=Paper=
{{Paper
|id=Vol-2345/paper10
|storemode=property
|title=A “Searchable” Space with Routes for Querying Scientific Information
|pdfUrl=https://ceur-ws.org/Vol-2345/paper10.pdf
|volume=Vol-2345
|authors=Renaud Fabre
|dblpUrl=https://dblp.org/rec/conf/ecir/Fabre19
}}
==A “Searchable” Space with Routes for Querying Scientific Information==
https://ceur-ws.org/Vol-2345/paper10.pdf
BIR 2019 Workshop on Bibliometric-enhanced Information Retrieval
A “Searchable” Space with Routes
for Querying Scientific Information
Renaud Fabre
Laboratoire Paragraphe (EA349), Universit Paris 8, Saint-Denis, France
renaud.fabre01@gmail.com
Abstract. Users searching for scientific information are confronted by
a “hidden face” of searchable space: their own selection of items, which
could help map their navigation on recorded search “routes”, is not open
to consultation, and remains either concealed or even unavailable. At
best, users benefit from recommendations but, as data on their own
choices are not shared, networked information for global navigation re-
mains nebulous.
This position paper tackles the following research question: How could
users searching for scientific information benefit from each other’s search
sessions? Our answer comprises two steps. First, using examples from our
own data, we look at the characteristics of user behavior, approached here
mainly via structures of collaborative personalized search. The second
step proposes a mapping of recorded search sessions: for similar queries,
search sessions are modeled by sets of typical user/item pairs in networks
we call “eco-systems” of queries. These eco-systems connect search ses-
sions and are open to navigation for users. Final output is threefold: 1)
visualization of interconnection between search sessions, 2) localization
of the search session that best suits a user’s needs, 3) navigation on
alternative “routes” between search sessions selecting alternative paths
to answers. The conclusion raises questions about typical uses of this
new kind of a documentary object, exploiting functionalities originally
adapted from a bipartite graph.
Keywords: knowledge management search session . query modeling informa-
tion retrieval. bi-partite graphs
1 Introduction
Scientific and technical information (STI) has been recently qualified as a “com-
plex system” that has to improve its accuracy and its digital organization: these
opinions, which appear in the research agenda (The National Academies of Sci-
ence and Medicine 2017) of the National Academies of Sciences of the United
States, underpin this position paper which raises questions about “searchability”
of STI.
“Searchable” means “capable of being computationally searched”, within an
independent community of search, open to serendipity (Conrad and Moeller,
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2017), and navigation in an unknown space, even if we could now consider that:
“Web search is governed by a unified hidden space, and each involved element
such as query and URL has its inborn position” ([3]). In the STI community,
searching is a highly dynamic practice as the “aims and intentions of the user’s
initial search evolve as new information is encountered” ([17]).
The main issue tackled by this position paper is to approach search sessions
for any query as “communities” of information retrieval, which are together con-
fronted by the “hidden face” of their common searchable space. Users of search
sessions never receive a record of other users’ generated contents. There is no
mapping of the various results of search sessions, showing clusters of user se-
lections of items: these data, even anonymized, are not open to consultation,
and remain either concealed or even unavailable. The lack of these kinds of data
significantly affects STI searches, where a single keyword could give access to
a very wide range of structured discussions and interpretations, each delivered
with their own specific “version” and “vocabulary” or field of experiment. Be-
cause of this limitation on alternative documentary selections, it remains difficult
to identify roots of controversies: accurate interpretation of views, downloads,
citations, is uncertain, and searching can sometimes appear as a lonely walk in
a forest of hazy homonyms.
However, current research barely addresses the issue, as only “few approaches
take advantage of searches performed previously by users” ([10]). Fortunately,
recent approaches on interactive information retrieval and user behavior high-
light that STI searches cover a large variety of distinct methods and needs ([12]).
Based on the latter positive standpoint, in short, this article tackles the following
research question: “How could STI search users benefit from each other’s search
sessions?”
Our answer is twofold. First, we look at the variety of search behaviors and
the need for a clearer approach to networks of search sessions: for similar queries,
search sessions could differ significantly in their selections of items according
to users’ variable needs, which are still hardly modeled. Second, we propose a
mapping of search sessions with the help of a bipartite graph that we developed
for navigation inside what we call the “eco-system” of answers to a query. The
final point of this article is to review issues of efficiency relating to this new
documentary object, modeling alternative searches, and helping to select, among
recorded options, the answer that best fits a query.
2 Part 1. Search session and users’ behaviors
STI search strategies are revealed by behaviors of the searches performed by
users. These behaviors could be identified from structured “relations between
the topics and the use of documents” ([4]), and users could choose between
alternative strategies of search and use ([6]).
We will look at the characterization of user behavior in two areas: first is
the community behavior of searches, which appears to differ among disciplines;
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second, we review elements of individual behaviors which appear to vary with
wide-ranging reference chasing purposes. Both seem to interact.
2.1 Community behaviors: variations among disciplines
To start with examples, differences in STI use behaviors among disciplines could
be considered via the large-scale survey of research data management carried
out at the CNRS (Centre National de la Recherche Scientifique) in 2014 ([16]).
Results, which are part of a nationwide survey on scientific information uses,1
include the opinions of 432 directors of French public research laboratories, an-
swering 91 questions, out of 1,250 directors of laboratories who were asked to
answer. Overall, with a rather high answer rate (above 30%), data reveal sig-
nificant differences in STI uses among various fields of scientific practice. For
instance, concerning adoption of a clearly identifiable common STI practice like
Research Data Management (RDM), “we can distinguish three groups: (1) lab-
oratories from nuclear and particle physics and from social sciences and human-
ities appear globally more advanced regarding RDM than other disciplines; (2)
laboratories from the three domains of ecology and the environment, informatics,
and earth sciences and astronomy have dedicated resources and make their data
available; (3) laboratories in the field of physics appear aware of the challenge.”
Regarding Open Access practices, results also reveal significant differences in the
management of STI data: these results all have direct impacts on the methods
and purposes of search activities and on searched items.
Another type of community behavior appears in a further recent national
survey (COPIST): also at the CNRS, we carried out a survey of STI manage-
ment at the national scale for all research organizations. Results include answers
from 106 research and higher education institutions and events, overall showing
a strong desire to share STI digital practices and fairly large differences between
current uses and management of groups of institutions. Detailed results on bib-
liometric uses and strategies are available:2 they reveal significant differences in
users’ practices at the various institutions, together with their desire to share
uses and data.3
2.2 Individual Behaviors: variations in reference chasing
It is established that emerging results create the risk of a “cold start” biased
answer to a query: new content is difficult to retrieve as it has, by definition,
not yet been produced elsewhere. Conversely, a versatile answer could be seen
as fruitful when trying to find out items which could be identified together
as “typical” and “original”. It can also be the case that the attempted search
1
http://www.cnrs.fr/dist/z-outils/documents/Enqu%C3%AAte%20DU%20-
%20DIST%20mars%202015.pdf
2
http://www.cnrs.fr/dist/z-outils/documents/copist-premiers-resultats.pdf, p. 55
and further
3
Ibid, pp. 11 and 19
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result is bound to unpredictable instrumental bases. [7] These search intentions
could explain how “multiple search strategies” have been experienced in an STI
context ([5]), and that a multiplicity of search tactics exist: up to 29 separate
search methods have been described (Bates, 1979).
Search sessions can thus have numerous goals, be grounded in variable mo-
tivations, and differ along with variable discovery methods ([7]). Searching thus
develops its own rationality: it could be said that “search is not research”. Over-
all, the search context remains nebulous, with a permanent threat of informa-
tion overload ([8]), and with questionable performances of recommender systems
([19]). User behavior modeling is not evenly covered by research: the field of ob-
servational ([9]) data is a significant example where a wide range of behaviors
“appear to balance breadth and specificity,” while authors clearly observe a wide
range of differences between uses of reviewed disciplines. Modeling of search ses-
sions meets with many open challenges, which are all based on “interactive IR”
and the need to model it.
3 Part 2: Networking search sessions: a “Query
Eco-System”
3.1 Interactive IR: a network for search sessions
Interactive IR proposes networking of search sessions in various type of frame-
works like a “collaborative query management system” for “search and browse
interaction” ([13]); it could be tested on data analyses of parsing on publishers’
knowledge bases (PKB) or researchers’ documentary logs. A better knowledge of
user behaviors results in “collaborative personalized search”[20] which “satisfies
the various information needs of different users” of the same query. In the prac-
tice of personalization techniques, a modeling system like PECIRS [15] gives
examples of “a new user-centric mechanism”. Personalization aims to “adapt
search results to enhance the relevance to users according to their past search
behaviors.”
In this context, the “query ecosystem” presented here takes an innovative
approach to personalization techniques, aiming at an “interactive information
retrieval process” ([5]), interlinking changes in search results with changes in
knowledge ([12]). Here personalization does not refer to users’ past behavior but
to visualization of all mapped search sessions and to autonomous selection of
the search session that best suits a user’s needs.
In a “query ecosystem”, interaction of a set of search sessions corresponds
to specific IR needs in given STI contexts: an example is when research infras-
tructures using the same kind of analysis source (X Rays, Neutrons, etc.) for a
large variety of experiments, each with their own practices in the same science,
produce comparable search sessions as “versions” of the same scientific object. In
these contexts, the main goal of a query eco-system is to “re-rank results based
on the user’s intention” by formalizing an analyzed interaction between similar
queries and retrieved results, and thus “lower the cognitive burden” of search by
mapping search sessions on a documentary topic of common interest ([21]).
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To build up representation of search sessions, we use graphs which have
long been used for a large variety of tasks related to knowledge representation.
Bipartite graph characteristics ([14]) are exploited in this article, with a typical
crown-graph figure, familiar to neural network approaches, but used here in a
novel manner, which we developed following ideas suggested by the information
analysis capacities of graphs ([11]) tied to their typical geometry. To the best of
our knowledge, there is no modeling of search sessions analysis comparable to
the “query eco-system.”
3.2 Definition 1: query routes and search sessions
Let us consider that our goal is to record “query routes” for a given keyword, and
that these routes, which of course could differ, are designed to be compared ([2]).
Let us then write:
Qn = f (Nn , Kn )
where Qn is a number of queries produced by users of a browser. Let us also
consider that for each query Q, we can record a number of users N and a number
of items (URL, documents, articles) K recording the search for a keyword at the
same time among the same corpus of items: we will call this group a community
of users of the same query “route” in a search session .
For positioning any distinct route, we could express the limits of its system
of “typical” search sessions ([10]) for a given query. Let us pose two limits of
substitution between “users” and “items” for any query: one limit is where a few
users ask for many items, and the other limit is when many users consult a very
small number of items. We then could write these limits of possible substitution
between users and items, N and K:
Q1 = f (Nmax,Kmin or Q1 = f (Nmin,Kmax
Or, in a general form:
[max, min
Qn = fN, K
[max, min
3.3 Definition 2: variation of queried search sessions
Let us now note α the coefficient of increase of N and β the coefficient of increase
of K when Q varies by one unit, that is to say, when a new additional query on
the same keywords is recorded (for instance: check users interested in items of a
domain before and after publication of a famous article). The above-mentioned
coefficients of variation will allow measurement of the “stability” or instability of
the search session’s content, according to variations in coefficients of the increase
or decrease of quantities n and k between two queries of the same “route”. We
will then record whether or not users and/or items have varied in a correlative
way, between queries Q1 and Q2 .
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We assume here that the data structure of any query could vary from one
to the other, and that this variation could be expressed by an observable and
recordable index of change in the relation between number of users and number
of items.
We could then write:
α β
Qn = f [nmax, min kmax, min
Let us note that value “min” or “max” of n and k provide data on the
measured quantity of these variables to “produce” Q, but let us also note that,
from one unit Q to the other (from one query to the other), the coefficient
of increase or of decrease between value “min” or “max” of n and k provides
additional data on the quantity of these variables in the production of Q. These
additional data provide a value of the “stability” or “instability” of the process
of querying an additional search session, with a dynamic index of behavior. For
any unit n and k in production of Q, the variation of quantities measured by α
and β is critical information about Q.
3.4 Definition 3: networks of queried search sessions
We know that with regard to the slope of a set of queries, with the added mix
of user and items that it measures, the slope tends to be stable when α + β ≤ 1
or (α + β) → 1: in the latter case, transition between Qn−1 and Qn is steady
because, as measured by α + β, variation of (n + k) is still of the same order as
the variation of Q. There is no significant increase or decrease in the number of
units (users and items) recorded between these two or more successive queries
and corresponding search session content.
Conversely, there could be also an alternative situation in which (α + β) ± ∞,
i.e. that for each variation of one unit of Q, we have an unstable variation of
characteristics of Q, indicated by an unstable variation of (α + β), which will
increase or decrease abruptly: relations between users and items will change
strongly. In the latter case, between units Qn−1 and Qn , the variation of the
quantities x and y would be unstable, which means that the query’s user and
item pair will change significantly. This situation could indicate that a threshold
has been reached in the effectiveness of the query.
In this case, why continue to allocate (n, k) to Q if (α + β) tends toward
minus infinity −∞? This would mean that the combination (n, k) becomes about
“performing”, which is questionable when the time comes to decide on a new
query Qn+1 with the same characteristics of n and k. With such characteristics
for a query, it could be more interesting to change n or k than to reproduce the
same quantities in the next query. More generally, for transition between Qn−1
and Qn , the choice of stability in n and k could be “justified” if we observe that
stability prevails with (α + β) tending to 1 and “questionable” when instability
prevails with α + β tending to ± ∞.
On a long series of queries, there will be “learning” phenomena which will give
meaning to the expression of limits in variations of the purposed investigation
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on a long set of queries covering large fields. In that perspective, it is possible
to fix arbitrary limits of variations to a given set of queries, with the “best” and
the “worst”, for instance:
α β
Best = Nmax , (α + β) → 1, Kmin
And
α β
Worst = Nmin , (α + β) ± ∞, Kmax
If we have selected these two opposite limits to queries belonging to an STI
data search, we could write our limits with the following framework:
“Best” “Worst”
Nmax Nmin
(α + β) → 1 (α + β) ± ∞
β β
Kmin Kmax
3.5 Definition 4: a “compass” positioning search sessions on routes
On the basis of the preceding expression, the two triplets could give shape to a
formal graph-based presentation of paths existing between these two limits and
possessing the characteristic of positioning all the non-contradictory solutions
existing between these two limits with three parameters. The following bipartite
Hamiltonian graph with connected nodes offers that representation.
α
N max a • • d α
N min
(α + β) → 1 b • • e (α + β) → ±∞
β
Kmin
c • • f β
Kmax
Fig. 1. “Compass” GRAPHYP: Positioning Typical Search Sessions
Figure 1 shows a complete representation of all the typical intermediary situ-
ation between our two limits, which gives us a tool for classification of observed
queries Q in series of searches on a given keyword, according to user and item
choices. Structured by GRAPHYP, this set of query search typical position has
two main characteristics, which will be detailed below with the help of Table 1.
As observed in Table 1, GRAPHYP expresses the whole set of non-contradictory
positions that structured search routes could occupy during any search on a given
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keyword in a database. This has consequences, defined by three characteristics,
on GRAPHYP operating rules.
NODE (Query) TYPICAL DIRECTION OF SEARCH (Users, Items)
a Nα max , Kβ max , (α + β) → ± ∞
b Nα min , Kβ max , (α + β) → 1
c Nα min , Kβ min , (α + β) → ± ∞
d Nα min , Kβ min , (α + β) → 1
e Nα max , Kβ min , (α + β) → ± ∞,
f Nα max , Kβ max , (α + β) → 1
Table 1: Classification of Typical Search Sessions in GRAPHYP
Characteristic 1: GRAPHYP functions like a compass as it supplies all
possible directions, and “locates” the recorded direction of a query Q. This set of
possible “positions” during a search gives an overview of the proposed modeling
of the searchable space for STI queries.
Characteristic 2: the second feature of GRAPHYP search modeling is that
it can be used to record and compare search “behaviors” in querying. It allows
users to learn from their past recorded behavior as well as from the recording
of other users of the same base, if data is made accessible to all users. The
anonymization solution is of course an important optional condition. [18]
In these two steps, the “compass” has the function of “orienting” and “lo-
cating” search attitudes as being recorded as individual dynamic behaviors in a
search session.
Characteristic 3: GRAPHYP also allows the expression, by summation of
individual attitudes, of the global trend of a community of users according
to the characteristics of their uses in their own search sessions.
It could be possible to arbitrarily select one node, between a and e, to fix an
optimum point or optimum search session there, and measure the distance
of a set of observed routes toward the selected node. Mapping of STI search
routes becomes possible, like in air or sea routes. Cooperative or conflicting
routes could be identified by the GRAPHYP data structure.
3.6 Navigation Rule 1: a “progressive learning system” for search
sessions
GRAPHYP creates opportunity to reach output (STI data search session best
results) while identifying alternative ways to reach it for a given input (query).
In this way, users’ learning profiles express the way they access items and which
items could be thus compared and located locally and globally in their search
sessions. GRAPHYP could be used as a kind of a browser for mapping uses of
search sessions for an STI community.
Like any compass, GRAPHYP secures navigational parameters at any scale
(see infra), owing to its built-in characteristics of information processing and
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transfer, its functions for positioning previously recorded routes, and its capacity
to record and recommend navigation paths. GRAPHYP thus belongs to a new
generation of learning systems combining intuitive choices and automatic path
definition at any operational scale.
3.7 Navigation Rule 2: locating search session routes
Let us take all nodes (a, b, c, d, e, f ) as possible starting points for definitions of
“goals” of routes of search sessions at time T . Any node could be designed as a
“goal” to be reached on a route built from the starting point of a given other
node. We could then record “search session profiles” corresponding to circulation
from one node to the other on a recorded route between search sessions.
a d
1 4
b e
5 2
3 6
c f
Fig. 2. Exploring Neighbor Search Sessions with GRAPHYP “compass”
From the design of Figure 2 above, we can observe that a continuous circula-
tion from node to node shows a common edge in all the pairs corresponding to
any node: node a has a common edge with node e, b has a common edge with
f , etc. From this standpoint, we could remark that it is consistently possible to
have circular “travel” inside GRAPHYP, from edge to edge, while coming back
to the starting node by the way of the complementary edge of that node. In this
way, search sessions could be interconnected and explored.
3.8 Navigation Rule 3: navigating between search sessions
As shown on Figure 2, node a could allow an exploration of GRAPHYP’s other
nodes which will represent six steps on one route, as designed here. Another six-
step route could be practiced from the same node a starting from its other edge.
There are at least 12 possible steps which could be explored from any node in
the Eulerian circuit, and GRAPHYP thus offers 72 possibilities for identifying
“search profile” characteristics which could be modeled and recorded in an STI
query.
Numerous characterized positions in exploration of possible search sessions
could be identified in this way: it offers users and their community detailed
records of explored and unexplored ways of discovering information.
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We could remark that these explorations of “possible” routes could be man-
aged overall with any conventional probabilistic approach, with which the pos-
sibilistic approach could combine for any purposed uses.
3.9 Navigation Rule 4: fixing a search session profile
We could also apply the design of Figure 2 to propose another significant feature
of traceability for searchable STI space, namely the recording of search profiles,
used to “read” characteristic steps of a navigation route in a database. This
result could be produced when projecting on x, y axes as shown in Figure 3.
nx + 5 c
d
nx + 3 c b
d f
a a
nx a
e c e
a c c
c
f f f f
b b b b
b
d d d d
f c c c
e e e
ny-2 e e ny - 2
a a a
f f
b
b
c
Fig. 3. Recording and Positioning of Nodes on Data Search Routes in GRAPHYP
Based on the design shown in Figure 3, we could, for any query applied to
this grid of structured data (in our example: nx + 3 and nx + 5, ny2), identify
the origin and thus the “kinship” of any search session located by GRAPHYP.
We could then evaluate all diverging or converging solutions surrounding any
observed node located on the grid. For any observed network, there exists an
“induced network” which applies the property of “mutual reachability” of
connected nodes in a network.
Mapping of search session routes using these ideas should help build strategies
for discovering information.
3.10 Navigation Rule 5: mapping distances between search sessions
Exploring neighbor routes between search sessions is an important feature of the
searchable space, as it provides information on its depth and treewidth and, as
far as we know, there are hardly any existing developments in mapping of this
aspect of searchable STI space.
Reducing uncertainty on the size and direction of future routes to discovery
could benefit from representations of courses which have been followed and/or
abandoned (Figure 3). Based on our example, exploration of neighbor arriving
routes could benefit from tree couples of edges which shape this node:
a = (e, f ) b = (d, f ) c = (d, e) d = (b, c)
c = (a, c) f = (a, b)
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e
a
f f
b b
d e
d
c
e
Fig. 4. Self-Similarity of the GRAPHYP Bipartite Graph
Here we have the grid of nodes which are linked by a specified common edge,
resulting from the position of each node on the grid. We could then propose the
mapping of a grid of correlated neighbor routes. The grid observed on Figure 4,
structured with the same nodes as the grid in Figure 3, offers the option of
projection that could be studied in a further work.
3.11 Navigation Rule 6: fractal scalability of searchable space
A final property of the bipartite graph that gave birth to GRAPHYP is self-
similarity, which is designed on the Figure 5 below.
The graph with nodes A, B, C, etc. is built on a larger dimension than
the preceding one, and the addition of these “compasses” on a self-similar basis
enables the building of information architectures of the same type for information
processing at any scale, and the application to the GRAPHYP operating frame
of basic addition, subtraction, multiplication and division operations.
Fig. 5. Searching at Various Query Scales : Fractal Scalability of GRAPHYP
Scalability of the system could thus be established as a final feature of de-
scriptive functionalities of the system. It could then be used as a tool connecting
approaches at various steps or in different scientific domains.
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4 Conclusions
The STI search community asks for accuracy and dynamic adaptation of deliv-
ered items, as highlighted by reviews of “observational” approaches to informa-
tion retrieval ([9]), and this requires a rich semantic search ([1]).
We propose mapping of recordable categories of typical search sessions, clus-
tered in user-item groups, which, together, shape what we called the “query
ecosystem”. Navigation in this system is organized for complete “searchability”
of a query, modeling alternative paths to answers. It raises several issues: first,
this new kind of a documentary object could help map “versioning” of search
sessions, and thus add a new piece of semantic analysis for identification of al-
ternative paths to answers, thus providing a “chronicle” of new incoming ideas
through the corresponding modeling of searches. The status of sharing and pri-
vacy relating to this modeling system is a highly important issue.
Further work will seek to test accuracy and fast identification of versioning of
search sessions; we will also check clustering at various scopes of fractal develop-
ment of search session mapping, which would be useful for interactions between
multipurpose searches (interdisciplinary, etc.) and the specific geometry of items
networking inside this type of data structure.
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