STI OC Result

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STI INNSBRUCK
EVALUATION OF SEMANTIC
ANNOTATIONS OF
TOURISMUSVERBAND
Corneliu Valentin Stanciu, Ioan Toma, Anna Fensel
STI Innsbruck, University of Innsbruck,
Technikerstraße 21a, 6020 Innsbruck, Austria
firstname.lastname@sti2.at
2014-03-11
Semantic Technology Institute Innsbruck
STI INNSBTRUCK
Technikerstraße 21a
A – 6020 Innsbruck
Austria
http://www.sti-innsbruck.

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Contents
1. Introduction....................................................................................................................................2
2. Evaluation ......................................................................................................................................2
2.1 Number of Visitors....................................................................................................................2
2.2 Search Engines..........................................................................................................................5
2.3 Landing Pages...........................................................................................................................6
3. Conclusion and Next Steps..............................................................................................................8

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1. Introduction
Structured data markup on web pages brings many benefits to a website in terms of visibility
enabling search engines to interpret content and, therefore, increasing the likelihood that the
website is included in the search results for a related query. Schema.org1, an approach supported
by main search engines such as Bing, Google, Yahoo!, Yandex, is the major initiative that webmasters
can use to markup their pages in ways recognized by major search providers. This document presents
the evaluation of the Tourismusverband Innsbruck (TVb) website, after annotating part of the
website’s content with Schema.org2 annotations.
The remainder of the document is organized as follows: Section 2 describes the evaluation in
terms of criteria and figures. Finally, Section 3 concludes the document and describes about next
steps.
2. Evaluation
This section provides the details involved in the process of evaluating the website. First, the
number of visitors will be presented for the periods before and after annotating the content. Then
comparisons between the
The date when the annotations were deployed on TVb’s website was Jan 20, 2014. For the data
analysis, a period of time of 40 days was chosen, before and after deployment.
2.1 Number of Visitors
For a selected period before the deployment, meaning December 11, 2013 until January 19, 2014, there
was a total of 249.510 visits, 67.7% of them being returning visitors and 32.3% new visitors, as also
shown in Figure 1 and 2.
Figure 1 - Total number of visitors between Dec 11, 2013 and Jan 19, 2014
1 http://schema.org/
2 http://schema.org/

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Figure 2 - New versus Returning Visitors
The second period which we analyzed is the period after deployment, meaning January 20, 2014 until
February 28, 2014. Figure 3 and 4 shows a total number of visitors of 201.242, 66% being returning
visitors and 34% new visitors.
Figure 3 - Total number of visitors between Jan 20, 2014 and Feb 28, 2014

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Figure 4 - New versus Returning Visitors
Comparing the two periods it results a decrease of 19.35% visitors for the period after deployment.
Taking into account that important events like Christmas, New Year,Christmas Market time, holiday, etc.
happened to be in the period before deployment, make the decrease not so relevant.
To order to ensure more stable and accurate results,we will need to have almost the same events in both
periods. Therefore, we decide to compare the same periods to time but in different years.
First we compare the period before the deployment of annotations (Dec 11, 2013 – Jan 19, 2014) with the
same period of the previous year (Dec 11, 2012 – Jan 19, 2013). As is shown in Figure 5, there is an
increase of 16.96% visitors, meaning that TVb was able to increase the number of visitors with 16.96%
without using semantic annotations.
Figure 5 – Comparison of visitors before deployment and the same period of previous year.
However, comparing also the period after deployment (Jan 20, 2014 – Feb 28, 2014) with the same period
of the previous year (Jan 20, 2013 – Feb 28, 2013), we can observe an increase of 25.59% visitors (see
Figure 6).

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Figure 6 - Comparison of visitors after deployment and the same period of previous year
If the increase of visitors without annotations was of 16.96%, the increase of visitors with annotations is
of 25.59%. Meaning that the difference of 8.63% on the number of visitors may be caused by annotating
the content.
2.2 Search Engines
Most users reach the desired resource destinations using search engines. Also on TVb website
are visitors coming through search engines and this section will show some figures regarding the
numbers of visitors coming from search engines and how this numbers are affected by semantic
annotations.
Figure 7 shows the number of visitors from the most well-known search engines, compared
over the period before deployment and same period of previous year.

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Figure 7 - Search engines for the period after deployment
Figure 8 shows the comparison of the period after deployment and same period of previous
year.
Figure 8 - Search engines for the period after deployment
To conclude this section, the most visitors came through Google, which has an increase from
27.77% to 41.85% visitors, followed by Bing with an increase from 82.27% to 90.51%.
This the difference of number of visitors per search engine may be caused by annotating the content.
2.3 Landing Pages

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One other important aspect of this semantic annotations evaluation is the direct impact of
annotated pages on the number of visitors, e.g. presentation page of Hotel Grand Europa on
TVb’s website. For this case we analyzed the before and after deployment periods directly.
Figure 9 – Comparison of some annotated landing pages

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The outcome, despite the important events which took place in the before deployment period, are
showing a total increase of 5.46% visitors on the landing pages of hotels, restaurant, sightseeing,
etc.
3. Conclusion and Next Steps
We know for a fact and we saw also in this document that search engines are using semantic
annotations. Just by annotating part of website’s entire content may increase the website’s
visibility.
For more accurate results we would like to re-evaluate the website based on a longer period of
time, e.g. 3-6 months and another review for the entire year 2014.