=Paper=
{{Paper
|id=Vol-292/paper-6
|storemode=property
|title=Vocabulary Patterns in Free-for-all Collaborative Indexing Systems
|pdfUrl=https://ceur-ws.org/Vol-292/paper6.pdf
|volume=Vol-292
|authors=Wolfgang Maass,Tobias Kowatsch,and Timo Münster,pages 45-57
|dblpUrl=https://dblp.org/rec/conf/semweb/MaassKM07
}}
==Vocabulary Patterns in Free-for-all Collaborative Indexing Systems==
https://ceur-ws.org/Vol-292/paper6.pdf
Vocabulary Patterns in Free-for-all Collaborative
Indexing Systems
Wolfgang Maass, Tobias Kowatsch, and Timo Münster
Hochschule Furtwangen University (HFU)
Robert-Gerwig-Platz 1, D-78120 Furtwangen, Germany
{wolfgang.maass,tobias.kowatsch,timo.muenster}@hs-furtwangen.de
Abstract. In collaborative indexing systems users generate a big amount
of metadata by labelling web-based content. These labels are known as
tags and form a shared vocabulary. In order to understand the charac-
teristics of that vocabulary, we study structural patterns of these tags
by implying the theory of self-organizing systems. Therefore, we utilize
the graph theoretic notion to model the network of tags and their valued
connections, which represent frequency rates of co-occurring tags. Em-
pirical data is provided by the free-for-all collaborative indexing systems
Delicious, Connotea and CiteULike. First, we measure the frequency dis-
tribution of co-occurring tags. Secondly, we correlate these tags towards
their rank over time. Results indicate a strong relationship among a few
tags as well as a notable persistence of these tags over time. Therefore,
we make the educated guess that the observed collaborative indexing
systems are self-organizing systems towards a shared vocabulary build-
ing. Implications on the results are the presence of semantic domains
based on high frequency rates of co-occurring tags, which reflect topics
of interest among the user community. When observing those semantic
domains over time, that information can be used to provide a historical
or trend-setting development of the community’s interests, thus enhanc-
ing collaborative indexing systems in general as well as providing a new
tool to develop community-based products and services at the same time.
Key words: Metadata, tagging, shared vocabulary, online community,
collaborative software, self-organizing system
1 Introduction
Cooperative, distributed labelling of content in the worldwide web is called col-
laborative indexing or social tagging. Within a collaborative indexing system
users annotate different contents e.g.: events1 , video clips2 , music3 , pictures4 ,
1
http://upcoming.org
2
http://youtube.com
3
http://last.fm
4
http://flickr.com, http://espgame.org
ESOE, Busan - Korea, November 2007 45
articles and references5 , weblogs6 or websites7 . These collaborative indexing sys-
tems facilitate mass categorization establishing so-called folksonomies, which is
a bottom up categorization made by a large user base.
A collaborative indexing system has basically two features. First, it is used
for future retrieval of self-indexed content. Secondly, it provides recommenda-
tions, which are based upon the co-occurrence of highly used tags within all
annotations, whereas we call one single process of annotation an indexing task.8
The recommendations are shown to the user by committing a tag query. For
instance: content tagged with html will be frequently tagged with css as well.
The data collected within an indexing task contains the name of the user,
an url linking to the content, one or more tags and time-stamp information.
Therefore, the data within a collaborative indexing system is basically a network
of users, tags and content in a given period of time. All tags together represent the
shared vocabulary of the user community. In this paper we study the structural
patterns of that vocabulary, thus focusing only on the partial network of tags.
Analyzing this partial network requires some constructs of the graph theory.
We assume the shared vocabulary to be a self-organizing system by means of
the systems theory [1]. Hence, stable patterns as well as specific correlations are
determined throughout the vocabulary.
In addition, implications on these patterns are presented. To support the re-
quirements of self-organizing systems by reducing external restrictions and forces
we choose the free-for-all collaborative indexing systems Delicious, Connotea and
CiteULike for empirical data extraction, where any user can index any content
element. Thus, indexing rights are not restricted as identified by Marlow et al.
[2].
This paper starts with related work covering collaborative indexing systems
and the systems theory. Then, we hypothesize two assumptions regarding stable
patterns within the vocabulary. Afterwards, we build up a model based on the
graph theoretic notion, clarify the methodic approach and present the empirical
data used to prove the assumptions. Subsequently, we present and discuss the
results of our analysis and draw implications on them. Finally, we give an outlook
on further research.
2 Related Work
A general review on collaborative indexing systems is given by Voss [3]. Mathes
[4] discusses the organization of information via tags and points out that user
generated metadata is of an uncontrolled nature and fundamentally chaotic com-
pared to a controlled vocabulary. But he also mentions that collaborative index-
5
http://citeulike.org, http://connotea.org, http://bibsonomy.org
6
http://technorati.com
7
http://del.icio.us, http://myweb.yahoo.com
8
There may also exist other recommender implementations, but we focus on the co-
occurrence of highly used tags because this information is freely accessible on the
web.
46 International Workshop on Emergent Semantics and Ontology Evolution
ing systems are highly responsive to the users needs and their vocabulary by
involving them into the process of organization. Vander Wal [5] distinguishes
between broad and narrow folksonomies depending on the amount of users, who
tag one specific content element. He also defines the difference between pure
tagging and folksonomy tagging.
Voss [6] discovers power law distributions of tag frequency rates in Deli-
cious and Wikipedia supporting the presence of self-organizing systems. Hotho
et al. [7] and Quintarelli [8] find power law distributions according to collabora-
tive indexing systems, too. Lund et al. [9] measure a power law distribution of
user shared tags within Connotea. Results of Golder and Huberman [10] show
regularities of dynamic structures within Delicious. Moreover, they introduce a
classification on the semantics of tags as well as Zhichen et al. [11].
Wu et al. [12] distinguish the potential of collaborative indexing systems as
a technological infrastructure for acquiring social knowledge. Millen et al. [13]
study the deployment of a collaborative indexing system within a company and
highlight the remarkable acceptance rate of the users as well as its personal and
organizational usefulness. In addition, Damianos et al. [14], Farrell and Lau [15]
as well as John and Seligmann [16] also examine the potential of collaborative
indexing systems for the enterprise covering people’s expertise, social networks
and the integration of those systems in existing collaborative applications.
An early classification of collaborative indexing systems is done by Hammond
et al. [17] confronting scholarly and general resources with links and web pages. In
a more detailed classification Marlow et al. [2] distinguish the design of a system
and present several user incentives. Heymann and Garcia-Molina [18] develop an
algorithm, which generates a hierarchical taxonomy of a tag network. For the
same purpose Mika [19] uses social network analysis on the network of users, tags
and content. Hotho et al. [7] develop a search algorithm for folksonomies to find
communities of interest within collaborative indexing systems. Cattuto et al. [20]
design a stochastic model for the analysis of indexing tasks over time consisting
of tags and users. Dubinko et al. [21] visualize tags over time with data from
Flickr, whereas Zhichen et al. [11] propose an algorithm for tag suggestions to
support the user within an indexing task. An overview of self-organizing systems
is given by Heylighen [1].
3 Motivation
As mentioned above, this paper deals with the partial network of tags. The
concept of tags is central in collaborative indexing systems. The same tags used
by different users to annotate similar content show a common understanding
of the users. The set of all tags utilized by the user community represents the
shared vocabulary. Users and content elements are linked to each other through
tags, which are also directly connected when they are used together within one
indexing task. Figures 1 and 2 are representing such an indexing task as well
as the resulting network of the tags sports, worldcup and soccer. Due to the
current work, the value of those tag connections is an essential dimension, which
ESOE, Busan - Korea, November 2007 47
is based on the frequency rate of tags co-occurring within all indexing tasks. A
prerequisite for a measurement of this frequency is the bag-model for aggregation
of tags, in which multiple tags can be assigned to one resource by multiple users
as discussed by Marlow et al. [2].
worldcup
url http://www.fifaworldcup.com
description FIFAworldcup.com The Official Site of FIFA World Cup soccer sports
tags sports worldcup soccer
Fig. 2. Resulting network of
Fig. 1. Graphical input mask for an indexing task the indexing task in Fig. 1
Prior work on stable patterns suggests that collaborative indexing systems
are self-organizing systems [10, 2, 6, 8, 9]. The vocabulary - consisting of tags and
generated within all indexing tasks by all users - is a part of this system, which
organizes its structure by itself, without a centralized control mechanism. The
users of a collaborative indexing system generate this vocabulary in a decentral-
ized approach, not even aware of it. On its own this system evolves over time
into a more stable state.
Contrary to the aforementioned work, we explore patterns emerging out of co-
occurring tags. Therefore, we want to know if the power law distribution, which
is common in broad folksonomies [7, 9, 8], is also applicable to the structure
of co-occurring tags. This would represent a community’s vocabulary, which
consists of a few tags co-occurring with high frequency rates and many tags co-
occurring with low frequency rates. Such a pattern - we call it tag economics -
would indicate a strong consensus on a particular subpart of the community’s
vocabulary, from which particular interests of the users can be identified. Due to
these considerations, we hypothesize the relation of co-occurring tags as follows:
H1 Let Ti be a tag and Tij all tags co-occurring with Ti . Then the ranked
frequency distribution of all valued connections from Ti to Tij follows a power
law curve.
Additionally, we focus on the frequency dynamics of tags over time depending
on their position in the aforementioned frequency distribution. We assume that
tags co-occurring with high frequency rates (higher position on the power law
curve) are more stable over time than tags co-occurring with low frequency
rates. This would represent persistence of the community’s interests or, when
tags with high frequency rates change to a low position, one can suggest a shift
of the community’s common understanding. Therefore, the current work has
the second objective to examine the relationship of the frequency rates of co-
occurring tags over time. We hypothesize this relationship as follows:
H2 The higher the frequency rates of the tags Tij , the more stable are they over
time.
48 International Workshop on Emergent Semantics and Ontology Evolution
4 Model
Let an indexing task be a quadruple comprised of < user, url, timestamp, tag ∗ >.
One user enters an url with none, one or more tags into a collaborative indexing
system at a certain time. Only two entities are important according to our hy-
potheses, namely timestamp and tag. Therefore, The community’s vocabulary
is modelled as an undirected, valued and finite graph V within a given period
of time δ. This period of time is essential, because the frequency of time based
indexing tasks is subject to fluctuations, which occur in the course of a day, a
week or month. Furthermore, δ can be used to affect directly the size of the
vocabulary V to ease the analysis.
The vocabulary V consists of a set of nodes (here tags) and a set of valued
links, which represent the frequency values of co-occurring tags. Hence, we refer
to this vocabulary as the network of tags, too. The links are undirected since each
tag i, which co-occurs with a tag j, also means that the tag j co-occurs with the
tag i, respectively. To better handle these frequency values, the vocabulary can
be described by a symmetric frequency matrix F , such that the value on the ith
row and jth column represents the frequency rate of the co-occurring tags i and
j over all indexing tasks within δ, denoted as f (i, j). Self references are excluded
since we focus only on co-occurring tags. Thus, the diagonal values f (i, j) with
i = j are always zero. Figure 3 exemplifies an undirected, valued graph of the
vocabulary V , whereas Fig. 4 shows the corresponding frequency matrix F .
Based upon this graph theoretic notion and the corresponding frequency matrix,
we are able to illustrate and compute the frequency distribution of co-occurring
tags.
1 2 3 4 5 j
1 1
0
0 2 2 1 6
6 2 0 0
2 2 0 4 3
5 3
2 0
3 2 4 0 0 2
2 2
1 2 4 0
4 1 0 0 0 2
4 3 5 6 3 2 2
0
0
i
Fig. 3. Undirected, valued graph of the
vocabulary V including 5 tags Fig. 4. Corresponding frequency matrix
F of the vocabulary V
4.1 Method
A frequency matrix F (δ1 ) is built within a given period of time. Afterwards,
the frequency values f (i, j) for each tag Ti are summed up. Consecutively, those
ESOE, Busan - Korea, November 2007 49
cumulative frequency rates are ranked by size and confined by a limit L. This
approach eliminates tags Ti with low cumulative frequency values of co-occurring
tags Tij , because they cannot contribute any meaningful values for co-occurring
tags and are therefore not relevant for further calculations. Then, N tags Ti with
maximum Nmax , medium Nmed and minimum Nmin cumulative frequency rates
are identified. Afterwards, the frequency distribution of all tags Tij co-occurring
with each tag Ti is calculated from this selection and subsequently ranked by size.
Finally, the values of these frequency distributions are normalized and utilized to
conduct a curve estimation regression statistic based on the power model, whose
equation is f (r) = β0 rβ1 with f (r) estimating the frequency rate depending on
the frequency rank r of a tag Tij . The results of the regression statistics are used
to prove hypothesis 1.
The aforementioned N tags Ti are also used to prove the second hypothesis.
Hence, N tags Ti with maximum Nmax , medium Nmed , and minimum Nmin
cumulative frequency rates are identified. Afterwards, the frequency rates for
each pair of co-occurring tags are written down in a time series each lasting δ2
over I iterations. To measure the stability between co-occurring tags Ti and Tij ,
the difference D from the mean frequency of each tag co-occurrence in Nmax ,
Nmed , and Nmin is calculated over all I iterations, so they can be compared
afterwards.
4.2 Empirical Data
The empirical data for the analysis was extracted from the collaborative in-
dexing systems Delicious, Connotea and CiteULike. This information is freely
accessible. Indexing rights are based on a free-for-all principle [2], thus support-
ing the requirements of self-organizing systems by reducing external restrictions
and forces. The content is respectively of textual nature. The selected collabora-
tive indexing systems differ in the community’s size and the quantity of indexing
tasks, the amount of tags, as well as the period of time in which the data was
gathered. Furthermore, all indexing systems use a bag-model to aggregate tags,
which is essential for our approach as mentioned in Sect. 3. Table 1 provides
detailed information about the empirical data.
Table 1. Empirical data
Indexing system Delicious Connotea CiteULike
Period of measurement 09/01/06 – 10/01/06 01/01/06 – 10/01/06 09/17/06 – 10/01/06
Indexing tasks (It) 452 806 92 333 3 798
It incl. at least 2 tags 269 737 (60%) 56 289 (61%) 2 430 (25%)
Tags incl. doublets 1 169 396 250 293 9 765
Distinct tags 130 776 (11%) 41 707 (17%) 3 659 (37%)
Distinct users 121 197 3 929 633
Users with at least 2 It 70 519 (58%) 2 722 (69%) 408 (64%)
50 International Workshop on Emergent Semantics and Ontology Evolution
5 Results
5.1 Hypothesis 1: Power Law Distribution of Co-occurring Tags
The ranked frequency distribution f (r) of Tij tags co-occurring with a tag Ti
is illustrated in Fig. 5. Thus, a power law distribution is clearly apparent in
the shared vocabulary, as to be expected from a broad folksonomy like Deli-
cious. There are many tags Tij with low frequency rates of co-occurring tags
and few with very high frequency rates. This result is proved by the cumula-
tive discrete co-occurrence distribution in Fig. 6, which illustrates the discrete
frequency distribution. There is a remarkable gap between tags, which co-occur
with only one single tag, and tags co-occurring with multiple other tags. Towards
the high co-occurrence rates the curve decreases rapidly as the logarithmic scale
demonstrates. Frequency rates of co-occurring tags above 100 lead to absolute
frequency rates less then ten. Similar results are provided by the collaborative
indexing systems Connotea and CiteULike.
The visual observations in Fig. 5 and 6 can be confirmed statistically. There-
fore, Table 2 shows the median of squared reliability (R̄2 ), the median degree
of freedom (F̄ ) as well as the exponent β̄1 according to the power law curve es-
timation algorithm9 over all corresponding tags within Nmax , Nmed , and Nmin .
Compared with other curve estimation algorithms, the power model performed
best by far.
In particular, data from Delicious with 2007 co-occurring tags Tij as a me-
dian degree of freedom shows a remarkable reliability of .96 by only .01 standard
deviation for Nmax . Values with less reliability values lie nearby .80, which is
still acceptable although standard deviation values show higher dynamics. Ad-
ditionally, a decrease of the exponent β̄1 can be observed related to the degree
of freedom by considering the data of Delicious and Connotea. This can be re-
ferred to a smoother power law curve, when less tags co-occur with a tag Ti . For
this reason, the relative low degree of freedom according to CiteULike can be
neglected to identify the aforementioned effect.
Due to these facts, the first hypothesis is supported by the empirical data. It
is quite evident that a power law curve of co-occurring tags is obvious for tags Ti
with high frequency rates, whereas the co-occurrences of middle and low ranked
tags Ti show more dynamics.
5.2 Hypothesis 2: Relation between Rank and Persistence of Tags
over Time
Figure 7 shows the dynamics over time of co-occurring tags against their de-
viations from the mean frequency. As illustrated, co-occurring tags with high
T1–250
frequency rates - values from Nmax - are more stable and have a lower scat-
ter respectively than co-occurring tags from Nmed or Nmin . Figure 8 shows the
9
Statistical software used for curve estimation: SPSS, Version 15.0.1, SPSS Inc.
Chicago, USA
ESOE, Busan - Korea, November 2007 51
f(r)
250
200
150
100
50
0
0 50 100 150 200 250 r
Fig. 5. Extract from the ranked frequency rates f (r) of Tij tags co-occurring with 30
tags Ti (different symbols) based on Delicious, δ1 : 09/10/06 – 09/19/06
100000
10000
1000
frequency
100
10
1
1 10 100 1000
discrete co-occurrence distribution
Fig. 6. Discrete co-occurrence distribution of tags Ti and Tij from Fig. 5 and the
corresponding frequency values
52 International Workshop on Emergent Semantics and Ontology Evolution
Table 2. Curve estimation regression statistics based on the power law f (r) = β0 rβ1 ,
R̄2 : median of squared reliability, F̄ : median degree of freedom, standard deviations
are provided in brackets
Indexing system Delicious Connotea CiteULike
N / L 30 / 25 30 / 25 20 / 10
δ1 09/10/06 – 09/19/06 01/01/06 – 09/25/06 09/15/06 – 09/25/06
R̄2 / F̄ / β̄1
for Nmax .96 (.01) / 2007 / -.96 (.08) .93 (.10) / 638 / -.82 (.28) .81 (.10) / 58 / -.46 (.18)
for Nmed .82 (.08) / 152 / -.51 (.12) .86 (.06) / 88 / -.58 (.29) .79 (.12) / 32 / -.47 (.32)
for Nmin .78 (.11) / 73 / -.42 (.21) .84 (.11) / 55 / -.53 (.28) .82 (.20) / 20 / -.65 (.40)
relative frequencies of two co-occurring tags from Nmax and Nmed in contrast
to each other over 30 iterations with δ2 = 1 day. This figure illustrates that the
interval of Nmed shows higher variations than the interval of Nmax .
The basic data from all examined collaborative indexing systems with the
average deviation of the mean frequency values D̄ over Nmax , Nmed , and Nmin is
shown in Table 3. As a result, δ2 and the number of indexing tasks within δ2 are
affecting the dynamics. Those in Connotea and CiteULike are much lower than
the dynamics in Delicious. This often causes co-occurring tags to appear only in
a small degree over all iterations and therefore, they stabilize only on a low level
with low frequency rates. Thus, a small deviation from the average frequency
rate over all iterations is not always indicating a high position of co-occurring
tags on the power law curve. There are also situations, where tags stabilize on
low frequency rates. The stability alone is therefore not a sufficient criterion for
the occurrence of high frequencies. In fact, the absolute frequency rates must be
observed for a positioning on the power law curve besides the deviation.
As a result, correlations between high frequency rates and their persistence
over time can be concluded, but not vice versa. A change of that persistence is
therefore only significant for a shared vocabulary, if the deviation of the average
value appears on high frequency rates. Nevertheless, the second hypothesis is
also supported by the figures of Table 3, although with less explanatory power
compared to the findings of hypothesis 1.
Table 3. Deviation over the average frequency
Indexing system Delicious Connotea CiteULike
N / L 250 / 30 30 / 10 15 / 5
δ2 1 day, 1 month, 1 day,
09/01/06 – 09/30/06 01/01/06 – 08/31/06 09/16/06 – 09/30/06
D̄(Nmax ) 14.1% 22.2% 15.1%
D̄(Nmed ) 19.4% 24.0% 16.1%
D̄(Nmin ) 19.8% 27.0% 16.7%
5.3 Implications
As shown in section 5.1, we observe a frequency distribution of co-occurring tags
which follows a power law curve. The examined collaborative indexing systems
ESOE, Busan - Korea, November 2007 53
0.3
Deviation over the average frequency in %
0.25
0.2
0.15
0.1
0 100 200 300 400 500 600 700
Ti
Fig. 7. Deviation over the average frequency in % of 750 tags Ti and all related tags Tij
T1–250 T251–500 T501–750
for Nmax , Nmed and Nmin based on Delicious, between 09/01/06 – 09/30/06
0.5
0.45
0.4
relative frequency in %
0.35
0.3
0.25
0.2
0.15
0.1
0.05
0
5 10 15 20 25 30
day
Fig. 8. Relative frequency comparison in % of 1 high (dashed) and low positioned tag
Ti from Fig. 5 with δ2 = 1 day and 30 iterations based on Delicious, between 09/01/06
– 09/30/06
54 International Workshop on Emergent Semantics and Ontology Evolution
support a distributed approach without central control mechanisms, favoring
self-organization. The observed distribution of tags means that the users have
a strong consensus at least on a particular subpart of the shared vocabulary,
since co-occurring tags with high frequency rates build a semantic domain. This
shows some sort of tag economics within a collaborative indexing system.
A further aspect indicating a self-organizing system is the resilience of the
system. Accidental errors, e.g., typos or willful sabotages of the system by users
have negligible effects, because single users cannot tip the scales of a power
law curve. In addition, the construct of indexing support through pre-defined
tags, which is suggested to consolidate the tag usage [11, 2], would additionally
support these findings by diminishing the limits of the uncontrolled vocabulary
such as polysemy, synonymy/uniformity and basic level variation problems [10,
22]. Hence, we suggest higher frequency rates within the top ranked tags as well
as lower rates within low ranked ones as fundamental impacts of the indexing
support construct.
Another feature of a self-organizing system is the adaptation of environmen-
tal changes. In terms of a collaborative indexing system, these changes can be
referred to as a shift of the community’s interest, which is likewise reflected in
a structural change of the vocabulary. Hence, semantic domains based upon co-
occurring tags with high frequency rates may change. For instance, if the position
of a tag Tij alters over time by means of an increase or decrease of the frequency
rates according to Ti , then this progress suggests a structural change within the
vocabulary and vice versa. The higher the position of this tag on the power law
curve, the more significant is the structural change of the vocabulary. When this
dynamic information is monitored one can observe a historical or trend-setting
development of the vocabulary based upon the time-stamp of selected indexing
tasks. Those trend curves of the vocabulary suggest changes within the commu-
nity’s interest and are useful for the particular user when searching for content
elements, users or tags in the time domain.
6 Conclusion and Future Work
In this paper we studied structural patterns of user generated vocabularies within
the free-for-all collaborative indexing systems Delicious, Connotea and CiteU-
Like. The theory of self-organizing systems was implied to hypothesize patterns
within those vocabularies. We built up a model based on the graph theoretic
notion consisting of tags and their valued connections. This was required to cal-
culate the frequency distribution of tags that co-occur with others, as well as to
correlate those tags towards their frequency rate over time.
Results indicate that only a few co-occurring tags exist with high and many
with low frequency rates, thus following a power law curve. In addition to that,
co-occurring tags with high frequency rates proved to be more stable over time
than those with low rates. The results were also depending on the quantity of
indexing tasks. For instance, the measured values of CiteULike yielded less ex-
planatory power than the values of Delicious. Implications are drawn through
ESOE, Busan - Korea, November 2007 55
the presence of semantic domains, which are based on co-occurring tags with
high frequency rates and the shift of common interests among the user com-
munity, if those high rates are fundamentally changing over time. The resulting
information can be used to provide a historical or trend-setting development of
the vocabulary and would not only be useful for the particular user but would
also support enterprises to develop products and services, which may depend on
or at least involve the interests and trends of online communities.
Due to the current work, the development of algorithms for trend information
and historical time series based on the frequency distribution of co-occurring
tags is an interesting area for further research. A common understanding of
the user community is expressed through the tag network comprised of valued
links with high frequency rates. In addition, semantic domains of more than
two co-occurring tags can also be identified with techniques of the social net-
work analysis such as centrality measurements or clustering. This network alters
dynamically in a self-organizing way over time suggesting new topics or events
of social, academic, technical or economic nature. Defining triggers on the ob-
served power law curve to identify those variances requires further clarification,
but would be very useful by supporting users, enterprises or public organisations
in upcoming decisions.
Moreover, there is still a challenge in collaborative indexing systems featuring
low indexing rates within a given period of time. This applies especially for
those systems deployed in companies. Therefore, it is essential to find techniques,
which permit major vocabulary coherence in such minimal systems and boost
the significance of the common understanding.
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