=Paper=
{{Paper
|id=Vol-162/paper-10
|storemode=property
|title=Using Formal Concept Analysis for Heterogeneous Information Retrieval
|pdfUrl=https://ceur-ws.org/Vol-162/paper10.pdf
|volume=Vol-162
|dblpUrl=https://dblp.org/rec/conf/cla/NafkhaJ05
}}
==Using Formal Concept Analysis for Heterogeneous Information Retrieval==
https://ceur-ws.org/Vol-162/paper10.pdf
Using Formal
Using FormalConcept Analysis
Concept Analysis
for for Heterogeneous Information
Heterogeneous Information Retrieval
Retrieval
Ibtissem Nafkha1, and Ali Jaoua2
Ibtissem Nafkha1 and Ali Jaoua2
1 University of Tunis, Department of Computer Science,
1
University of Tunis, Department of Computer Science,
Campus Universitaire, le Belvédère, 1060, Tunis, Tunisia.
Campus Universitaire,le Belvédère, 1060, Tunis, Tunisia.
ibtissem.nafkha@fst.rnu.tn
2
University of of
2 University Qatar,
Qatar,Faculty
Faculty of
of Sciences,
Sciences,
Department
Department of Computer Science, Doha,
of Computer Science, Qatar.
Doha, Qatar.
ibtissem.nafkha@fst.rnu.tn,
jaoua@qu.edu.qa jaoua@qu.edu.qa
Abstract. With the advent of the Web along with the unprecedented amount of
information coming from sources of heterogeneous data, Formal Concept
Analysis (FCA) is more useful and practical than ever, because this technology
addresses important limitations of the systems that currently support users in
their quest for information. In this paper, we will focus on the unique features
of FCA for searching in distributed heterogeneous information. The develop-
ment of FCA-based applications for distributed heterogeneous information re-
turns a major gain.
1 Introduction
The information systems these days manage, import, broadcast, exchange and inte-
grate big volumes of sometimes recorded data, often in different formats (documents,
cards, tables). With the internet development, the institutions are often confronted to
the manipulation and the analysis of important information volumes. These informa-
tions are often coming from heterogeneous data sources and are themselves of het-
erogeneous nature. Regarding this heterogeneity, the integration or the simple ex-
change of the data is not an easy task if the different intervening (producers or infor-
mation consumers) do not agree on the semantic of data. It is therefore very difficult
to research the answer to an information need in all bases.
In this direction, we are very interested in defining an approach that is focused par-
ticularly on the detection of the similar objects. Furthermore, the important volume
that occupies the heterogeneous data creates gaps and technical difficulties such as
pertinent information deficiency and the loss time for precise information research. In
this context, we propose an analysis and an interpretation approach of the similar
objects allowing jointly to realize a more effective research and to extract automati-
cally the information from the dispersed sets of heterogeneous data in the framework
of the cooperative work. Our approach is based on the formal concept analysis.
Radim Bělohlávek, Václav Snášel (Eds.): CLA 2005, pp. 107–122, ISBN 80–248–0863–3.
108 Ibtissem Nafkha, Ali Jaoua
So, this paper is organized as follows. In section 2, we introduce some basic defini-
tions on formal analysis. Then in section 3, we present the related work. Section 4 is
devoted to the presentation of proposed system for searching in heterogeneous infor-
mation. In section 5 and 6, we present the evaluation of our system.
2 Mathematical Foundations
Among the mathematical theories recently found with important applications in com-
puter science, lattice theory has a specific place for data organization, information
engineering, data mining and for reasoning. It may be considered as the mathematical
tool that unifies data and knowledge or information retrieval [1,4,7,10,18,20,23]. In
this section, we define formal context, formal concept, Galois connection and the
lattice of concepts associated to the formal context.
2.1 Formal Context
Definition 1. A formal context is a triple k = , where O is a finite set of ele-
ments called objects, P a finite set of elements called properties and R is a binary
relation defined between O and P. The notations (g,m), or R(g,m)=1, mean that "for-
mal object g verifies property m in relation R" [3,12].
Example 1. Let O = {a1, a2, a3, a4, a5, a6} be a set of person of different grade and P =
{b1, b2, b3, b4, b5, b6, b7} be a set of the properties. This context describes the profes-
sional qualifications verified by the persons set according to the binary relation R.
The
b1 b2 b3 b4 b5 b6 b7
a1 1 0 1 0 0 0 0
a2 1 1 0 0 0 0 0
a3 1 1 1 1 0 0 0
a4 1 1 1 0 1 0 0
a5 1 0 1 0 0 0 1
a6 1 1 0 0 0 1 1
Table 1. An example of a formal context.
2.2 Galois Connection
Definition 2. Let A ⊆ Ο and B ⊆ P two finite sets, R a relation on O x P. For both
sets A and B, operators f(A) and h (B) are defined as [12]:
f (A) = {m | ∀g, g ∈ A Æ (g,m) ∈ R}
Using Formal Concept Analysis for Heterogeneous Information Retrieval 109
h (B) = {g | ∀m, m ∈ B Æ (g,m) ∈ R}
Operator f defines the properties shared by all elements of A. Operator h defines
objects sharing the same properties included in set B. Operators f and h define a Ga-
lois Connection between sets O and P [12].
Proposition 1. Operators f and h define a Galois connection between O and P, such
that if A1, A2 are subsets of O, and B1, B2 are two subsets of P, then f and h verify
the following properties [12]:
- A1 ⊆ A2 ⇒ f (A1) ⊇ f (A2)
- B1 ⊆ B2 ⇒ h (B1) ⊇ h (B2)
- A1 ⊆ h o f (A1) and B1 ⊆ f o h (B1)
- A ⊆ h (B) ⇔ B ⊆ f (A)
- f = f o h o f and h = h o f o h
2.3 Formal Concept
Definition 3. A formal concept of the context is a pair (A,B), where A ⊆ Ο,
B ⊆ P, such f (A) = B and h (B) = A. Sets A and B are called respectively the do-
main (extent) and range (intent) of the formal concept [3,12].
2.4 Concept Lattice
Definition 4. From a formal context , we can extract all possible concepts. In
[12], we prove that the set of all concepts may be organized as a lattice, when we
define the following partial order relation << between two concepts, (A1,B1) <<
(A2,B2) ⇔ (A1 ⊆ A2 ) and (B2 ⊆ B1). The concepts (A1,B1) and (A2,B2) are called
nodes in the lattice.
2.5 Objects Similarity
The object similarity can be envisioned according to two view points:
- The semantic view point: the objects are similar if they have commons properties,
- The system view point: to take into account the object model have vector model.
110 Ibtissem Nafkha, Ali Jaoua
Semantic Similarity. Definition 5. Let k= a formal context, O is object set,
P is properties set and R is the binary relation between O and P. The similarity
between two objects a and b is considered the commons properties. Let a and b two
elements of O, Pa the verifying properties by the object a and Pb the verifying
properties by the object b. The commons properties between two objects a and b
forms the set Pa∩Pb. The similarity between two objects is calculated with the
following formula [23]:
Pa ∩ Pb
Similarity (a, b) =
Pa ∪ Pb (1)
The similarity is a value in the interval [0,1]. In our system, we use this formula in
order to detect the similar documents.
Example 2. Let two formal contexts, presented in table 2, defined respectively be-
tween 5 objects {O1, O2, O3, O4, O5} and three properties {A, C, D} and between four
objects {O6, O7, O8, O9} and four properties {A, B, C, E}.
A C D
O1 1 1 1 A B C E
O2 1 1 0 O6 0 1 1 1
O3 1 0 1 O7 1 1 1 0
O4 1 0 0 O8 1 1 0 1
O5 1 1 1 O9 0 1 0 0
Table 2. Formal context example.
The object O1 is similar to the object O6 with similarity degree equal to 0.2. Indeed,
objects O1 and O6 verify in five different properties which one is common. The simi-
larity between O1 and O6 is:
Similarity (O1, O6) = 1 / 5 = 0.2
System similarity. In order to measure the similarity between two objects a and b, it
necessary to take in consideration the different object models. For this reason, we
present only the similarity calculation between two objects in the vector seen model
the complexity of the others model. [11,21,22,24,25,26,27]
Definition 6. The similarity between two objects a and b in the vectorial model [24,
25, 26] is measured as the angle cosines between two vectors presenting those ob-
jects.
a.b
Similarity (a, b) = cos( a , b ) = (2)
a.b
Using Formal Concept Analysis for Heterogeneous Information Retrieval 111
Object Similarity Choice. We mention that the object similarity value whatever the
view point system or semantic is a value in the interval [0,1].
This object similarity criterean may crold two values: two objects may seen alike or
different from each other. So, we determine two sets Sim_objet and
Dis_objet according to similarities and dissimilarities of an object with an object a:
Sim_objet (a) = { b ; Similarity (a,b) >= αsim }
Dis_objet (a) = { b ; Similarity (a,b) < αsim }
where αsim is the threshold that determines the object notion near or distant. In our
work, this threshold is provided by the user. The given value of the research session
means that the user accepts the similar answers with this degree. Seen that we use the
concepts formal analysis as basic foundation of our research approach, we do not
consider the similarity from the system view point but we are very interested in the
similarity from the semantic view point.
3 Related Work
Using FCA can complement the existing search systems to address some of their
main limitations. Basically, FCA exploits the similarity between documents in order
to offer an automatic support structure (i.e., the document lattice) in which we place
the information retrieval process. The document lattice can be used to improve basic
individual search strategies [1,2,4,13]. Moreover, query refinement is one of the most
natural applications of concept lattices. Its main objective is to recover from the null-
output or the information overload problem. The concept lattice may be used to make
a transformation between the representation of a query and the representation of each
document [5,6,7,8,9]. The query is merged into the document lattice and each docu-
ment is ranked according to the length of the shortest path linking the query to the
document concept. On the other hand, in the set of terms describing the document,
there exist hierarchies in the form of thesaurus [4,10,13,14]. The information search
using FCA takes as input a query that will be forwarded to a selected search engine
[6,7,8]. The first pages retrieved by the search engine in answer to the query are col-
lected and parsed. At this point, a set of index units that describe each returned docu-
ment is generated; such indices are next used to build the concept lattice correspond-
ing to the retrieved results. The last step consists in showing the lattice to the user and
managing the subsequent interaction between the user and the system. In spite of such
limitations such as for larger information collection, generally we get a huge number
of reference, we are interested in building a FCA-based system for distributed infor-
mation, which may affect both the efficiency and the effectiveness of the overall
system [18,19,20]. These systems suppose that a same document is identified in same
manner that presents a strong hypothesis. In order to reduce this constraint, we pro-
posed a similar object detection method. While basing itself on this last one, we have
defined a cooperative system of heterogeneous information retrieval HIC2RS that will
be described in the next section.
112 Ibtissem Nafkha, Ali Jaoua
4 Cooperative Conceptual Retrieval System for Heterogeneous
Information
We present in this section the cooperative research for heterogeneous information.
While considering the formal concept analysis as mathematical foundation, we pro-
pose an heterogeneous information conceptual cooperative retrieval system HIC2RS,
as illustrated in figure 1, that is composed of two parts:
1) The first part is the cooperative information retrieval system handling lo-
cal databases. The search of the answer to a query consists in applying a
research conceptual approach on every local database. As a result, we will
have concepts set forming the content of a Response vector.
2) The second part is the final answer formulation that operates in two steps :
i) Similar objects detection based on the Response vector and on the lo-
cal databases set, and
ii) The concepts merger based on the similar objects set and operated ac-
cording to the similarity threshold given by the user in order to offer the
final answer.
Fig. 1. Heterogeneous Information Cooperative Conceptual Retrieval System Architecture.
4.1 Cooperative Information Retrieval System
The first part of the system HIC2RS is formed of information retrieval systems set
that cooperate to give the complete answer to a query. Every information retrieval
system has access to a local database on which it applies the Galois connection to
rediscover the satisfactory documents query. This last one is keywords set. To resolve
a query (Qr), every conceptual information retrieval system executes the research
algorithm, presented in the following, on its local database (LD). This application
gives us concepts set forming the Response vector (RV).
Using Formal Concept Analysis for Heterogeneous Information Retrieval 113
Algorithm Research
Inputs: Query: Qr
Local database: LD
Output: Response Vector: RV
Begin
M := the keywords of LD.
Ml := M ∩ Qr
RV contains the concept obtained by Galois connec-
tion application on M1.
End
4.2 Final Answer Formulation
In this section, we present the second part of the system HIC2RS that is the final an-
swer formulation. The final answer formulation is carried out in two steps: i) the
detection of the similar objects of the Response vector, and ii) the merger of the dif-
ferent answers based on the Response vector and on the similar objects. The final
answer formulation consists in the application of the algorithm Merge_IH that we
propose on the Response vector basing on the query and on the similarity threshold to
have the final answer.
Similarity Objects Detection. The similar objects detection consists in examine the
documents that figure in the Response vector and calculating the similarity between
them. From the concepts, we create a similar objects set. This set contains the similar-
ity degrees between the different documents. The similarity degree calculation be-
tween two documents is based on the formula (1) defined in section 2.5. In fact, seen
that our system is based on the terminologies of the concepts formal analysis, it is
useless to use the similarity from the system view point that depends on used model
to present and search the information such as the vectoriel model. While taking ac-
count of the keywords number of every document and the number of common key-
words between them, the similarity degree between two documents is calculated.
Answer Merge. Basing on the calculated similarity degrees as well as on the Re-
sponse vector concepts, we formulate the final answer to the query. The merger is
based on algorithm Merge_IH that we propose by the continuation.
This merger algorithm combines the Response vector concepts while respecting cer-
tain conditions. We construct the final answer in a repeated way. Initially, the final
answer is an empty set. We treat the concepts set element by element.
For every element, if the keywords (the extension) of the concept are different of
those of query, we add then the documents (his intention) to the final answer. If this
condition is not satisfied, we search the similar documents to those of other concepts
of the Response vector (the intention) verifying the threshold similarity, and we cal-
culate the union of the extensions (to obtain the under together keywords). We con-
tinue to construct these sets of similar documents until we find all the query key-
words.
114 Ibtissem Nafkha, Ali Jaoua
This algorithm has as entry the query, the similarity threshold and the Response vec-
tor and as a result the final answer.
Algorithm Merge_IH
Inputs : Query: Qr
Response Vector RV: a concepts set C1 .. CN
Threshold similarity: S
Output : Final answer: FA
Begin
FA := ∅•// initialize the final answer
For each concept Ci of RV do
If extent of concept Ci = Qr then
Add the intent of Ci to FA
Else
While exist a concept Cj (j > i) do
- Initialize P by the extent of Ci
- Initialize D by the intent of Ci
While (P <> Qr and exist a concept Cj) do
- Add the extent of Cj to P
- Search the similar documents, with
the threshold S, between the intent of
the concept Cj and the elements of D:
D := Similar (D, intent_ Cj,S)
- Pass to the next concept
End do
If (P = Qr ) then
Add D to FA : FA :=FA ∪ D
End if
End do
End if
End for
End.
The similar function consists in looking the similar objects with a similar threshold in
two objects sets. This research is based on the similar objects set found at the time in
the phase of the similar objects detection. We keep only the objects having a
similarity degree greater than the similarity Threshold. The function is described in
the following and it has as inputs two objects sets A1 and A2 and a similarity
threshold α and as output the set A3.
Function Similar
Inputs:
Objects sets: A1, A2.
Similarity Threshold: S
Output: Objects set: A3
Begin
A3 := Ø
For each object di of A1 do
Using Formal Concept Analysis for Heterogeneous Information Retrieval 115
For each object dj of A2 do
- Calculate the similarity between two objects di
and dj :
Pdi ∩ Pd j
α :=
Pdi ∪ Pd j
- If α>=S, add objects di and dj and the similarity
α to A3.
End if
End for
End for
Return (A3)
End.
4.3 Illustrative Example
We take an illustrative example to show the HIC2RS system functionalities. Let the
databases presented in tables 2, 3 and 4. These databases describe documents set
indexed by a keywords set. For the query: "Which documents indexed by the key-
words M2, M3 and M4 having a similarity Threshold 0.33", the query is formed by
three keywords M2, M3 and M4. The treatment of this query is carried out in two
steps.
- Step 1 : Cooperative Research
The research principle is explained in figure 2.
Fig. 1. Cooperative Information Retrieval System.
116 Ibtissem Nafkha, Ali Jaoua
Every conceptual information retrieval system applies algorithm retrieve on its local
database. The Galois connection application on the query keywords sets existing in
the first database presented in table 3 and the query (M1 = {M2,M3}) gives the docu-
ments set {D1}. The found concept is then ({M2,M3}, {D1}).
For the second local database presented in table 4, the Galois connection application
for the keywords M2 and M4, the common found keywords between the local data-
base keywords and those of the query, we give the documents {D6, D9}. So, the result
for this local database is formed by the concept ({M2,M4}, {D6,D9}).
The third local database presented in table 5 contains the keywords M3 and M4. While
applying the Galois connection, we find the documents set {D10,D13}. Thus, the con-
cept ({M3,M4}, {D10,D13}) is the result of this research.
We obtain three concepts from different local databases that we find in the Response
vector presented by the table 6.
1 2 3
M2 M3 D1 M2 M4 D6 D9 M3 M4 D10 D13
Table 3. TheResponse vector.
Basing ourselves on this vector, we construct the final answer.
- Step 2: Final Answer Formulation
The final answer formulation is realized in two phases: similar objects detection and
the answers merger.
Similar objects detection : The Response vector contains three concepts that we ex-
amine one by one. The first concept contains the document D1. We calculate then the
degree of similarity between this document and every document existing in the two
other concepts that are D6, D9, D10 and D13. The same treatment is carried out on the
document D6. We calculate the similarity degree between D6 and D10 then between D6
and D13. The same treatment is done on the document D9. The degrees of calculated
similarities are the following ones:
Similarity(D1, D6) = 1/3 =0.33; Similarity (D1, D9)= 1/4 = 0.25;
Similarity (D1,D10) = 1/3 = 0.33; Similarity (D1,D13) = 1/3 = 0.33;
Similarity (D6,D10) = 1/3 = 0.33; Similarity (D6,D13) = 1/3= 0.33;
Similarity (D9,D10) = 1 / 4 =0.25; Similarity (D9,D13) = 1/4 = 0.25;
Answer Merge : We remind that our query is {M2,M3,M4} and the similarity threshold
is 0.33. Initially, the final answer is an empty set. We treat the first concept of the
Response vector. Its keywords are different from the query. So, we merge those key-
words with those of the second concept and we search the similar documents. The
result of this research is the documents set {D1,D6}, considering that the documents
D1 and D6 are similar with the degree 0.33, and that the keywords union is the set
{M2,M3,M4} that is equal to the query. The similarity between D1 and D9 is equal to
Using Formal Concept Analysis for Heterogeneous Information Retrieval 117
0.25 that is less than the similarity threshold. So, we ignore D9 and we add the found
documents to the final answer. At this step, the final answer is the set {D1,D6}.
Then, we calculate the union of the keywords and the similar documents between the
first and the third concepts of the Response vector. The merge result is the set
{M2,M3,M4} that is equal to the query. We remark that the documents D10 and D13 are
similar to D1 and to D6 with the degree superior to 0.33. So, we add those documents
to final answer that becomes {D1,D6,D10,D13}.
Thus, we continue with the next concept. We merge the keywords of the second and
the last concepts. The result is the set {M2,M3,M4}. The similar documents are
{D6,D10,D13} that we add to the final answer. The final answer is now the set
{D1,D6,D10,D13} that will be delivered to the user.
Remark 1: If we take for example a similarity threshold equal to 0.8, our system re-
turns an empty answer. This answer explains oneself by the fact that there doesn’t
exist similar objects for this degree. As opposed to the threshold equal to 0.2, the final
answer is then composed by all documents forming the Response vector. This can be
explained by the fact that the similarity degrees between the different documents are
greater than the given value. Thus, our approach considers that the documents set
represent the same knowledge and we evade late the empty answers.
5 Complexity Analysis
In order to evaluate the system HIC2RS, we calculate the temporal and the spatial
complexities.
5.1 Temporal Complexity
We suppose that a database has n objects and m properties and we dispose of k local
databases.
We recall the steps of our system HIC2RS:
- Phase 1: the concepts research from the different local databases.
- Phase 2: the similar objects detection and the merge of k found concepts.
The temporal complexity CT of the system is then:
CT = CPhase 1(n,m,k) + CPhase 2(n,m,k)
The phase 1 needs k×n×m operations and the phase 2 needs k×(k-1)/2+(n×k) opera-
tions. So, the temporal complexity is: CT = k×n×m+k×(n+1)+(n×k) = (k×n×m)+(k2-
118 Ibtissem Nafkha, Ali Jaoua
k)/2+n×k ≈ O(k×n×m+k2) operations. The temporal complexity of the system HIC2RS
is then in order of O(k×n×m+k2) operations.
5.1 Spatial Complexity
The system HIC2RS uses k matrix of n lines and of m columns, a vector of k elements
as well as a square of dimension n. The system reserves thus (k×n×m)+k+(n×n) mem-
ory cases. So, the spatial complexity of the system HIC2RS is equal to: CS = (n× m×
k+ k+n2).
6 Evaluation
The system HIC2RS treats heterogeneous information. Indeed, to remedy the problem
of the existence of different identifications for similar or identical documents, we
proposed a similar objects detection method during the cooperative information re-
trieval process. The implementation of this system consists first of in fragmenting a
test collection and next in releasing the retrieval process while supposing that a same
document can have different identifications. This hypothesis is based on unit similar
objects detection. The experiment was conduced on CRAN and MED collections.
The CRAN collection (Cranfield collection) includes a textual corpus that has a size
upper than 1.6Mo. This collection contains 1400 documents and 4612 different terms
and it is tested on 225 queries. The MED collection includes a textual corpus that has
a size upper than 1.1Mo. It contains 1033 scientific articles extracted from the medi-
cine database domain and 5831 different terms and it is tested on 30 queries. With
experiments done on the MED and CRAN test collections, we noticed that the final
quality of retrieval improved in term precision and recall that in term answer times.
The figure 3 illustrates the precision and recall graph of the MED test collection for
the system treating homogenous information CIRS and HIC2RS.
1
0,9
0,8
0,7
0,6
HIC2RS
0,5
CIRS
0,4
0,3
0,2
0,1
0
0,08 0,11 0,21 0,31 0,333 0,5 0,6 0,75 0,8 0,9 1
Recall
Using Formal Concept Analysis for Heterogeneous Information Retrieval 119
Fig. 2. Precision and recall graph for the MED test collection.
We note, according to figure 3, that for the MED test collection, the measure of aver-
age precision has 11 reminder points for the system treating information homogenous
(CIRS) is in the order of 43.9%. While, for the system HIC2RS treating information
heterogeneous is on the order of 46.7%. Thus, the similar object detection integration
gives an improvement of average precision on the order of 6.4%.
All the same, experimentations done on the CRAN test collection fragmented showed
an improvement of average precision of the CRAN test collection on the order of
7.5%. (figure 4).
1
0,9
0,8
0,7
0,6
HIC2RS
0,5
CIRS
0,4
0,3
0,2
0,1
0
0,1667 0,2 0,25 0,3333 0,4 0,5 0,6 0,6667 0,8333 1
Recall
Fig. 3. Precision and recall graph for the CRAN test collection.
The figure 5 shows that HIC2RS treats different MED test collection queries faster
than the conceptual information retrieval system.
450
400
350
300
250
CIRS
HIC2RS
200
150
100
50
0
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30
Query
Fig. 4. The answer time take by the systems CIRS and HIC2RS for the MED test
collection.
Of even for the CRAN test collection, the answer time take by HIC2RS is lower than
the one take by the system treating information homogenous (to see figures 6).
120 Ibtissem Nafkha, Ali Jaoua
450
400
350
300
250 CIRS
200 HIC2RS
150
100
50
0
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29
Query
Fig. 5. The answer time take by the systems CIRS and HIC2RS for the CRAN test
collection.
7 Conclusion
We presented in this paper a conceptual cooperative retrieval system for heterogene-
ous information (HIC2RS). Being given a heterogeneous environment constituted by
a set of information retrieval systems handling each a local database, our approach
allows soliciting these databases in order to have a complete answer to a user query.
In fact, after a query and according to a similarity threshold given by the user, our
system releases conceptual research processes on the different local databases and it
will have as a result a concepts set. Basing on this concepts set and on the similarity
threshold, the system formulates the final answer that it delivers to the user. The simi-
lar objects detection method, that we defined, enriched the returned answers of differ-
ent databases. This method improved average precision of 6.4% for the MED test
collection and of 7.5% for the CRAN test collection.
References
1. Aboud M., Chrisment C., Razouk R., Florence S., Soulé-Dupuy, Query a Hypertext Infor-
mation Retrieval System by use of Classification. Information Processing and Management,
29(3), (1993) 387-396.
2. Amati G., Carpineto C., and Romano G., FUB at TREC-10 Web Track: A
Proabilistic Framework for Topic Relevance Term Weighting. In Proceedings of
the 10th Text REtrieval Conference (TREC-10), NIST Special Publication 500-
250, Gaithersburg, MD, USA (2001) 182-191.
3. Bordat J.P., Calcul pratique du treillis de Galois d'une correspondance. Math. Sci.
Hum., 96, (1986) 31-47.
Using Formal Concept Analysis for Heterogeneous Information Retrieval 121
4. Carpineto C. and Romano G., Using Concept Lattices for Text Retrieval and Min-
ing. In the 1st International Conference on Formal Concept Analysis, Darmstadt,
Germany, (2003).
5. Carpineto C. and Romano G., Information retrieval through hybrid navigation of
lattice representations. International Journal of Human-Computer Studies, 45(5),
(1996) 553-578.
6. Carpineto C. and Romano G., A lattice conceptual clustering system and its appli-
cation to browsing retrieval. Machine Learning, 24(2), (1996) 1-28.
7. Carpineto C. and Romano G., Effective reformulation of Boolean queries with
concept lattices. In Proceedings of the 3rd International Conference on Flexible
Query-Answering Systems, pages 83-94, Roskilde, Denmark, 1998.
8. Cole R. and Eklund P., Browsing semi-structured web texts using formal concept
analysis. In Proceedings of the 9th International Conference on Conceptual Struc-
tures, Stanford, CA, USA, (2001) 319-332.
9. Efthimiadis E., Query expansion. In M. E. Williams, editor, Annual Review of
Information Systems and Technology, v31, American Society for Information Sci-
ence, Silver Spring, Maryland, USA, (1996) 121-187.
10. Ferrfie S. and Ridoux O., A file system based on concept analysis. In Proceedings
of the 1st International Conference on Computational Logic, London, UK, (2000)
1033-1047.
11. Fuhr and C. Buckley, A probabilistic learning approach for document indexing,
ACM Transactions on Information System 9, 19991, N°3, pages 223-248.
12. Ganter B. and Wille R., Formal Concept Analysis - Mathematical Foundations.
Springer, 1999.
13. Godin R. and Mili. H., Building and Maintaining Analysis Level Class Hierar-
chies Using Galois Lattices. In Proceedings of the 8th Annual Conference on Ob-
ject Oriented Programming Systems Languages and Applications, Washington,
D.C., USA, (1993) 394-410.
14. Godin R., Missaoui R., and April A., Experimental comparison of navigation in a
Galois lattice with conventional information retrieval methods. International
Journal of Man-Machine Studies, 38: (1993) 747-767.
15. Godin R. , Saunders E. , and Jecsei J., Lattice model of browsable data spaces.
Journal of Information Sciences, 40: (1986) 89-116.
16. Jaoua A., Bsaies Kh., and Consmtini W., May reasoning be reduced to an Infor-
mation Retrieval problem. Relational Methods in Computer Science, Quebec,
Canada, (1999).
17. Jaoua A., Al-Rashdi A., AL-Muraikhi H., Al-Subaiey M., Al-Ghanim N., and Al-
Misaifri S., Conceptual Data Reduction, Application for Reasoning and Learning.
The 4th Workshop on Information and Computer Science, KFUPM, Dhahran,
Saudi Arabia, (2002).
122 Ibtissem Nafkha, Ali Jaoua
18. Nafkha I., Elloumi S. and Jaoua A., Conceptual Cooperative Information Re-
trieval System. In International Arab Conference on Information Technology,
Doha December 16-19, Qatar, (2002) 534-539.
19. Nafkha I., Elloumi S. and Jaoua A., Conceptual Information Retrieval System
based on cooperative conceptual data reduction. 1St International Conference on
Information & Communication Technologies : from Theory to Applications, Syria,
(2004).
20. Nafkha I., Elloumi S., Jaoua A., Using Concept Formal Analysis for Cooperative
Information Retrieval. Concept Lattices and their applications Workshop
(CLA’04), VSB-TU Ostrava, September 23th-24th, 2004.
21. Rijsbergen C.J. Van, A non-classical logic for information retrieval. The Com-
puter Journal 29, 1986, N 6, pages 481-485.
22. Rijsbergen C.J. Van, A new theorical framework for information retrieval. Pro-
ceeding of the 1986-ACM Conference on Research and Development in Informa-
tion Retrieval, 1986, pages 194-200.
23. Salton G., Automatic Text Processing: The Transformation, Analysis, and Re-
trieval of Information by Computer. Addison Wesley, 1989.
24. Salton G., A. Wang and C. S. YANG, A vector space model for automatic index-
ing, Communication of the ACM 18, 1975, N°11, pages 613-620.
25. Salton G., Improving Retrieval Performance by Relevance Feedback. Journal of
the American Society for Information Science 41, 1990, N°4, pages 288-297.
26. Salton G. and Buckley C., Improving retrieval performance by relevance feed-
back. Journal of the American Society for Information Science (JASIS). Vol.41,
N°4, pages 288-297, 1990.
27. Waller G. W. and Kraft D.H., A mathematical model of a weighted Boolean re-
trieval system. Information Processing and Management (1997), N°15, pages
235-245.