One of the many benefits of RDF graph databases is their structure. They overcome the flexibility limitations of relational databases, as data is stored as richly connected entities.
Datasets with many relationships between data points can be effectively stored, queried over, and now visualised in Version 4.0.0 of RDFox. This increased functionality is great for RDFox users, as they can now visualise, present and explore their data in a user-friendly way.
By visualising the data, relationships can be identified, investigated, and trends spotted, allowing for deeper analysis of the data. This article will showcase the new explorer feature.
The new explorer feature allows query results to be visualised. A query is a “request for data or information from a knowledge graph.” RDFox uses the standard SPARQL query language and can execute rapid data retrieval, even for heavily inter-connected data.
After the user has run a query, to visualise the results the user has two choices; they can hover over individuals, and explore the data from the item level (left image) or they can explore the full query results from the ‘Explore’ button on the top tool bar (right image).
For example, if the user explores from the individual level, the properties of individual results can be visualised. If the query is searching for full bodied wines, we can explore each wine’s properties (see AI Wine Sommelier article). Some properties are linked to numbers, others to objects (blank nodes: the small beige squares) that are themselves linked to numbers.
Alternatively, the user can use the explorer button at the top to view the full query results. For example, this image provides the full dataset from the Family demo, as seen in our Getting Started guide. To guard against the user attempting to display an unwieldy result set, the explorer restricts the size of the visualised subgraph to two hundred nodes. If a user encounters this restriction, they can refine the query and get more specific results.
From this view the user can explore all the query results and the relationships between the data. The explorer tool allows the data to be moved around on the canvas and provides other options, for example, the user can zoom in and out using the buttons on the tool bar. An overview of the graph, omitting node labels and edges to literals, is displayed when the user zooms far enough out. To see them again the user needs to simply zoom back in.
The explorer also provides layouts to help with data exploration. This includes a default, forced directed layout which lays things out intuitively, as well as Circle, Concentric and Breadth First layouts. These can be accessed using the layout button on the tool bar.
Where there are multiple links between entities, this is summarised by a number to ensure the data is presented concisely (left image). When hovered over, more information on the relationships can be viewed (right image). The explorer tool features other subtleties not represented in these static images, including the dynamic readjustment of the canvas to fit the browser and the visualised data.
The team at Oxford Semantic Technologies are extremely pleased to be able to offer their users this explorer feature.
Graph data is fundamentally about entities and their connections. While tabular representations of query results are undeniably the best way of presenting conclusions about data stored in graph form, the explorer tool will enhance the user’s ability to navigate the data itself by visualising these fundamental connections, to investigate and demonstrate its structure, and to aid in the formulation of new SPARQL queries.
I personally love graph visualisations. When query and rule bugs are being stubborn, I often use the visualisation to check that a graph’s structure is as I expected. This is particularly useful when integrating with existing graphs that I am unfamiliar with. In addition, developers that are new to linked data find it extremely useful (both for conceptual understanding and query creation) to see the structure of the data they are working with.
To try the data explorer, you can request a free one-month trial license here. For more information on Version 4.0.0. read our “What’s new in Version 4.0.0?” article. Alternatively, to learn more about RDFox, check out our blog!
The team behind Oxford Semantic Technologies started working on RDFox in 2011 at the Computer Science Department of the University of Oxford with the conviction that flexible and high-performance reasoning was a possibility for data-intensive applications without jeopardising the correctness of the results. RDFox is the first market-ready knowledge graph designed from the ground up with reasoning in mind. Oxford Semantic Technologies is a spin-out of the University of Oxford and is backed by leading investors including Samsung Venture Investment Corporation (SVIC), Oxford Sciences Enterprises (OSE) and Oxford University Innovation (OUI).