FUZZY SEMANTIC MATCHING IN
(SEMI-)STRUCTURED XML DOCUMENTS
Indexation of Noisy Documents
Arnaud Renard, Sylvie Calabretto and Béatrice Rumpler
Université de Lyon, CNRS, INSA-Lyon, LIRIS, UMR5205, F-69621, France
Keywords: Information retrieval, (semi-)Structured documents, XML, Fuzzy semantic matching, Semantic resource,
Thesaurus, Ontology, Error correction, OCR.
Abstract: Nowadays, semantics is one of the greatest challenges in IR systems evolution, as well as when it comes to
(semi-)structured IR systems which are considered here. Usually, this challenge needs an additional external
semantic resource related to the documents collection. In order to compare concepts and from a wider point
of view to work with semantic resources, it is necessary to have semantic similarity measures. Similarity
measures assume that concepts related to the terms have been identified without ambiguity. Therefore,
misspelled terms interfere in term to concept matching process. So, existing semantic aware (semi-
)structured IR systems lay on basic concept identification but don’t care about terms spelling uncertainty.
We choose to deal with this last aspect and we suggest a way to detect and correct misspelled terms through
a fuzzy semantic weighting formula which can be integrated in an IR system. In order to evaluate expected
gains, we have developed a prototype which first results on small datasets seem interesting.
1 INTRODUCTION
Today’s society is evolving and relies on more tools
and practices related to information technologies.
This is mostly due to the evolution of
communication infrastructures. Indeed, the difficulty
no longer lies in information availability but rather
in access to relevant information according to the
user. In order to help in information management,
the Web is growing according to two tendencies.
On one side, the first one deals with the larger
availability of more structured data. That means that
large amounts of data which were formerly stored in
flat textual files are now frequently stored in (semi-
)structured XML based files. That is the reason why
we choose to deal this kind of documents.
On the other side, the second one brings
semantic aware techniques in order to achieve better
machine level understanding of those data.
Semantics consists in the study of words meaning
and their relationships like: homonymy, synonymy,
antonymy, hyperonymy, hyponymy. The use of
semantics in IR systems can be an efficient way to
solve data heterogeneity problems: both in terms of
content and data structure representation (documents
follow neither the same DTD nor the same XML
schema). Most of the time, heterogeneity is due to
the lack of a common consensus between
information sources, which results in global end
users incapacity to have a whole knowledge of
documents content and structure in a given
collection. As a consequence, semantics can be
considered as a key factor in search engine
improvement. This observation can be made for both
domain specific as well as public at large search
engines (like Google, Yahoo …). Indeed, there are
an increasing amount of attempts to take semantic
into accounts and recently Google integrated some
kind of semantics to fill the semantic gap between
user real needs and what he has typed as a query. In
the same way Microsoft launched recently a new
semantic search engine known as “Bing”.
It is commonly accepted that the use of semantic
resources like ontologies, thesauri and taxonomies of
concepts improve IR systems performances (Rosso,
2004). Thus, to use a semantic resource, it is
necessary to perform matching between terms of
documents and concepts instances in a semantic
resource. Some systems already try to achieve
(semi-)structured IR by using semantic resources but
they are still few. Our goal is to improve results by
253
Renard A., Calabretto S. and Rumpler B.
FUZZY SEMANTIC MATCHING IN (SEMI-)STRUCTURED XML DOCUMENTS - Indexation of Noisy Documents.
DOI: 10.5220/0002807502530260
In Proceedings of the 6th International Conference on Web Information Systems and Technology (WEBIST 2010), page
ISBN: 978-989-674-025-2
Copyright
c
2010 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved
making a fuzzy semantic matching to take into
account common mistakes in indexed documents
such as typos or wrong words spelling. In fact, none
of semantic aware IR systems currently take into
account these anomalies.
The article is structured as follows: we present in
section 2 some semantic resources, similarity
measures, different approaches proposed in the
literature about (semi-)structured IR which consider
the semantic aspect, and some error correction
systems proposition. After, we present our proposal
in order to improve semantic indexing of structured
documents in section 3. Then, we discuss briefly
about prototype design in and the evaluation process
in section 4, to finish with evaluation results we
obtained in section 5. Finally, we conclude and
debate about future works in section 6.
2 RELATED WORKS
A common characteristic of semantic aware IR
systems is the necessity of external semantic
resources as well as similarity measures allowing for
comparisons between concepts. It leads Bellia
(Bellia, 2008) to define the notion of semantic
framework, which relies on two complementary
concepts: an external semantic resource and a model
for measuring similarity between concepts.
2.1 Semantic Resources
Semantic resources can be split in two categories
according to the range of knowledge they represent:
domain specific resources, and general resources.
Given the nature of documents collections which are
as we indicated before very heterogeneous, only
general resources are considered here. Indeed,
domain specific semantic resources do not cover a
sufficiently broad area and would provide fine
grained but fragmentary knowledge about
collections. We plan to use general semantic
resources: thesaurus like Wordnet, ontologies like
YAGO (Suchanek, 2007) which is a large and
extensible ontology built on top of Wikipedia and
Wordnet, or DBpedia which is resulting from a
community effort to extract structured data from
Wikipedia (Auer, 2007). DBpedia “uses cases”
indicate that it can be used as a very large
multilingual and multidomain ontology. DBpedia
has the advantage of covering many domains and
containing many instances. Moreover, it represents
real community agreement and “automatically”
evolves as Wikipedia changes. Kobilarov
(Kobilarov, 2009) works about interconnection of
many domain specific BBC sites by using DBpedia
resources seem to be promising. However, there
seems to be a lack of semantic similarity measures
available on DBpedia data, which makes it difficult
to use. As we can see in the next section, semantic
similarity measures are very useful to use semantic
resources.
2.2 Semantic Similarity Measures
Similarity measures are required to be able to
evaluate the semantic similarity of concepts included
in a semantic resource such as a thesaurus or
ontology. These measures provide estimations about
strength of relations between concepts (which
queries terms and documents terms are related). It is
particularly useful in the semantic disambiguation
process, in terms weighting process and when
querying by concepts. An almost complete survey of
disambiguation may be found in (Navigli, 2009).
Two types of semantic similarity measures can
be distinguished. The first type is based on the
structure of the semantic resource and counts the
number of arcs between two concepts. In contrast,
the second type of measures is based on the
information content. Information content reflects the
relevance of a concept in the collection according to
its frequency in the whole collection and the
frequency of occurrence of concepts it subsumes.
However, Zargayouna (Zargayouna, 2005) showed
that the first type of measure could be as efficient as
the second one. Moreover, the second type of
measures requires a learning phase dependent on the
quality of the learning collection. So it is more
difficult to carry out (especially because of the
difficulty to find a suitable collection for the
learning phase). Examples in this area, are Resnik
(Resnik, 1995) works who brought the information
content, as well as those of Jiang (Jiang, 1997) and
Lin (Lin, 1998) using a mixed approach, and more
recently of Formica (Formica, 2009). In this work,
we will only discuss the first type of measurement.
Rada (Rada, 1989) suggested that the similarity
in a semantic network can be calculated by relying
on links expressing taxonomic hypernym/hyponym
relationships, and more specifically of “is-a” type.
Then, the semantic similarity can be measured in
taxonomy by calculating the distance between
concepts by following the shortest path between
them. It is mentioned in this article that this method
is valid for all hierarchical links (“is-a”, “sort-of”,
“part-of” ...), but it may be modified to take into
account other types of relationships.
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254
Wu and Palmer developed in (Wu, 1994) a
measure of similarity between concepts for machine
translation. Their method is defined according to the
distance of two concepts with their smallest common
ancestor (the smallest concept that subsumes both of
them), and with the root of the hierarchy. The
following formula allows computing of similarity
between two concepts
and
:
,
2
,
,
2
(1)
Where,
is the smallest common ancestor of
and
(according to the number of arcs between
them),
is the number of arcs between and
the root, and
,
the number of arcs between
and .
In Zargayouna (Zargayouna, 2005), the proposed
similarity measure is based on Wu-Palmer’s (Wu,
1994). The “father-son” relationship is privileged
over other neighborhood links. To achieve that, Wu-
Palmer’s measure needs to be modified, because in
some cases it penalizes the son of a concept
compared to its brothers. Adaptation of the measure
is made thanks to the specialization degree function
of a concept
which represents its distance
from the anti-root. This helps to penalize concepts
which are not of the same lineage.
,
2
,
,
2
,
(2)
,
,
,
(3)
Where,
is the maximum number of
arcs between the lowest common ancestor and anti-
root “virtual” concept: .
In Torjmen (Torjmen, 2008) works on
multimedia structure based IR, they assume that the
structure of an XML document can be assimilated to
ontology. Consequently, they proposed a new
refinement of Wu-Palmer (Wu, 1994) and
Zargayouna (Zargayouna, 2005) measures
applicable directly on documents structure.
Various works designed to manage semantics in
IR systems require the use of tools and resources we
have introduced. However, most approaches take
only the semantic of documents textual content into
account and not the semantic of their structure but
some IR systems tend to take semantic into account
in both content and structure of documents. The
XXL system is the first one which incorporated
ontology in the indexing process.
2.3 (Semi-)Structured Semantic IR
Systems
The XXL query language system allows querying
XML documents with syntax similar to SQL.
Indeed, it is based on XML-QL and XQuery query
languages and adds a semantic similarity operator
noted “~”. This operator allows expressing
constraints of semantic similarity on elements and
on their textual content. Query evaluation is based
on similarity calculations in ontology as well as
terms weighting techniques. The XXL search engine
architecture is based on 3 index structures
(Schenkel, 2005): element path index, element
content index, ontological index. This approach,
which consists in semantic indexing by ontology,
seems to be interesting.
Van Zwol studies on XSee IR system (Van
Zwol, 2007) are interesting because it confirmed that
semantic improves the performance of structured IR
systems.
Zargayouna (Zargayouna, 2004-2005) works on
semantic indexing led to SemIndex prototype
(dedicated to the semantic indexing) and SemIR
(dedicated to the retrieval). In this system, the
semantic dimension is taken into account at both
terms and structure levels. The previously defined
similarity measure is used for terms sense
disambiguation. This is performed favoring the
meaning attached to the concept that maximizes the
density of the semantic network. The originality of
the approach is primarily in the similarity measure
used to enrich terms weighting method.
Mercier-Beigbeder measure (Mercier, 2005) is
merged by Bellia (Bellia, 2008) with a previous
version of Zargayouna’s works (Zargayouna, 2004)
to take semantic into account. This measure is then
enriched to consider XML formalism and latent
similarity links between documents.
Other semantic aware structured IR systems may
be cited such as CXLEngine (Taha, 2008), which is
derived from previous works that led to OOXSearch.
Nevertheless, neither system takes terms
uncertainty into account during the indexing process.
2.4 Documents Error Management
Mechanisms
Terms uncertainty Errors may have several sources.
They can be caused by bad quality documents which
results in wrong characters recognition by OCR.
Distribution of this kind of errors across documents
is somewhat unpredictable a priori. Errors can be
caused by human errors in particular when those are
FUZZY SEMANTIC MATCHING IN (SEMI-)STRUCTURED XML DOCUMENTS - Indexation of Noisy Documents
255
dyslexics, or when they come from foreign countries
and learn a new language, or when they write
documents on portable devices … Damerau in
(Damerau, 1964) established a list of different kind
of resulting errors.
According to (Pedler, 2007) two error types can
be distinguished: non-words errors which can be
easily detected thanks to a dictionary, and real-
words errors which are harder to detect while they
represent real existing words. Indeed, to be able to
detect the second type of errors, the spellchecker
must be able to understand (thanks to semantics) the
context in which the syntaxically but not
semantically correct term is misused.
Error correction problem has been challenged in
the Text Retrieval Conference (TREC-5 Confusion
Track). Three versions of a collection of more than
55000 documents containing respectively error rates
of 0%, 5%, and 20% have been used to run different
approaches in the management of those errors for
information retrieval systems. A paper which
describes this track (Kantor, 2000) indicates the
different methods followed by five of the
participants. It shows a drop in performances of
every IR systems in presence of corrupted
documents containing errors. Three of them used
query expansion with altered terms and two of them
tried to correct documents content. Comparison of
these methods indicates that the second approach
seems to offer better results and constitute a good
starting point. Introduction of semantics in these
error correcting systems could be a way to achieve
better results. This is the in which our proposal
evolves.
3 PROPOSAL: FUZZY
SEMANTIC WEIGHTING
During our study of related works, we could identify
that Zargayouna’s (Zargayouna, 2004-2005)
weighting method introduces good concepts for
semantic (semi-)structured IR so that we extends
(Zargayouna, 2004) semantic weighting formula.
The objective is to eliminate mistakes and typos in
content by making a fuzzy term matching.
3.1 Terms Semantic Weighting
In Zargayouna (Zargayouna, 2004), the semantic
weight
,,
of a term in a tag of a
document
in the semantic vector corresponds to
the sum of its weight and semantically close terms
TFITDF weights.
,,
,,
∑
,
,,
(4)
However, TFITDF is better suited for structured
XML documents than for (semi-)structured XML
documents as it considers specific tags models.
Thus, in
,,
, TFITDF is replaced with
standard TFIEFIDF weighting formula.
3.2 Terms Fuzzy Matching
Our idea is to enrich the semantic weighting formula
proposed in (Zargayouna, 2004) by taking into
account errors in terms spelling leading to
uncertainty in written terms. To manage this purpose
we were inspired by Tambellini’s works on
uncertain data management (Tambellini, 2007).
Since we rely on a lexicalized semantic resource
where concepts are represented by terms, we believe
it may be interesting to perform a fuzzy matching
between documents terms and terms reflecting
concepts in the lexicalized semantic resource.
According to (Tambellini, 2007), two terms
and
can be paired according to: their concordance
i.e. their relative positioning that we note
,
, and their intersection i.e. common areas
between two terms that we note
,
.
Table 1: Adapted Allen’s spatial relations.
The concordance value noted
,
is
determined according to terms characterization. It
depends on spatial relationships derived from
Allen’s relations (Allen, 1983-1991): “
”,
“
”, “”, “”, “” and
“
_”. Each characterization is then
associated with a value
.
,
0.8,
,
0.6,
,
0.8,
,
0.2,
,
1,
,
0,
,
_
(5)
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256
It should be noted that in (Tambellini, 2007)
these values seem to be determined empirically.
We respectively note terms common areas of
and
,
and
(cf. Table 1).
The intersection value
,
is highest
i.e. 1 if terms common areas are equals i.e.
and otherwise its value is
,
.
,
1,
0
,
1,
(6)
The problem of uncertainty is present in many
areas including systems which determine if two
words are phonetically identical (like Soundex
algorithm and its derivatives: Metaphone …).
Spelling correction systems rely on the problem of
data uncertainty in the manner they try to compare
two words according to their common letters. This
kind of algorithm is used to determine
,
. Indeed, terms common areas are
phonetically encoded and we note them respectively
and
:
,
0.75
1
,
max
,
(7)
The proximity between encodings is computed
using a normalized Hamming distance and then
leveraged with a factor of 0.75 which reflect
intersection uncertainty relative to the phonetic
encoding. Thus, the matching value
,
can be defined from
,
and
,
:
,
,
,
(8
)
A term
in a document may be considered to be
in the semantic resource if there is a term
in the semantic resource and
,
among concordance relations defined above, and if
,
1. In the same way if
,
_ then it is from the
semantic resource.
Therefore, it is possible to define an
approximation of each term in the documents
collection as: all concepts instances of the ontology,
which are neither
nor :
~
|
(9)
Where,
~ is the set of terms
representing
close concepts
in the semantic resource to a
document term.
3.3 Misleading Terms Detection
It is evident that a fuzzy semantic weighting could
introduce noise if applied on correct (not misspelled)
terms. Therefore, it is needed to detect off-board
terms which can be considered as being misleading
terms first. We propose to use Semantic frequency to
achieve this by calculating the frequency of the term
and that of all semantically close terms:
,,
,,
∑
,
,,
(10)
Indeed, if a term
is out of context, its semantic
frequency will probably be very low as it will be
isolated. Thus, terms whose semantic frequency is
below a threshold can be considered as misleading
terms. A
function tries
to determine if a term is a wrong term, or not:
,,
1,
,,
0,
(11)
The threshold estimation has been
experimentally determined and fixed at:
0.4
,
where
is the number of terms in the considered
element
. In order to adapt the threshold to different
profile of elements textual content (for example
when there is not a prevailing thematic in the
element), we plan to use an outlier identification
data-mining algorithm. The drawback of our error
detection method is that it can’t detect misleading
terms when they are alone in an element. Indeed in
that case there is no context in the elements that is
why surrounding elements should be used instead.
3.4 Terms Fuzzy Semantic Weighting
Misleading terms detected have to be corrected with
best possible substitutes. Our proposition can be
considered as fuzzy (imprecise and uncertain) the
replacing term selected
~ (among
approximations of term
) is the one which seems
the most relevant in the context. For this, we select
the term
whose semantic frequency penalized by
its matching value with the term
obtains the
highest score:
~
max
,
,,
(12)
To confer more fuzziness to our proposition we
could have considered building a vector of best
replacing terms instead of choosing the best one
according to our criteria. The new occurrence of the
selected replacing term can then be weighted: from
nothing if it is not present elsewhere in the element
FUZZY SEMANTIC MATCHING IN (SEMI-)STRUCTURED XML DOCUMENTS - Indexation of Noisy Documents
257
or its occurrence can be added to the semantic
weight of this term if it exists already. Obviously,
the matching value is used to weight the significance
of the selected term owing to term matching
uncertainty. Our terms weighting formula derived
from (Zargayouna, 2004) is:
,,
,
,,
,
,
,
,,
,
(13)
Where
,
,, is the same
formula as
,, except the fact that its
TFIEFIDF factor is updated to take new possible
term occurrence into account.
,
(14)
|
|
|
:
|
,
(15)
|
|
|
:
|
,
(16)
We have presented terms fuzzy semantic
weighting formula for terms belonging to a
document which has been integrated within a
semantic aware (semi-)structured IR system to be
evaluated.
4 PROTOTYPE
The prototype we have developed to validate our
weighting formula should have multiple index
structures to access the collection through the
structure, the content, and especially the concepts. In
addition, it should create a Soundex index of terms
in the lexicalized semantic resource in order to make
fast comparisons of documents and semantic
resources terms phonetic forms. During prototype
development phase, we performed a survey of
existing libraries and platforms dedicated to IR with
the objective to allow the prototype to scale-up on
large documents collections. The following tools
have been considered: Zettair, Lemur Toolkit,
Dragon Toolkit, Terrier, Lucene, GATE. Although
none of evaluated systems responds to semantics and
(semi-)structured documents constraints, GATE
platform has been considered because of its high
level of modularity. Thus, it provides many useful
tools and libraries to improve prototype
development speed, so some of them were used in
our prototype (cf. Figure 1). Index persistence
problem has been managed through Java Persistence
API and a MySQL/InnoDB relational database.
Figure 1: Global application architecture.
5 EVALUATION
The developed prototype allowed us to study the
behavior of our proposal against a collection of
documents according to the kind of anomalies we
wish to correct.
Table 2: Terms distribution per document and element.
Markup Doc1 Doc2 Doc3 Doc4
name
Anne,
Frank
concert
supermarket,
grocery,
store
movie, theater
sect/title
introducti
on
introductio
n
introduction introduction
sect/par
book,
writings,
dairy,
girl,
family,
diary,
journal
concert,
music,
musician,
recital,
ensemble,
orchestra,
choir,
band,
show, tour
food,
merchandise,
meat,
produce,
dairy,
pharmacy,
pet,
medicine,
clothes
movie (x2),
theater (x2),
theatre (x2),
picture, film,
cinema,
motion, picture,
ticket,
projector,
screen,
auditorium
Table 2 is a representation of textual elements of
a collection of four documents. Only items with text
content are represented as they would result after
selection and stemming of words achieved through a
morphosyntactic analyzer.
We have deliberately introduced an error in
« Doc1/sect/par » element to highlight the interest of
our proposal. Indeed, the term “diary” has been
replaced by the word “dairy”, shown in red bold
italic in Table 2.
5.1 Terms Semantic Frequency
In order to identify off-board terms requiring fuzzy
weighting, we calculate the semantic frequency of
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258
each term. The calculated threshold value is 0.2 and
is symbolized by a red horizontal line. Each term
which semantic frequency is below that point is
considered as being off-board, and consequently as
misleading.
Figure 2: Histogram of terms semantic frequencies.
We can observe on Figure 2 histogram that only
one term has a semantic frequency below the
threshold. The term “dairy” can be identified as
being out of context (it has indeed been introduced
as an error on the word “diary”), and therefore the
fuzzy semantic weighting formula has to be applied
to correct the mistake.
5.2 Terms Fuzzy Semantic Weighting
It is necessary for detected wrong terms to identify
the best term in the set of terms approximation. The
term of the semantic resource which achieves the
best score according to the matching value (
)
and to its semantic relatedness in the element will be
considered as being the best substitute. In the
considered case, the best replacing term retrieved for
“dairy” is “diary”. So the weight of the term “diary”
is enriched with “dairy” occurrence. Hence, we have
increased the importance of the term “diary” almost
as if no mistake occurred on it. Its importance is still
lowered due to the uncertainty in terms fuzzy
matching.
We can observe on
Figure 3 histogram that none of the first two
weighting schemes (solid bars and hollow bars) is
able to detect the erroneous writing of an occurrence
of the term “diary” spelled as “dairy”. This is the
normal behavior expected from these formulas.
However, we note that the third weighting formula
affects to the term “diary” a weighing greater than
other terms thanks to enrichment (modulo the
confidence of the matching between “dairy” and
“diary”) of the weighting of this term with the
occurrence of erroneous term “dairy”.
Figure 3: Terms weighting comparison according to the
weighting formula.
This is what we want to achieve in order to
weight terms beyond errors which can be found in
original documents. This can be seen as a semantic
corrector which runs during indexation process.
6 CONCLUSIONS AND FURTHER
WORKS
In this paper, we have presented a state of the art
about useful tools to make semantic aware (semi-
)structured IR systems. In particular, semantic
similarity measures which allows for concepts
comparisons. We then talked about related IR
systems and exposed some considerations about
error management mechanisms. We finally ended
with a proposal for a misleading terms detection
method and a fuzzy semantic weighting formula that
can be incorporated in an existing system.
The fuzzy matching and weighting formula we
propose can be used in conjunction with semantic
resources such as Wordnet. An interesting evolution
would be to use YAGO or DBpedia instead of
Wordnet while they represent much richer resources.
Our first evaluations show index quality
improvements.
The first short-term development is the
implementation of a more scalable prototype
allowing us to evaluate error detection/correction
and the weighting formula with richer semantic
resources on very large datasets like INEX
evaluation campaign documents collection.
0
0,05
0,1
0,15
0,2
0,25
0,3
Weight
Terms
TFIEFIDF SemWmod FuzzySemW
FUZZY SEMANTIC MATCHING IN (SEMI-)STRUCTURED XML DOCUMENTS - Indexation of Noisy Documents
259
As indicated before, many other refinements can
be considered at different stages. For the misleading
term detection, we plan to use data-mining
algorithms in order to detect outlier values and avoid
the use of empirical thresholds. We plan to include
surrounding elements in context definition to help in
populating elements context. For the correction
phase, we could consider vector of replacing terms
instead of choosing the “best” replacing one.
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