With the advent of Linked Data the amount of automatically generated machine-readable data on the Web, often obtained by means of mapping relational data to RDF, has risen signi cantly. However, manually created, quality-assured and crowd-sourced data based on ontologies is not available in the quantities that would realise the full potential of the semantic Web. One of the barriers for semantic Web novices to create machine-readable data, is the lack of easy-to-use Web publishing tools that separate the schema modelling from the data creation. In this demonstration we present ActiveRaUL, a Web service that supports the automatic generation of Web formbased user interfaces from any input ontology. The resulting Web forms are unique in supporting users, inexperienced in semantic Web technologies, to create and maintain RDF data modelled according to an ontology. We report on a use case based on the Sensor Network Ontology that supports the viability of our approach.
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Demonstrating the ActiveRaUL Web service deployedSystem
hasDeployment Deployment
5 d e p l o y m e n t P r o c e s s P a tr d e ploy e d O n
Platform
Process Part
ystem S d attache hasSubsystem
To relieve a Web developer from manually de ning a Web form model according
to the RaUL ontology, we have extended the ActiveRaUL service with a deployment
endpoint that upon invocation generates RaUL Web forms from an arbitrary user
submitIgtneodreodInntAolgloorgithym.The biggest challenge in automatically creating such Web forms
from an ontology is the mismatch between the graph nature of RDF and the tree
structure of a Web form. In the algorithm implemented in ActiveRaUL we
distinguish six di erent types of sub-graphs occurring in ontologies and introduce decision
controls to map these sub-graphs to useable web forms. We will demonstrate these
di erent types of mapping on a use-case based on the the Semantic Sensor Network
(SSN) ontology that can be used to describe the capabilities of sensors, the
measurement processes used and the resultant observations. Figure 1 shows the \System"
class of the SSN ontology and its relations to other classes, whereas Figure 2 shows
a screenshot of a generated Web form by ActiveRaUL of the \System" class. The
numbers 1{6 in both gures indicate the six di erent types of sub-graphs we
distinguish in the algorithm (see Figure 1) and how they are displayed in the Web form
(see Figure 2).
We compared ActiveRaUL to the widely used state-of-the-art ontology editing tool,
WebProtege. The demonstration deployment of ActiveRaUL set up for the user study
already pre-loading the SSN ontology is available at: http://www.activeraul.org/
demo/index.html From the university deployment example de ned by the SSN
working group we extracted three test cases, each with a number of tasks. We asked
users to model these test cases in WebProtege and ActiveRaUL. For evaluating the
two systems we considered three usability metrics, (
Exp. Users Inexp. Users
All Users
Test Case 1 Test Case 2 Test Case 3 WebProtege ActiveRaUL WebProtege ActiveRaUL WebProtege ActiveRaUL 3:46 1:50 5:13 1:50 10:24 4:01 4:05 2:17 5:23 1:29 11:26 4:08 3:57 2:05 5:19 1:38 11:00 4:05 E ciency: Table 2 shows the average times participants required to complete a test case. Both participant groups, inexperienced and experienced, were signi cantly faster (between 27% and 56% faster) completing the test cases in ActiveRaUL compared to WebProtege.
Usability: After completion of the three test cases in both systems we asked the participants to rate their subjective reactions on the usability of the systems on a ve-point Likert scale as required by the SUS methodology. SUS yields a single number representing a composite measure of the usability of a system with scores in the range from 0 to 100, 100 being the best score. Overall ActiveRaUL scored 72.1 out of 100 points compared with 32.5 for WebProtege, indicating that the participants found ActiveRaUL easier to use than WebProtege for the creation of ontology instances.
Concluding, our user study proved that ActiveRaUL is indeed easier, more e ective and more e cient to use for the creation of RDF data than the state-of-the-art ontology editing tool.