Comparative Study on Graph-based Information Retrieval: the Case of XML Document

International Journal of Advanced Engineering, Management and Science
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ISSN: 2454-1311 | Vol-7, Issue-8; Aug, 2021
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Comparative Study on Graph-based Information
Retrieval: the Case of XML Document
Imane Belahyane1
, Mouad Mammass2
, Hasna Abioui2
, Assmaa Moutaoukkil3
, Ali
Idarrou2
IRF-SIC Laboratory, Ibn Zohr University, Agadir Morocco
Received: 11 Jul 2021; Received in revised form: 04 Aug 2021; Accepted: 12 Aug 2021; Available online: 20 Aug 2021
Abstract— The processing of massive amounts of data has become indispensable especially with the
potential proliferation of big data. The volume of information available nowadays makes it difficult for the
user to find relevant information in a vast collection of documents. As a result, the exploitation of vast
document collections necessitates the implementation of automated technologies that enable appropriate
and effective retrieval.
In this paper, we will examine the state of the art of IR in XML documents. We will also discuss some
works that have used graphs to represent documents in the context of IR. In the same vein, the
relationships between the components of a graph are the center of our attention.
Keywords— Information Retrieval, graph relations, graph-based approach, XML document, XML
document retrieval.
I. INTRODUCTION
In the context of BigData, The continual spread of digital
multimedia documents necessitates the updating of the
many existing technologies used to exploit the available
digital mass. Consequently, locating information is a
process of matching the query expressing the user's need
with the documents in the documentary database.
In a digital world, where the number of documents grows
exponentially, studies have shown that more than 80% of
the organizations and businesses’ data is in the form of
documents, compared to less than 20% in traditional
databases. Our research operates within the framework of
the Information Retrieval (IR) in semi-structured
documents (SSD). Indeed, the SSD has organizational
properties that facilitate their analysis.
In the literature, the XML document (eXtensible Markup
Language) is a documentary standard, which is generally
qualified as a semi-structured document. Indeed, the XML
document is quickly becoming the ultimate format for
exchanging documents - and, more typically, information.
It is currently utilized by ERP providers, middleware
editors, database providers, and in e-commerce and
libraries. To retrieve information from a document corpus,
a representation model that is appropriate for the type of
document is required. The more sophisticated the
modeling of the documents will be, the more relevant but
difficult the comparison of the documents will be[1] . The
standard structuring languages such as XML and their
derivatives allow serializing a tree structure of a document
in the same file. As a result, the DSS must be represented
by a graph that measures the many relationships between
the components of the XML document and prioritizes the
semantic and contextual information conveyed.
The rest of the paper is organized as follows: In section 2,
we will describe the principle of Document Information
Retrieval, by establishing a state of art on the works that
addressed the issue of Information Retrieval in XML
documents. In section 3, we will highlight the types of
relationships that exist between the components of the
graph. In section 4, we will analyze the relevance of the
structural aspect, specifically the relations of a graph, as
well as the advantages and limitations of the many types of
relations that exist between the components of the graph,
to determine which would best serve our context.

Imane Belahyane et al. International Journal of Advanced Engineering, Management and Science, 7(8)-2021
II. INFORMATION RETRIEVAL IN XML
DOCUMENTS
2.1 Introduction
Information Retrieval is a field of computer science that
deals with the acquisition, organization, storage, search
and selection of information [2], That system can locate
precisely documents related to a query expressing a user’s
need.
Globally, the classic steps of an IRS are the indexing
phase, the representation phase, and the comparison phase.
During the indexing phase, the system unifies the coding
of the documents in the document collection and organizes
the collection. At the level of the document representation
phase, documents are represented by a model that
synthesizes them as much as possible. In the comparison
phase, the system compares the query to all of the
documents in the collection. It does so by providing a
matching mechanism between the user query and the
documents, or more particularly between the query
representative and the document representatives, the
system compares the query to all of the documents in the
collection.
IR is a promising field that has been growing steadily since
the 1990s. Since then, new approaches have been created,
particularly in terms of the information available on the
web. These SRIs are normally based on textual or
multimedia content or the use of external resources
(semantics). They allow the query to be matched to
documents without taking the structural aspect into
account. This, of course, excludes the option of including
the relationships between the entities in a document. The
structural aspect is primarily based on a tree-like document
representation. It allows the documents to be structured in
such a way so that the various relations of a document
representation are highlighted.
In the following subsection, we survey a literature review
about different publications, which address IR in XML
documents.
2.2 Information Retrieval in XML documents
The goal of an IRS is no longer to deliver an exact
response to the user's request, but rather to provide the user
with a set of results arranged by relevance. Querying a
collection of XML documents means comparing the query
with all the XML documents in the document database.
Indeed, the XML document is a semi-structured document
by essence. Therefore, one of the major problems of IR is
to be able to compare two documents by taking into
account the content, the structure, and the semantic aspect.
In our previous paper, we discussed two categories of
document representation: dependent and data-independent
structures.
In what follows, we present the body of work that has
addressed the problem of IR in XML documents. The
work [3] provides an information retrieval paradigm based
on a similarity metric to rigorously compare XML
documents. It sets a comparison framework of XML
document structure based on the commonality of subtrees
and the semantics of labels. [4] Performs IR in XML
documents using the notion of tree tuples to semantically
identify consistent substructures. The work [5] addresses
the IR problem at the structural level by path matching
between the XML document and the query representing
the user need. In terms of content similarity, a matrix is
generated utilizing artificial language processing
techniques to compute the similarity between the
keywords. For the calculation of semantic similarity, the
approach relies on fuzzy matching.
In [6], the proposed approach is based on edit distance,
which takes into account the content's structural and
textual similarities. The suggested structural similarity
algorithm merges the set of DTDs (Document Type
Definition). This latter comprises the document collection
into an undirected graph by employing the edit distance
method and the shortest path method [7]. The structural
similarity is computed preterially between the subtree of
relevant nodes S and the query tree. The extraction of the S
subtree starts with a selection phase of the relevant leaves.
Paths are extracted based on the existing nodes in the
query, from the root to the leaf. These relevant node paths
are merged into a subtree. These steps reduce the size of
the subtrees and increase the efficiency of the proposed
model because the time of the edit distance path strongly
depends on the cardinality of the input trees. In [8], the
approach used for IR is based on two scores. The first one
(content score) is propagated at the tree level in order to
obtain the sub-trees containing the relevant leaves; the
score is computed by a weighting algorithm of the form tf-
idf. The second (structure score) computes the score of the
subtrees previously extracted by the tree editing distance
algorithm.
In the next part, we offer a state-of-the-art review of
publications that have employed graphs for IR objectives.
III. GRAPH-BASED INFORMATION RETRIEVAL
3.1 Preamble
The classical graph theory problem can be described as
follows: Given a graph database and a
graph representing the query . Finding all the graphs in

which is a subgraph, is equivalent to finding the
suitable match between the query and the [1].
Graphs have been widely instrumental in the case of
complex document representation and used in a variety of
fields owing to the graph's vital function in increasing the
meaning of the document represented.
Our previous research [9] has concentrated on aspects that
contribute semantics to graph-based IR methods in the
context of images.
Using graph theory to solve IR issues entails taking
structural, contextual, and semantic factors into
consideration. The combination of these aspects increases
the accuracy and best meets the need described in the
query. Furthermore, the graph's flexibility allows for the
modeling of multiple relationships between the same
nodes.
The study [10] presents a method applied at the indexing
phase, in order to extract sub-graphs for IR purposes. The
adapted approach consists of matching the size of the
query to the size of the sub-graphs utilized to create the
index. [11] uses a graph-based approach for enhanced
bibliographic retrieval to a co-citation network
incorporating citation context information; the method is
based on a graph similarity calculation algorithm and the
Random Walk with Restart (RWR) algorithm. The authors
of [12] describe and structure the events of a document in
order to build the text summary. The method consists in
building the event graph by combining machine learning
and rule-based methods. This extractive multi-document
summarization approach chooses sentences based on the
significance and temporal structure of events.
Graphs are made up of vertices and edges. Edges are
responsible for combining and linking the vertices. The
relations in the graph express the relation of membership
or typing [1]. In [13], the proposed model is based on the
graph which is an algebraic model closely related to the
vector space model. Each vector coordinate is a value that
expresses the significance of the term in the document or
query. A bipartite graph is used in [14] to represent the
documents with the indexed terms in the document
collection. The link reflects the relationship between the
document and its own indexed terms. Work [15] proposes
a technique for bibliographic retrieval by an interface of
interrogation for documentary bases by natural language,
the structuring of the request and the documents is based
on graphs.
[15] Presents a technique for bibliographic retrieval which
uses an interface of interrogation for documentary bases
and relies on natural language. On that regard, the
structuring of the request and the documents is based on
graphs.
In the next section, we discuss within the context of IR the
relationships between the components of a graph.
3.2 Relationships between the components of the
graph
A graph G is a set of nodes connected by links called
edges. Indeed, an edge can carry information about the
direction of navigation from one node to another, typing
information or a content serving the user need. Moreover,
the information carried by the link differs according to the
context and the objective of the study. In the context of
social networks, edges can express connections, friendship
links between individuals. Edges on the Internet might
indicate wire or wireless connections between computers
or routers. As for the web, edges can reflect the hyperlinks
between web pages. In rail networks, edges can be used to
express connections between stations. As for road
networks, edges can represent the road segments between
its intersections. At documents, edges convey the
relationship between the document and its own indexed
terms or sometimes they express the link between nodes
that correspond to the distance between documents, etc.
Following that, we divide a graph's relations into three
categories: grammatical relations, string relations, and
numerical relations.
- Grammatical relations
The vertices of a graph representing a document depend on
the words or morphemes of a text. Syntactic functions
produced by a dependent grammar are often used to
designate such edges. In the literature, grammatical
relations are used to structure a textual document in order
to build a hierarchical structure that can be browsed and
analyzed. In [16], the text is structured using a dependency
parsing process. Grammatical relations, according to the
same paper, are intended for sentence identification and
syntactic structure creation. They play an essential role in
the semantic analysis phase [17]. Independent grammar is
a process that determines the type of dependent
relationship that exists between the terms in the document.
The work [15] proposes a method of bibliographic
retrieval which uses a query interface for documentary
databases and relies on natural language. In the same vein,
a parser is used to structure the query and the documents,
allowing it to display the many grammatical relations that
link the text fragments. It is worth mentioning that this
type of relationship can only be applied to IR in the text.
- String relations
The String relations include typing or membership
information and describes the link's characteristics.

Moreover, this type of relation enriches the semantics of
the document. Because of its great expressiveness, this
type of relationship is often utilized in image and video IR.
In the same context, this type of connection can express
spatial and temporal relations. Allen relations [18] allow to
structure the content of audiovisual sequences according to
temporal information. Indeed, [18] identifies a complete
set of temporal relations which can exist between two
intervals. In [12], document is represented as an event
graph where a graph representation involves not only the
recording of events, but also the representation of temporal
connections.
The work [19] illustrates the spatial interactions that
govern the relationships between the image's parts. The
scenes are represented by relational graphs that include
information on area types and spatial layouts. The study
[1] focuses on a case-based reasoning application in the
field of CAD (Computer Aided Design), where cases are
design items represented by directed label graphs. The
work [20], [21] and [22] use spatial and temporal relations
in the field of image IR.
- Numerical relations
Numerical relations are used in all fields of IR. Their basic
idea is to provide a numerical value to each edge in the
graph, called edge weight. The weight of an edge is
computed either by a weighting function, or fixed
according to the needs of the approach. The weighting
function is a mathematical expression that is used to
calculate sums, integrals, or averages in which certain
components are more important or influential on the same
set than others. In [23], the weighting function is used in
the context of multi-structured documents for document
classification purposes. The proposed weighting function
expresses the constraints related to hierarchical or
contextual. In other words, it expresses the distribution of
these components in the graph and the nature of the
relations between these components. In [8], the weight of
an edge, abbreviated as structure score, is obtained by
combining all the scores of the editing distance of the
subtrees. In [5], the weight of an edge positioning in a i
hierarchy is defined as . This work is applied to an XML
document hierarchy modeling for IR purposes. In [24], the
nodes of the tree correspond to the XML documents and
the relationship between the nodes correspond to the
distance between the documents. Therefore, the
relationship between the nodes of the structure ensures the
preservation of the order. In the same regard, the tf-idf
paradigm is also used to weight the relationships of the
graph.
The method followed in [25] is based on a summary of the
tree, in which a collection of vectors is retrieved by
sequentially reducing the structure and aggregating the
leaves containing the text. The weighting function is used
in the same way as tf-idf-edf [26] to assign a weight to
each node and edge that reflects its relevance in the
collection to which it belongs. [10] presents a model for
presenting terms as nodes and the number of occurrences
of terms as the relationship between nodes.
IV. DISCUSSION
In this paper, we have reviewed some work that have
engaged with IR in XML documents. We also discussed
several studies in which graphs have been utilized to
represent texts in the setting of IR. As we proceeded, we
conducted research on the many sorts of connections that
occur between entities. Indeed, the graph is made up of
nodes and the connections between them. The relations
constitute the backbone of the graph since they make
explicit the nature of the link and add contextual, structural
and semantic information.
Table 1 summarizes the advantages and limitations of each
type of relationship.
Table 1: Advantages and limitations of each type of relationship
RELATIONS TYPE ADVANTAGES LIMITS
String Relations
• Well expression of characteristics
• Expression of temporal and spatial relations
• Expression of semantic relations
• Applicable in all areas of IR
• Order not taken into account
• High complexity
Grammatical
relationships
• The parser is well defined
• Appropriate complexity
• Multiple link types generation
• Semantic expression support
• Order not taken into account
• Poor information is carried by the
relationship
• Not applicable in image and video

retrieval
Numeric
relationships
• Numeric relationships
• Appropriate complexity
• Applicable in all areas of information
retrieval
• A weighting function must be
established
• Necessary interpretation of results
There are three types of graph relations: string relations,
grammatical relations, and numerical relations. String
relations allow users to describe the properties of the
connection to express a type or membership link, as well
as depict the temporal and geographical relationships
between graph nodes. In contrast to grammatical relations,
which are exclusively used in textual corpus to approve the
nature of the grammatical relationship between the words
of a textual document, they are relevant in all IR settings.
Grammatical and String relations do not convey the order
of the nodes in the document structure, but numerical
relations do. The connection weight can represent the
degree of distribution of the graph's components or the
significance of a node in respect to a node in the query. In
all IR situations, the weighting function may be utilized to
add contextualized information and quantify the
information supplied by the connection.
Finally, each type of relation has its specificity, depending
on the objective and the context of where it is applied.
V. CONCLUSION
This study is a continuation of our previous work on
information retrieval in semi-structured documents. We
referred to a number of studies that employed graphs to
represent documents in the context of XML information
retrieval. In this context, we have concentrated on the
relationships between graph components. Indeed, in the
field of graph comparison, and notably in document IR,
these connections transmit a substantial amount of
information.
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