-metaphacts-shacl-ontology.trig - extended by metaphacts with additional semantic model elements (vocabulary and place hierarchy) based on the original Nobel Prize ontology published by the Nobel Prize Foundation.
    • nobel-prize-metaphacts-skos-vocabulary.trig - created by metaphacts to handle the vocabulary terms within metaphactory. The identifiers of the "category" individuals from the official Nobel Prize OWL ontology have been reused and turned into a vocabulary (defined as SKOS scheme). The terms also have been augmented and enriched with additional structure and metadata solely for exploration and demonstration purpose.
  • Nobel Prize App
  • Nobel Prize Branding App


The dataset is hosted and available for download through the Amazon Simple Storage Service (Amazon S3) bucket, which allows us to use the SPARQL UPDATE LOAD command in metaphactory to load the Nobel Prize ontology, vocabulary, and instance data.

How to interact with the Nobel Prize dataset

There are a plethora of ways you can navigate and explore the Nobel Prize App. If you don’t know where to start, follow the steps below for an example of how you can interact with the Nobel Prize App: 


  1. Enter https://nobelprize.metaphacts.cloud into your browser. 

  2. The link will open to the Nobel Prize App start page which includes a search bar and two pre-loaded categories (Laureates and Nobel Prize, respectively) to offer you a starting point for your exploration. You’ll also see a simple Nobel Prize branding incorporated into the app. 

  3. Type ‘Albert Einstein’ in the search bar, the search bar will already display a suggested result. If you hit enter, you will see additional options for adjusting your search parameters. 

  4. Click on ‘Albert Einstein’ and it will open to a content page. This page includes a photo of Einstein, an overview, key facts, a biography, other notable works, etc. It also contains a map view of locations associated with Einstein, such as place of birth/death and other affiliations. 

  5. By integrating Wikidata data into the app, we can fetch additional information such as Notable Works, Other Awards, and Member Of (associations), that add more detail to the laureate’s profile. Utilizing Wikidata as an additional data source enriches the Nobel Prize dataset, enabling you to expose and search for links more effectively. For instance, leveraging the information available on Wikidata, you can designate Germany as a 'location' and directly link to its coordinates, which could be used in a map component.  

  6. There is also a timeline component that displays some important dates in his life, again his birth date, date of death and when Einstein was awarded the Nobel Prize. From this information, you can already glean insight into Einstein’s career, such as the fact that he was awarded the Nobel Prize around the middle of his life, around the age of 42 — slightly earlier in his career than the average age of a Nobel Prize winner. 

This is just one example of the many ways you can experiment with the Nobel Prize data through the Nobel Prize App and see how your data can be extended to provide more value and advanced analysis. Hopefully, it will also give you inspiration for what kinds of applications and pages you can create with metaphactory. 


Benefits of exploring linked data with the Nobel Prize App

We created the Nobel Prize App for several reasons, with one of the main reasons being that we wanted to provide the knowledge graph community with a simple way to experiment with linked data and to showcase how easy it would be to create their own knowledge graph-powered applications. 


Linked data is a method of interlinking data so that it can be easily surfaced, understood and shared across all platforms and systems. The method is based on semantic web principles, which envisions a World Wide Web where all web content is human- and machine-readable. A linked data approach enables you to leverage information that already exists online to enrich your data and facilitates knowledge discovery through the integration with diverse data sources, existing datasets and ontologies and vocabularies. It also promotes the scalability and sustainability of your data by ensuring that new data can be seamlessly integrated at any point. As a side note, linked data is sometimes used interchangeably with “semantic knowledge graph” because the semantic knowledge graph is the technological evolution of linked data that facilitates this practice. 


While the Nobel Prize dataset is already made available for public use, there hasn’t been a way to engage with the data in a visual, interactive manner. As mentioned above, the Nobel Prize App displays Nobel Prize data through a user experience that includes search functionality and enhanced visual components, enabling users to find information more efficiently and interact with the data in an intuitive and fun way. 


By creating the Nobel Prize App, we aim to demonstrate the ease of creating a sophisticated app in metaphactory and allow you to explore linked data firsthand. The user interface makes this information consumable for all end-users, no matter their level of technical expertise, thereby facilitating more seamless collaboration and potentially leading to significant discoveries and advanced insights.


We decided to use the Nobel Prize dataset specifically because it is a contained and complete dataset that’s graspable in size, meaning that it is easy to deploy, update and maintain and therefore simple for you to reproduce if you’d like to experiment with it yourself.


How the Nobel Prize App helps you understand low-code app building in metaphactory:

By exploring the assets in our Nobel Prize bundle, you’ll learn how to replicate this live demo in your own metaphactory instance and mature in your app-building skills. Since the concept of the Nobel Prize is widely known, it provides digestible and easy-to-understand information that most people will recognize.


The Nobel Prize App covers all essential pieces of the semantic model: an ontology, a vocabulary related to it, a dataset fully covered by ontology and vocabulary, as well as an app built on top. Not only does the bundle consist of the Nobel Prize data and instructional documents, but it also includes UI templates you can follow, reproduce and tweak before you start creating your own templates from scratch for your specific use case. 


In the Nobel Prize app templates, you can understand how metaphactory's front-end components, such as <semantic-table>, interact with the data through SPARQL queries. You can then tweak the queries and immediately see the results reflected in the UI. By going through the exercise of adjusting metaphactory’s components to the underlying data model with SPARQL queries, it’ll be much easier to take the next step - to load your own model and data and configure metaphactory to fulfill the use case you have in mind.

You’ll also be able to look under the hood and understand how to build the in-page navigation or how components can work together first-hand, such as with the timeline and map components. By experimenting with these components, you’ll learn how to build an app that offers a seamless and enriched user experience. 

How to extend the Nobel Prize app by introducing other components

  1. First, you must deploy a metaphactory instance via one of our free trial options (for example you can start a trial in Docker or AWS Marketplace. 

  2. Once you have a metaphactory instance, you will first see a blank page.

  3. Then, go to the Github repository to download the nobelprize-app and nobelprize-branding apps. 

  4. Deploy the Nobel Prize app and branding app by following the instructions laid out in this help article (which must be opened in a fresh metaphactory instance). 

  5. Load the Nobel Prize bundle from the S3 bucket by clicking the "SPARQL" link in the application header of your metaphactory instance and then pasting and running the following command: LOAD <https://metaphacts-datasets.s3.amazonaws.com/nobel-prize-bundle.trig.gz>

  6. Now that you have the Nobel Prize system fully set up, you can extend the app further by reading this blog post created in partnership with Amazon AWS and copying the tables and SPARQL queries available to show more data to the user and enhance user experience.


The Nobel Prize app is a live app demonstrating the types of applications you can build in metaphactory. With our Nobel Prize bundle, you gain access to assets that will aid in your experimentation and help you to reproduce the app in your own metaphactory instance, or use it as a blueprint for diving into building your own custom application. 

Try it for yourself!

metaphactory is an industry-leading enterprise knowledge graph platform transforming data into consumable, contextual and actionable knowledge. Our low-code, FAIR Data platform simplifies capturing and organizing domain expertise in explicit semantic models, extracting insights from your data and sharing knowledge across the enterprise.


metaphactory includes innovative features and tools for:


  • Semantic knowledge modeling — explicitly capture knowledge & domain expertise in a semantic model & manage knowledge graph assets such as ontologies, vocabularies and data catalogs
  • Low-code application building — build easy-to-configure applications that fit your enterprise and use-case requirements using a low-code, model-driven approach
  • End-user-oriented interaction — users of any level of technical experience can interact with your data through a user-friendly interface that includes semantic search, visualization, discovery & exploration and authoring

Power knowledge democratization and decision intelligence within your enterprise with metaphactory. Trusted by global enterprises like Boehringer Ingelheim, Siemens Energy and Bosch.


Explore the Nobel Prize App and start your metaphactory free trial to experiment with creating your own custom app.


Happy app-building!

Dmitry Pavlov

As Director of Customer Success at metaphacts, Dmitry is responsible for making sure that our clients have a positive and rewarding experience with metaphactory and he drives many customer-oriented product improvement initiatives to make this experience even smoother.

Pauline Leoncio

Pauline Leoncio is an experienced copywriter and content marketer with over 6+ years in marketing. She's developed content plans and creative marketing material for growing B2B and B2C tech companies and covers a range of topics including finance, advanced tech, semantic web, food, art & culture and more.