Facilitating Access to Health Web Pages with Different Language
Complexity Levels
Marco Alfano
1,4 a
, Biagio Lenzitti
2 b
, Davide Taibi
3 c
and Markus Helfert
1 d
1
Lero, School of Computing, Dublin City University, Glasnevin Campus, Dublin, Ireland
2
Dipartimento di Matematica e Informatica, Università di Palermo, Palermo, Italy
3
Istituto per le Tecnologie Didattiche, Consiglio Nazionale delle Ricerche, Palermo, Italy
4
Anghelos Centro Studi sulla Comunicazione, Palermo, Italy
Keywords: e-Health, Health Information Seeking, User Requirements, Language Complexity, Structured Data on the
Web.
Abstract: The number of people looking for health information on the Internet is constantly growing. When searching
for health information, different types of users, such as patients, clinicians or medical researchers, have
different needs and should easily find the information they are looking for based on their specific requirements.
However, generic search engines do not make any distinction among the users and, often, overload them with
the provided amount of information. On the other hand, specific search engines mostly work on medical
literature and specialized web sites are often not free and contain focused information built by hand. This
paper presents a method to facilitate the search of health information on the web so that users can easily and
quickly find information based on their specific requirements. In particular, it allows different types of users
to find health web pages with required language complexity levels. To this end, we first use the structured
data contained in the web to classify health web pages based on different audience types such as, patients,
clinicians and medical researchers. Next, we evaluate the language complexity levels of the different web
pages. Finally, we propose a mapping between the language complexity levels and the different audience
types that allows us to provide different types of users, e.g., experts and non-experts with tailored web pages
in terms of language complexity.
1 INTRODUCTION
The number of people looking for health information
on the Internet has been steadily growing over the
years (Taylor, 2010; Pew Research Center, 2013)
even though Akerkar and Bichile (2004) argue
whether looking for health information on the Internet
leads to patient empowerment or to patient deception.
Although looking for medical information on the
Internet may present some drawbacks, such as the
amount and quality of information, they show that
Internet has a major influence on patients/citizens
since more than 70% of them declared that the
medical information found on the Internet influenced
their treatment decisions. Moreover, patients/citizens
a
https://orcid.org/0000-0001-7200-9547
b
https://orcid.org/0000-0003-2664-7788
c
https://orcid.org/0000-0002-0785-6771
d
https://orcid.org/0000-0001-6546-6408
found relatively easy to obtain medical information
using the Internet and one third found it easier than
asking their health-care professionals.
When searching for health information on the
Internet, different types of users should easily find the
information they are looking for based on their
specific requirements (Alfano et al., 2015a; Banna et
al., 2016; Pletneva et al., 2011; EDC, 2011). In fact,
patients, physicians and medical researchers have
diverse needs and bring different levels of reading
ability and prior knowledge together with a different
vocabulary (Seedorff and Peterson, 2013; Zielstorff,
2003). However, generic search engines (like
Google
©
, Bing
©
or Yahoo
©
) work on the whole web
but make generic searches often overloading the user
Alfano, M., Lenzitti, B., Taibi, D. and Helfert, M.
Facilitating Access to Health Web Pages with Different Language Complexity Levels.
DOI: 10.5220/0007740301130123
In Proceedings of the 5th International Conference on Information and Communication Technologies for Ageing Well and e-Health (ICT4AWE 2019), pages 113-123
ISBN: 978-989-758-368-1
Copyright
c
2019 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved
113
with the provided amount of information. Moreover,
they are not able to provide specific information to
different types of users. On the other hand, specific
search engines, such as PubMed
1
or Quertle
2
, mostly
work on medical literature. They provide extracts
from medical journals that are mainly useful for
medical researchers and experts but not for non-
experts. Moreover, they do not consider all the
information contained in the web that may provide
additional insights. Another source of information
comes from the specialized web sites oriented either
to non-experts (e.g., WebMD
3
, Healthline
4
or
MedlinePlus
5
) or to medical professionals (e.g.,
Health on Net Foundation Select
6
, Translating
research into practice
7
or MDConsult
8
). Those sites
contain very focused information but are mainly built
by hand and then miss all the huge amount of
information that is available on the web. Moreover,
they are often not free.
A question then arises: Is it possible to facilitate
the search of health/medical information on the web
so that users can easily and quickly find information
based on their specific requirements? In this paper,
we provide a first answer to this question by
presenting a system that allows different types of
users to find web pages with proper language
complexity levels. To this end, we first present a short
survey that shows as a growing number of different
users use search engines to look for health
information on the web. We then use the structured
data present in the web to classify health web pages
based on different audience types such as patients,
clinicians and medical researchers. Next, we present
the results of some experiments to evaluate the
language complexity levels of the different web pages
and propose a mapping between the language
complexity levels and the different audience types
that allows us to provide users with proper web pages
in terms of language complexity.
2 SEEKING HEALTH
INFORMATION ON THE
INTERNET
We now present a short survey of the main
characteristics related to health information seeking
on the Internet, based on the following dimensions:
1
http://www.ncbi.nlm.nih.gov/pubmed/
2
https://quertle.com/
3
http://www.webmd.com/
4
http://www.healthline.com/
Who (e.g., number of people searching for health
information in the Internet)
Where (e.g., search engines, social networks)
When (e.g., time frequency)
What (e.g., symptoms, pathologies, remedies,
drugs)
The ‘Cyberchondriacs’ Harris Poll (Taylor, 2010)
shows that the percentage of all US adults who search
for health or medical information online has increased
from 27% to 76% from 1998 to 2010. On the other
hand, the ‘Health Online 2013’ Pew report (Pew
Research Center, 2013) says that 72% of adult users
in the U.S. were looking for health information online
in the previous year. When asked to think about the
last time they went online for health or medical
information, 39% of online health seekers say they
looked for information related to their own situation.
Another 39% say they looked for information related
to someone else’s health or medical situation. An
additional 15% of these internet users say they were
looking both on their own and someone else’s behalf.
For what concerns Europe, (Kummervold, 2008)
shows a growth from 14% to 39% in the 2005-2007
period. Moreover, in 2010, national bodies reported
that 52,5% of adults in Spain were looking for health
content on the Internet (Instituto Nacional de
Estadística, 2010) and 39% in the UK (UK national
statistics, 2010).
According to (Pew Research Center, 2013), 77%
of online health seekers say they began their last
session at a search engine such as Google, Bing, or
Yahoo. Another 13% say they began at a specialized
site in health information, like WebMD. Just 2% say
they started their research at a more general site like
Wikipedia and an additional 1% say they started at a
social network site like Facebook. According to the
survey reported in (Pletneva et al., 2011), a general
search engine is the most frequently used tool to look
for online health information (82% of the participants
said that they use a search engine always or often).
Other popular sources include websites providing
health information (38%) and Wikipedia or medical
search tools such as HONselect and Medline Plus
(37%). Forums and blogs are always or often used by
23% of the respondents and 5% use Facebook or other
social networks.
The same paper affirms that Internet is the second
source of information after physicians whereas
5
http://www.nlm.nih.gov/medlineplus/
6
http://www.hon.ch/
7
http://www.tripdatabase.com/
8
http://www.mdconsult.com/
ICT4AWE 2019 - 5th International Conference on Information and Communication Technologies for Ageing Well and e-Health
114
(Keselman and Slaughter, 2007) states that Internet is
the most commonly consulted resource for health
information followed by conversation with health
care providers and use of a medical dictionary.
Taylor (2010) shows that the percentage of US
adults who often or sometimes search for health or
medical information online has increased from 42%
to 73% from 1998 to 2010. Moreover, 81% of health
information seekers have looked for health
information online in the last month and 17% have
gone online to look for health information ten or more
times in the last month. On average, health
information seekers do this about 6 times a month.
According to the survey presented in (Pletneva et al.,
2011), 24% of the respondents say they were looking
for health information on the Internet at least once a
day (some mentioned from four to six times a day in
comments) and 25% did it few times a week.
Moreover, 8% did it once a week, 16% did it few
times a month and 16% did it once a month.
(Pew Research Center, 2013) shows that the most
searched health topics are: Specific disease or
medical problem (55%), Certain medical treatment or
procedure (43%), How to lose weight or how to
control your weight (27%), and Health insurance,
including private insurance, Medicare or Medicaid
(25%). According to the survey reported in (Pletneva
et al., 2011), the search activity of users is mostly
focused on general health information (68%), long-
term chronic diseases (59%), healthy lifestyle and
nutrition (50%), short-term (up to 2 weeks) acute
disease (39%), kids health (22%) and elderly health
and care (19%).
The above data clearly show that there is a high
number of people seeking for health information on
the Internet that has been constantly increasing over
the years (who). Search engines are the most used
means to access medical information (where) and
they are used more and more often (when) to seek
information on a broad range of medical subjects
(what). As a consequence, a question arises: Is it
possible to facilitate the search of medical
information on the web so that users can easily and
quickly find the information based on their specific
requirements?
To answer this question, in the next sections, we
analyse what are the main user requirements when
seeking for health information on the web and
propose a methodology for providing users with web
9
https://schema.org/
10
https://health-lifesci.schema.org/
11
http://schema.org/MedicalAudience
12
http://schema.org/Patient
pages that present different language complexity
levels, i.e., one of the most important user
requirements.
3 USE OF STRUCTURED DATA
TO CLASSIFY HEALTH WEB
PAGES BASED ON AUDIENCE
TYPES
When seeking for health information on the Internet
different types of users have different requirements
(Alfano et al., 2014; Ardito, 2013; Banna et al., 2016;
Eysenbach and Köhler, 2002; Higgins et. Al, 2011;
Jacobs et al., 2017). Among others, non-experts
usually require the used language to be easy to
understand whereas medical experts require a more
technical and precise language. Both categories
require the information to be trustworthy.
In order to satisfy these requirements, and mainly
the one related to the language complexity, we now
investigate the possibilities offered by structured data
to find web pages suitable to different classes of
users. To this end, we exploit the semantic
information available in the World Wide Web and, in
particular, the one provided by schema.org
13
. This is
a very important initiative, founded by some major
web players, that aims to create, maintain, and
promote schemas for structured data on the Internet.
In particular, schema.org defines the types and
properties associated with the information included in
the web pages so to expose them to search engines
and make easier for people to find the ‘right’ web
information. It is, presently, used in over ten million
web sites
9
, and its adoption has been investigated in
previous research (Dietze, 2017).
For the scope of the present work, we consider the
core schema and the health-lifesci extension
10
that
contains 100 types, 177 properties and 147
enumeration values related to the health/medical
field. In particular, we consider the
MedicalAudience
11
type that describes the target
audiences for medical web pages and Patient
12
,
Clinician
13
and MedicalResearcher
14
specific types.
As reported in schema.org, a patient is any person
recipient of health care services. Clinicians are
medical clinicians, including practicing physicians
and other medical professionals involved in clinical
13
http://schema.org/Clinician
14
http://schema.org/MedicalResearcher
Facilitating Access to Health Web Pages with Different Language Complexity Levels
115
practice, and medical researchers are professionals
who make research on the medical field.
Considering the above types, we have performed
an analysis based upon the schema.org information
made available by the Web Data Commons
initiative
15
. The Web Data Commons (WDC)
(Meusel, 2014) contains all Microformat, Microdata
and RDFa data extracted from the open repository of
web crawl data named Common Crawl (CC)
16
. The
data, released in November 2017, have been used in
this work. The whole dataset contains about 3.2
billion pages, with about 38.9% of them presenting
structured data.
The dataset dump available on the Web Data
Commons web site that we used in our study consists
of 38.7 billion RDF quads
17
. These are sequences of
RDF terms in the form {s, p, o, u}, where s, p and o
represent a triple consisting of subject, predicate,
object and u represents the URI of the document from
which the triple has been extracted. The dataset dump
has been made available as compressed files and each
file is around 100 MB large. Overall 8,433 files with
a total size of 858 GB have been provided. From these
8,433 files we have extracted the quadruples that
contain the Patient, Clinician and MedicalResearcher
specific types. We have obtained, then, a subset of the
WDC dataset dump that contains, for each type, the
number of RDF quads reported in Table 1.
Table 1: Number of RDF Quads extracted for each specific
type.
Schema.org types
RDF Quads
Patient
36,186
Clinician
15,913
MedicalResearcher
3,458
Fig. 1 presents an example of RDF quads, for the
Patient subtype, extracted from Web Data Commons.
It clearly shows the subject, predicate, object and URI
of the quadruples. In compliance with the Open
Science model, we have made available the RDF
quads subsets, for the Patient, Clinician and
MedicalResearcher specific types, at the address
http://h-easy.lero.ie/opendata/, in order to allow other
researchers to use and lead further research on these
data. Fig. 2 shows an extract of five RDF quads from
each subset.
Thus, by using, in turn, one of the three subsets
we are able to extract web pages targeted to Patient,
Clinician and MedicalResearcher types. Notice that,
at this stage, we have found web pages that have been
15
http://webdatacommons.org/
16
http://commoncrawl.org/
Figure 1: Example of RDF quads for the Patient subtype.
targeted to the different user types by their authors but
we do not exactly know why. It could be related to the
language complexity level (e.g., more or less
technical) or to the treated subject (e.g., pathology
symptoms and remedies, for patients, or deepening
aspects, for medical researchers), or to something
else. In the next section, we will map the language
complexity levels to the different audience types so to
be able to provide users with web pages related to
their specific requirements.
4 MAPPING LANGUAGE
COMPLEXITY USER
REQUIREMENT TO
AUDIENCE TYPES
As seen above, users (mainly non-experts) have
different requirements when searching for health
information on the web. In particular, one of the most
important requirement for non-expert health
information seekers is that the language used in the
web pages must be easy to understand. On the
opposite, medical experts require that the info they
are looking for presents a proper technical and
rigorous terminology.
We then consider two classes of users:
Non experts (e.g., patients or citizens);
Experts (e.g., physicians or medical
researchers).
We have used the three subsets presented in the
previous section, Patient, Clinician, and
MedicalResearcher, and, for each quadruple, we have
analysed the related web page in order to estimate its
language complexity. To this end, we have evaluated
the ‘term familiarity index’, as described in (Kloehn,
N. et al., 2018; Leroy et al., 2012) of the English and
non-empty web pages (around 50% of the total). In
particular, for each web page, we have computed the
term familiarity of each word by using the number of
results provided by the Google search engine and then
17
https://www.w3.org/TR/n-quads/
ICT4AWE 2019 - 5th International Conference on Information and Communication Technologies for Ageing Well and e-Health
116
(a)
(b)
(c)
Figure 2: An extract of five RDF quads extracted from Patient (a), Clinican (b) and MedicalResearcher (c) subsets.
we have computed the term familiarity index of the
page by averaging all the word familiarity indexes.
This information has been stored in a database to
avoid work duplication.
In particular, for each web page, we have
computed and stored the total number of words, the
number of unique words, the number of least common
words (i.e., the number of words minus the number of
words belonging to the list of the three thousand most
recurrent words as represented by the “Dale-Chall
Easy Word List”
18
), the average of term familiarity
indexes of all words and the average of term
familiarity indexes of the least common words (in
order to evaluate if the probable presence of the most
recurrent words in each web page could cause a bias
18
https://www.usingenglish.com/resources/wordcheck/lis
t-dale-chall+list+of+simple+words.html
in the average of familiarity indexes). The results of
the performed tests, for the three audience types, are
available at the address http://www.math.unipa.it/
simplehealth/simple2/ResSchema.php and the first
six results of each audience type are shown in Fig. 3.
Next, we have computed some statistics related
the term familiarity indexes of the web pages for the
different target audiences (by taking into account all
words and the least common ones) and we have
obtained the results reported in Fig. 4 and Fig. 5.
Fig. 4 shows, for each specific type, the box plot
of the average of the term familiarity indexes
computed for all words. A box plot is a standardized
way of displaying the distribution of data based on a
five number summary (“minimum”, first quartile
Subject Predicate Object URI
<https://medlineplus.gov/enc
y/article/001199.htm>
<http://schema.org/MedicalWebPage/au
dience>
<http://schema.org/Patient>
<https://medlineplus.gov/ency/article/00
1199.htm>
<https://medlineplus.gov/enc
y/article/003983.htm>
<http://schema.org/MedicalWebPage/au
dience>
<http://schema.org/Patient>
<https://medlineplus.gov/ency/article/00
3983.htm>
_:node49375316dc617590329
84f402ff140
<http://schema.org/MedicalWebPage/au
dience>
<http://schema.org/Patient>
<https://www.healthgrades.com/group-
directory/tn-tennessee/memphis>
_:node29c2dc857960d350697
9ce6e9e9fcab
<http://health-
lifesci.s chema.org/MedicalWebPage/audi
ence>
<http://schema.org/Patient>
<https://healthjoy.com/health-
plan/coverage/chicago-il-
60654/dentist/PPO-all/>
_:nodea935f7bf8568d9be197c
aabd279ed880
<http://schema.org/MedicalWebPage/au
dience>
<https://schema.org/Patient>
<https://www.ready2sm ile.com/temecul
a-ca/alternative-to-braces-for-teens/>
Subject Predicate Object URI
_:nodead17e982643d4ff761
893dc8d21a8a
<https://schema.org/Clinician>
<http://www.fpnotebook.com/OB/Fet
us/FtlBrdycrd.htm>
_:nodebbeb4aa9fdaa7c6bb
bf84ef58572be
<https://schema.org/Clinician>
<http://www.fpnotebook.com/ENT/Li
p/AnglrStmts.htm>
_:node5a c15d6f84ffa289c1b
2b15e24ce99c
<https://schema.org/Clinician>
<http://www.fpnotebook.com/Neuro/
Motor/Chr.htm>
_:nodebc14ebca2a1b81633
703ba9f9108660
<http://schema.org/Clinician>
<http://docality.com/doctor/profile/1
730195587/dr-azizul-hoque-md>
_:nodea943b2c2e88ea6d72
16a44b8ca4d1f6
<http://schema.org/Clinician>
<http://docality.com/doctor/profile/1
730195587/dr-azizul-hoque-md>
_:nodeebf33f24f05b1ec242
d1f8af45f71f42
<http://schema.org/Clinician>
<http://docality.com/doctor/profile/1
730195587/dr-azizul-hoque-md>
Subject Predicate Object URI
_:node7c9bf2b3856678d871
182f3b135bd0
<http://schema.org/MedicalWebPage/
audience>
<http://schema.org/MedicalResearcher>
<http://www.malacards.org/card/sarc
oma_synovial>
_:nodee6cdd393ab8d0a188
a48568b4a6f2
<http://schema.org/MedicalWebPage/
audience>
<http://schema.org/MedicalResearcher>
<http://www.malacards.org/card/vasc
ular_disease>
_:nodec9cc1378dc9f960c4a
d9f9a788af4b
<http://health-
lifesci.schema.org/MedicalWebPage/a
udience>
<http://schema.org/MedicalResearcher>
<https://healthjoy.com/health-
plan/coverage/chica go-il-60654/sports-
medicine/PPO-all/>
_:node352af2c53295fea33d
31d7283802098
<http://health-
lifesci.schema.org/MedicalWebPage/a
udience>
<http://schema.org/MedicalResearcher>
<https://healthjoy.com/health-
plan/coverage/chica go-il-
60654/pedi atrics-behavior-and-
neurodevelopment/PPO-all/>
_:node275549fc96b456291c
2d630b61f3b63
<http://health-
lifesci.schema.org/MedicalWebPage/a
udience>
<http://schema.org/MedicalResearcher>
<https://healthjoy.com/health-
plan/coverage/chica go-il-60654/pain-
medicine/PPO-all/>
Facilitating Access to Health Web Pages with Different Language Complexity Levels
117
(a)
(b)
(c)
Figure 3: First six test results for Patient (a), Clinican (b), and MedicalResearcher (c) audience types.
(Q1), median, third quartile (Q3), and “maximum”).
Overall, the median and the first-third quartile
interval of Patient is much higher of those of Clinician
and MedicalResearcher that partially overlap. The
outliers above the maximum mainly refer to pages
that contain informative/commercial data for the
different types of users and then use a simple
language. The outliers below the “minimum” mainly
refer to pages, such as those of the
www.malacards.org domain, which indicate all three
classes, as target audiences, but have a low term
familiarity index clearly indicating that they should
be targeted only to medical experts for what concerns
the language complexity.
Fig. 5 shows, for each specific type, the box plot
of the average of the term familiarity indexes
computed for the least common words (as seen
above). The same considerations of Figure 4 apply to
ICT4AWE 2019 - 5th International Conference on Information and Communication Technologies for Ageing Well and e-Health
118
Fig. 5 but, by eliminating the most common words,
the figures of Patient lower much more than the others
showing, once more, as the web pages targeted to
Patient are the ones tending to use the simplest
language.
Figure 4: Box plot of the average of term familiarity indexes
for all words.
Figure 5: Box plot of the average of term familiarity indexes
for the least common words.
The experimental results show that the web pages
targeted to Patient, present, on average, a much higher
term familiarity index and thus a simpler terminology
whereas the web pages targeted to Clinician and
MedicalResearcher present, on average, a lower term
familiarity index and thus a more complex
terminology, even though Clinician pages are a little
closer to Patient pages. As a consequence, Patient
pages, falling in the intervals shown in Fig. 4 and Fig.
5, can be used for the Non-expert class and
Clinician/MedicalResearcher pages, falling in the
intervals shown in Fig. 4 and Fig. 5, can be used for
the Expert classes producing then the following
mapping:
Non-experts -> Patient
Experts -> MedicalResearcher and Clinician
This allows us to provide different types of users
with health web pages targeted to their specific
language complexity requirements. Notice that the
presence of structured data inside a web page can also
be seen, somehow, as a guarantee of information
quality even though an evaluation of the quality level
of the info of a web page requires a specific analysis
that is outside the scope of this work.
As a final step, we have built a navigational tool
that allows to navigate among the web pages related
to the different types. This navigational tool can be
accessed at the address http://www.math.unipa.it/
facile and Fig. 6 reports the input interface of the tool
with the ‘diabetes’ term to be searched.
Figure 6: Input interface of the navigational tool.
Fig. 7 reports the top ten results of the
navigational tool for the ‘diabetes’ term and for the
three types: Patient, Clinician and MedicalReseracher
and the related weight. The ranking of each web page
is presently done by computing the page wight as
follows:
- Patient
(Term_Frequency * Page_Familiarity_Index) /
Total_Number_Of_Words (1)
because we want meaningful pages (high
number of occurrences of the searched item) but
with the simplest language;
- Clinician and MedicalResearcher
(Term_Frequency / Page_Familiarity_Index) /
Total_Number_Of_Words (2)
because we want meaningful pages (high
number of occurrences of the searched item) but
with the most complex/technical language.
By examining Fig. 7 we can easily see that the top
links of Patient present a high term familiarity index
and belong to medlineplus.gov which is notoriously a
web portal for non-experts. The top links of Clinician
present a medium-low term familiarity index and
belong to the fpnotebook.com web portal which acts
as a medical dictionary and presents a technical
language even though understandable by users with
Facilitating Access to Health Web Pages with Different Language Complexity Levels
119
(a)
(b)
(c)
Figure 7: Diabetes outputs for Patient (a), Clinician (b), and MedicalResearcher (c).
ICT4AWE 2019 - 5th International Conference on Information and Communication Technologies for Ageing Well and e-Health
120
Table 2: Comparing Google ranking and term familiarity ranking for ‘diabetes’ keyword.
#
Google Ranking
Term Familiarity Ranking
Familiarity
Index
1
https://medlineplus.gov/diabetes.html
https://www.nhsinform.scot/illnesses-and-
conditions/diabetes/diabetes
9.43
2
https://www.webmd.com/diabetes/default.htm
https://www.nhs.uk/conditions/diabetes/
8.89
3
https://www.medicalnewstoday.com/articles/323
627.php
https://www.medicalnewstoday.com/article
s/323627.php
8.63
4
https://en.wikipedia.org/wiki/Diabetes_mellitus
https://www.healthline.com/health/diabetes
8.42
5
www.diabetes.org/
http://www.diabetes.org/
7.34
6
https://www.medicinenet.com/diabetes_mellitus/
article.htm
https://www.mayoclinic.org/diseases-
conditions/diabetes/symptoms-causes/syc-
20371444
7.30
7
https://www.nhs.uk/conditions/diabetes/
https://www.medicinenet.com/diabetes_mel
litus/article.htm
7.26
8
https://www.mayoclinic.org/diseases-
conditions/diabetes/symptoms-causes/syc-
20371444
https://medlineplus.gov/diabetes.html
7.02
9
https://www.healthline.com/health/diabetes
https://www.webmd.com/diabetes/default.h
tm
6.91
10
https://www.nhsinform.scot/illnesses-and-
conditions/diabetes/diabetes
https://en.wikipedia.org/wiki/Diabetes_mell
itus
6.01
some medical skills. The top links of
MedicalResearcher present a low term familiarity
index and belong to malacards.org web portal that is
a human disease database and presents a very
technical and complex language. Notice that, as seen
above, some malacards.org pages contain all the three
audience types and may appear in the other rankings
because often present a high number of occurrences
of the searched item. Of course, the ranking
mechanism presented here is just a first proposal and
needs to be refined and enriched to transform the
navigational tool in a proper user-focused search
engine.
The use of structured data related to the intended
audience, in combination with the term familiarity of
a web page, provides a method to rank web pages in
terms of the complexity level of the text. Generalising
this approach, the term familiarity method can be
used to rank web pages even when they do not contain
any specific structured data about their audience. As
an example, Table 2 shows the comparison between
the top ten results provided by the Google search
engine for the ‘diabetes’ keyword (1
st
column) and the
same set ranked according to the page term
familiarity index (2
nd
and 3
rd
columns).
The applications of such ranking method can be
various. To cite a few examples: a meta search engine
could enrich the results of popular search engines by
providing additional information about the term
familiarity of each result or the results could be sorted
and presented according to the requirements of expert
or non-expert users.
4 CONCLUSIONS
In this work, after presenting the main characteristics
related to health information seeking on the Internet,
we have proposed an approach based on structured
data (by using schema.org) to classify health web
pages for different audience types.
Moreover, we have executed some experiments to
evaluate the term familiarity indexes of different web
pages and proposed a mapping between the language
complexity user requirement and the different
audience types. We have then presented a
navigational tool that allows different users to obtain
the web pages related to their language complexity
requirements.
Of course, more experiments need to be executed
in order to better understand the correlation between
the language complexity levels and the different
audience types and establish specific thresholds for
what concerns the term familiarity index of a web
page so that we can easily classify it as suitable to
expert or non-expert. Moreover, as seen above, we
need to improve the ranking mechanism of our
navigational tool so that is able to provide the
“correct” pages (in relation to the searched item/s)
while privileging, as much as possible, the term
Facilitating Access to Health Web Pages with Different Language Complexity Levels
121
familiarity. Furthermore, we want to analyse how to
provide users with web pages with proper language
complexity levels even using pages with different
complexity levels, e.g., simplifying the complex
medical terminology for a non-expert (Alfano et al,
2018; Alfano et al, 2015b). Finally, we want to
consider other user requirements, such as the quality
of information, and analyse if and which structured
data (e.g., schema.org types) could provide us, for
example, with web pages which present a high quality
of information.
ACKNOWLEDGEMENTS
This work was partially supported by the European
Union’s Horizon 2020 research and innovation
programme under the Marie Skłodowska-Curie grant
agreement No 754489 and by Science Foundation
Ireland grant 13/RC/2094 with a co-fund of the
European Regional Development Fund through the
Southern & Eastern Regional Operational
Programme to Lero - the Irish Software Research
Centre (www.lero.ie).
We would like to thank Dr. Paolo Bolzoni, of the
School of Computing at the Dublin City University,
for the technical support in analysing the large
amount of web semantics information contained in
Web Data Commons.
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