Christian Sallaberry
, Mustapha Baziz
* **
, Julien Lesbegueries
and Mauro Gaio
* Laboratoire d’Informatique-Université de Pau (UPPA, France)
** Institut de Recherche en Informatique de Toulouse (IRIT), France
Keywords: Geographic Information Extraction and Retrieval, Spatial Information Scope, Classical IR, Digital Libraries.
Abstract: This paper deals with spatial Information Extraction (IE) and Retrieval (IR) in Digital Libraries
environments. The proposed approach (implemented within PIV
prototype) is based on a linguistic and
semantic analysis of digital corpora and free text queries. First, we present requirements and a methodology
of semantic annotation for automatic indexing and geo-referencing of text documents. Then we report on a
case study where the spatial-based IR process is evaluated and compared to classical (statistical-based) IR
approaches using first pure spatial queries and then more general ones dealing with both spatial and
thematic scopes. The main result in these first experiments shows that combining a spatial approach with a
classical (statistical-based) IR one improves in a significant way retrieval accuracy, namely in the case of
general queries.
PIV: project named Virtual Itineraries in Pyrenees (moun-
tains of the south-west of France)
Geographically related queries form nearly one fifth
of all queries submitted to Excite search engine, the
terms occurring most frequently being place names
(Sanderson and Kohler, 2004). Our contribution
focuses on digital libraries and proposes to extend
basic services of existing Library Management
System with new ones dedicated to geographic
information extraction and retrieval (PIV project
(Lesbegueries et al., 2006)). Geographic information
in such a repository is composed of a spatial feature,
a temporal feature and a thematic one. “Music
instruments in the vicinity of Laruns in the XIXth
century” is an example of a complete geographic
feature: “Music instruments” is the thematic feature,
“vicinity of Laruns” is the spatial feature and
“XIXth century” is the temporal one.
Let’s assume that to initiate a geographical
retrieval process the spatial feature has to be explicit
whereas the temporal one could be implicit or not
locally expressed and the thematic feature can be
missing. Consequently, to process geographical
information in-depth, analysis of spatial information
is mandatory.
Our spatial model supports absolute and Relative
Spatial Features. Spatial features such as “Biarritz
district” are well-known named places. We call them
Absolute Spatial Features (ASF). Complex Spatial
Features as “Biarritz vicinity” or “South of Biarritz
district” have to be interpreted and, therefore, need
some spatial reasoning processes (Cohn and
Hazarika., 2001). Such features are called Relative
Spatial Features (RSF). We associate each RSF to
one or more spatial relationships (adjacency,
inclusion, distance, orientation) for a recursive
Works like the SPIRIT project, the Geosearch
system, the GEO-IR system, etc. are related to
spatial information management. They are presented
in (Chen et al., 2006). A difference of our approach
with other ones like SPIRIT (Jones et al., 2004) and
GIPSY (Woodruff et al., 1994) relies on the back-
Sallaberry C., Baziz M., Lesbegueries J. and Gaio M. (2007).
In Proceedings of the Ninth International Conference on Enterprise Information Systems - HCI, pages 190-197
DOI: 10.5220/0002383701900197
office spatial reasoning used for both ASFs and
RSFs interpretation and indexing. For instance, the
SPIRIT system mainly tags ASFs. Another
specificity concerns the granularity level of the
managed information units: textual paragraphs of a
domain specific corpora (cultural heritage of
Pyrenees) in our case and web pages in the case of
SPIRIT system. In the proposed approach, a refined
spatial information interpretation and a markup
process are applied both within the information units
indexing stage and the users’ query interpretation.
As we work on specific digital library collections
and as these collections are quite stable and not too
large, the hard back-office spatial process seems to
be suitable (Lesbegueries et al., 2006). Therefore,
the cost of such refined spatial aware indexing is
reasonable. Queries are interpreted dynamically in
the same way and SFs blow-by-blow indexes allow
a more accurate information retrieval.
The paper is organized as following. In the
second section we present PIV spatial semantics
processing. In the third section, we experiment and
present the first results of an evaluation and
combination of PIV spatial approach with classical
statistical IR approaches.
2.1 An Overview of the System
In PIV project, we want a non-expert user (tourist,
scientist or scholar) to access to territorial-oriented
digitized corpora. Figure 1 represents PIV system’s
two main sub-processes of Information Extraction
and Retrieval.
Figure 1: Synoptic schema of information extraction,
retrieval and visualization in PIV system.
Roughly, IE is held in four main stages. First of
all, documents collections are built (stage (1)), in
this paper, we used digitized archives dealing with
the cultural heritage of the south west of France.
Then in stage (2), a linguistic and semantic analysis
of these digital corpora is carried out in order to
extract SFs as formal representations of instances of
the PIV spatial model. The third stage (3) parses
geographic gazetteers (districts, named-places,
roads, cliffs, valleys, …) in order to validate SFs
captured before. IE then computes spatial
representations and georeferences (stage (4)). Thus,
the IE sub-processes results are either absolute (e.g.
“Laruns village”) or relative SFs (e.g. “Laruns
village vicinity”).
IR part is also based on such an analysis of the
query (stage (6)) and relies on a spatial mapping. It
computes intersection surfaces (stage (7)) between
spatial representations corresponding to the query
and those contained in the indexes (cf. §2.4). It will
be then necessary to extract fragments of such
relevant documents (stage (8)) and, finally, to
present them to the user (stage (9)).
2.2 The Spatial Core Model
In this model, according to the linguistic hypothesis,
a SF is recursively defined from one or several other
SFs and spatial relations are part of the SFs’
definition (Lesbegueries et al., 2006, 2006b). The
target/landmark principle (Vandeloise 1986) can be
defined in a recursive manner. For instance, the SF
“north of the Biarritz-Pau line” is first defined by
“Biarritz” and “Pau” landmarks that are well known
named places, the term “line” creates a new well-
known geometrical object linking the two landmarks
and cutting the space into two sub-spaces, finally, an
orientation relation creates a reference on the target
to focus on.
Figure 2 shows that a SF has at least one
representation (A) with a natural or artificial
boundary; it can be specialized (B) into an absolute
(ASF), i.e. “Laruns village” named place or a
relative feature (RSF). A RSF is defined with a
reference, i.e. “west of Laruns village” relation
linking at least one other SF (C). The cycle
represents the recursive definition.
Figure 2: Spatial core model simplified schema.
For spatial information extraction in textual
documents, a Definite Clause Grammar illustrated in
(Lesbegueries et al., 2006) specifies lexicons and
rules in order to detect SFs and create instances of
this model.
Thus, a SF spatial relation can be an adjacency
(“nearby Laruns”), an inclusion (“centre of
Laruns”), a distance (“at about 10 kms of Laruns”), a
geometric form (“the Laruns Arudy Mauleon
triangle”) or an orientation (“in the west of Laruns”).
In the core model all of these spatial references
have attributes used to characterize them. So, for
instance, distance has a numerical and/or a
qualitative parameter and adjacency has a qualifier
as defined in (Lesbegueries et al., 2006b) and
(Muller 2002).
So, a XML tree (cf. §2.3) complying with the
PIV XML schema (Lesbegueries et al., 2006)
describes any SF.
2.3 Spatial IE and Indexing
Hereinafter, we briefly describe the Linguistic and
Semantic Processing Sequence supporting PIV
spatial IE process (Lesbegueries et al., 2006).
The LPS goal is to populate a structured
information repository (XML indexes) from
heterogeneous information sources (news papers and
books contents, postcards descriptors). We also used
it to separate spatial features from the thematic ones
in the query when evaluating IR results (cf. §3.5).
According to works on textual documents
(Lesbegueries et al., 2006b), we adopt an active
reading behaviour, that is to say sought-after
information is known a priori. This is why, unlike
slight Natural Language Processing (NLP)
(Abolhassani et al., 2003), our linguistic and
semantic processing sequence is locally applied near
candidates for named places. To mark these
candidates a lexicon is used in order to have a quite
good generic bootstrap process. So ASFs (i.e.
villages’ names, forests’ names, etc.) are detected
first and marked. Then RSFs are built from
previously pointed out ASFs. The data processing
sequence used to highlight spatial features is
implemented as described in Figure 3.
Figure 3: Linguistic/Semantic Processing Sequence (LPS).
First a tokeniser and a splitter parse the textual
flow (Figure 3-A). This pre-treatment corresponds to
new textual flow where the initial content is added
with logical sub-structures marks; words separators
marks are added with their lemmas (thanks to a
lemmatization phase embedded).
In the second stage (Figure 3-B), spatial features
called “candidates” are detected as following: first,
all sentences having tokens starting with a capital
letter and preceded with a token containing terms
specified in a lexicon “in”, “from”, … (known as
spatial feature’s initiator) are marked. Then, a Part
Of Speech (POS) tagger parses these marked
sentences and retrieves words’ POS.
In the third stage (Figure 3-C), a Definite Clause
Grammar (DCG) based analysis interprets the
extracted syntagms (inclusion, adjacency, distance to
another spatial feature, etc.). The feature “near of
Laruns” is interpreted as a RSF (“rsf” tag in line 2
Figure 4) itself defined by an adjacency relation
(line 4-6 Figure 4) and by the “Laruns” ASF (line 7-
10 Figure 4).
The SFs validation stage calls external services
(gazetteers) to confirm every candidate ASF (Figure
3-D). For the sentence “Paul passe près de Laruns”
(Paul passes nearby Laruns): “Laruns” candidate SF
is confirmed whereas “Paul” candidate SF is
removed. All the RSFs candidates associated to a
non-validated ASF are also removed. Finally a MBR
(Minimum Bounding Rectangle) (Lesbegueries et
al., 2006) representation consisting on geocode
coordinates (lines 13-18 Figure 4) is added to the
XML index tree.
ICEIS 2007 - International Conference on Enterprise Information Systems
Figure 4: An excerpt of the SFs XML indexes.
2.4 Spatial IR Based on SFs
We use SFs indexes to undertake queries and
retrieve information from documents.
A free text interface supports the IR stage.
Queries are analyzed exactly as the documents of the
corpus are: the same IE data processing sequence is
executed and every SF is extracted. All the validated
SFs are geo-localized and a MBR is attached to each
one of these SFs. A query is analyzed online
whereas corpus documents are analyzed offline.
Our search technique is based on a spatial
mapping between the SFs of the query and those of
the documents (stage (7) in Figure 1). This mapping
is done thanks to the geospatial footprints created
dynamically for the query and those stored in index
files of the corpus.
For example, Figure 5 illustrates a query and an
indexed area (precise geospatial footprints for ASFs
and approximated MBRs for RSFs).
Figure 5: Relevance computing.
The selection process consists in processing
index files and computing intersections with a GIS
(Lesbegueries et al. 2006). Then, we select
corresponding relevant Documents fragments (Df).
We are able to calculate the relevance of a
document fragment by computing an evaluation of
the surface which results from the intersection
between the SF of the document fragment and the
ones of the query:
For any query, the relevance of each recovered
document may be different (Figure 5):
precisionDf =
cesignificanDf =
distance Df =
Therefore, we compute Df score as following:
distance Df
The closer the centroids of I and Q are to each
other, the higher the relevance score of Df.
An XML DBMS (eXist -
and a GIS (PostGIS -
support these searching and computing operations
on the corpus indexes. Figure 6 illustrates relevance
computing via functions and queries submitted to the
area(intersection(Q_geom, Df_geom))
geomfromtext(‘corner coordinate’))
SELECT pi.gid, pi.doc_name, pi.par_id, pi.SF-name,
(tq.isurf/tq.dfsurf + tq.isurf/tq.qsurf)/(2 + tq.d/tq.D)
AS weight
FROM piv_index pi, temp_query tq
WHERE pi.gid=tq.gid ORDER BY weight DESC;
Figure 6: Surfaces, distances and score computing.
The query of Figure 6 returns the relevant
documents and paragraphs IDs. Then the original
texts and the SFs details may be presented in a
weighted order.
In this section, we evaluate the PIV spatial-based IR
approach based on information extraction (IE) of
Spatial Features (SFs) in textual documents. The
PIV results are compared to those obtained by a
classical keywords-based IR using the same
collection and the same set of test queries. The used
classical IR approach is defined in the next section.
3.1 Classical IR Approach
The IR classical approach is based on the notion of
“bag” of single words (Baeza-Yates et al., 1999). In
such full text approaches, documents are first
indexed using a classical term indexing. It consists
in selecting single words occurring in the
documents, and then stemming these words using an
appropriate stemmer (Porter 2001) and at the end
removing stop-words according to a stoplist. We
used in this paper a stoplist and a French stemmer
from the Snowball family of stemmers (Porter
2001). A weight Wtd(t,d) is then assigned to each
term t in a document dj following the formula given
in (2):
Where tf
represents the frequency of the term t
the document d
, n
is the number of documents
containing the term t
and N the total number of
documents in the collection. dl
represents the length
of the document d
and avg_dl, the average length of
the document in the collection. This weighting
method, which is an enhanced TF.IDF formula, is
introduced to attenuate the negative impact of large
documents in the searching stage (Robertson et al.,
1995). This is also suitable for the used collection
(paragraphs with various lengths). The same
indexing process is applied to queries.
A vector-based model (Boughanem et al., 2001)
is then used to retrieve documents: for a given query
q, the Inner product between the vector of the query
and the ones of each document d
in the collection is
applied in order to compute the relevance score:
),().,(),(Re (3)
Finally, this relevance score is used to determine the
ranking of the document (d
) in the final list of
retrieved documents in response to the query (q).
3.2 Sample Data
The corpus used for training and testing the PIV
system is provided by the MIDR county media
library. The collection contains 10 OCRised books
dealing with the Pyrenean cultural heritage of the
XIXth and XXth century. The books are splitted into
paragraphs constituting about ten thousand
document units. We have made 12 queries on which
8 deal with only spatial scope whereas the 4
remaining deal with both spatial and thematic
scopes. A spatial query could support Absolute
Spatial Features (ASF) or Relative Spatial Features
(RSF). A thematic and spatial query like “music
instruments in Laruns vicinity” supports both
ASF/RSF features (“Laruns vicinity”) and other non
spatial features (“music instruments”).
First we carried out scan and OCR processing of
the books of the corpora. Then we ran PIV prototype
automatic Information Extraction processes. The
processing of one book of 200 pages (stages 2, 3 and
4 of Figure 1) takes five minutes. PIV prototype
found 9835 candidate SFs in these ten books.
3.3 Evaluation of the Spatial IR
We submitted the eight spatial scope queries to the
PIV system and compared the first ranked
documents (top 5, 10 and 15) to the hand-craft
judgments. The results are given in Table 1. Avg
represents the average precision computed over all
the used queries and P@5, P@10 and P@15 design
precision measures carried out respectively at the top
5, 10 and 15 documents. The last column, Number
of responses, represents the total number of retrieved
documents (averaged over the queries).
Table 1: PIV and Classical results on spatial queries.
P@5 P@10 P@15
of responses
A) Spatial approach
Avg 0.78 0.81 0.73 637
B) Classical approach
Avg 0.50 0.43 0.40 252
It can be seen that PIV approach brings 78%
accuracy at top 5 and 81% at top 10. When the same
queries are applied to the classical full text IR
system, the results decrease significantly (Table 1-
B). For instance the average precision on the eighth
queries at the five top documents (P@5) reaches
78% (PIV) whereas it is only of 50% when using the
classical approach. The reason is that in a spatial
query like “near Laruns”, the classical approach
never returns documents dealing with other districts
like “Eaux-Bonnes” or “Louvie-Soubiron” which are
located in the vicinity of “Laruns”. So RSFs
extraction from documents and queries also allows
increasing the number of retrieved relevant
documents: in average 637 document-units are
ICEIS 2007 - International Conference on Enterprise Information Systems
retrieved by the spatial approach for all the queries
whereas the classical approach retrieved only 252.
3.4 Evaluation of the Thematic +
Spatial IR
We look for the impact of using more general
queries containing both spatial and thematic
features. As it can be seen in Table 2-A, the results
are very decreasing for the PIV approach (only 15%
at top 5). A careful analysis of the results shows that
some relevant documents are retrieved but they are
not ranked at the top. So, PIV system is not suitable
for rank-ordering in the case of general (spatial +
thematic) queries. Indeed, PIV’s IE and IR processes
deal only with spatial information.
Table 2: PIV and Classical on thematic + spatial queries.
P@5 P@10 P@15
of responses
A) Spatial approach
Avg 0.15 0.18 0.18 1154
B) Classical approach
Avg 0.48 0.39 0.36 331
As in the first case, the same set of queries is
submitted to the classical IR system. The results
(Table 2-B) are clearly more accurate for the
classical approach than those obtained by the PIV
system (Table 2-A). For instance, the system brings
in average 48% of relevant documents at top 5 and
36% at top15. One can also notices the difference in
the number of responses between the two
approaches: PIV approach retrieved in average 1154
document-units whereas the classical approach
retrieved only 331. This is due to the fact that PIV
system processes all spatial features related to the
area specified in the query (towns, mountains, etc.),
whereas the classical approach seeks for only
documents matching the query words.
3.5 Combining Spatial and Classical IR
The previous results suggest that in the one hand, the
spatial PIV approach is suitable to retrieve
documents dealing with spatial features but lacks of
rank-ordering relevant documents when dealing with
non spatial queries. On the other hand, the classical
full text approach lacks of exhaustivity when it deals
with spatial scope queries but outperforms the PIV
approach when the queries deal with thematic
features. So, one can think to combine the two
approaches in order to take advantage of their
effectiveness and reduce their lacks. Moreover, the
fact that the document unit corresponds to a
paragraph increases the probability that spatial and
thematic information occurring in the same unit be
semantically related.
Figure 7: Combining Spatial and Classical IR approaches
by intersecting the two sets of results.
The idea is to subdivide the query into two sub-
queries (as schematized in Figure 7), the spatial sub-
query and the thematic one. The spatial sub-query
contains named places, or any expression identified
by the Linguistic Processing Sequence (LPS) as
ASFs or RSFs (cf. §2.3). The thematic sub-query
contains all the remaining query terms related to any
non spatial scope (time, events, etc.) without
belonging however to the stoplist. As schematized in
Figure 7, “the vicinity of Laruns” and “Music
instruments in the XIX century” represents
respectively the spatial sub-query and the thematic
sub-query of the query example “Music instruments
in the vicinity of Laruns in the XIX century”.
Once the two sub-queries are identified, they are
submitted to the system supporting the appropriate
approach: PIV for the spatial sub-query and
Classical for the thematic one. The final result is
then built by intersecting the two sets returned by
PIV and Classical approaches. The ranking is based
on the one obtained by PIV: each ranked document
in the PIV result set is added to the final result if it
belongs also to the Classical result set.
The detailed results obtained using the previous
spatial + thematic queries according to this strategy
are given in Table 3. The results confirm the
assumption that combining the two approaches will
enhance retrieval accuracy by rank-ordering more
documents for relevance. For instance at top 5,
precision reaches 70% when we combine the two
approaches, whereas it was of 48% for the classical
approach and only 15% for the spatial approach.
Table 3: Combining PIV with classical approach for the
thematic + spatial queries.
P@5 P@10 P@15
number of
A) Combining Spatial + Classical approaches
Avg 0.70 0.50 0.43 25.75
However, one can notice the reduced number of
retrieved documents because of the trivial
combination used (intersection criteria): for
example, fo the query 12, the combined approach
retrieves only four documents whereas the Classical
approach returns 233 and the PIV one returns 724.
This precision improvement causes an important
decrease in recall.
So an open area may concern the merging
problem of the two sets of results (spatial based
approach results and classical full text ones) in order
to optimize not only precision at top retrieved
documents, but also recall. This may probably be
possible by replacing intersection operator by more
complex ranking ones.
Our contribution focuses on restricted corpora such
as local cultural heritage collections of documents
and is complementary to traditional search methods
used in library or documentary management
systems. The PIV’s Linguistic and semantic
processing plus qualitative spatial reasoning support
absolute and relative spatial features (ASF/RSF)
accurate extraction and retrieval. The PIV prototype
validated this approach (Lesbegueries et al., 2006).
A first evaluation scanned the spatial IE process
of the PIV prototype (Sallaberry et al., 2007). It led
us to extend grammar rules in order to improve the
RSF capturing process. We also integrated a new set
of spatial resources describing Pyrenean roads,
rivers, woods, valleys, mountains, etc.
This paper presents the results of the evaluation
of the PIV prototype spatial IR process. A case study
involving sample documents and queries given by
the MIDR Library of Pau County makes
comparisons between the PIV spatial-based
prototype and a more classical statistical-based
approach. The results show that even-though PIV
approach outperforms classical keywords-based
approaches in the case of spatial queries. According
to these results and those stated in (Vaid et al.,
2005), (Martins et al., 2005), such a spatial approach
and statistical approaches need to be combined in
order to enhance retrieval accuracy in the case of
general queries dealing with both spatial and
thematic scopes. As the PIV system relies on an
architecture of web services, all or part of them
might be easily integrated in existing library or
documentary management systems.
Such a combined approach’s results merging is
an actual research point. In fact, PIV’s slight IR
intersection operator (figure 7) ensures a good
precision but a quite poor recall factor. Future works
will address integration of spatial and thematic
similarity ranking and experiment new merging
algorithms using product, maximum similarity,
various linear combination functions (Martins et al.,
Our project is led in partnership with the Greater Pau
City Council and the MIDR media library. We want
to thank them for providing us with their digital
corpus and their support.
Abolhassani, M., Fuhr, N., Govert; N., 2003. Information
Extraction and Automatic Markup for XML
documents, Intelligent Search on {XML} Data,
Springer, vol. 2818, pp. 159–174.
Baeza-Yates, R. A., Ribeiro-Neto., B. A., 1999. Modern
Information Retrieval. ACM Press / Addison-Wesley.
Borillo, A., 1998. L’espace et son expression en français.
L’essentiel. Ophrys.
Boughanem, M., Chrisment, C., Tmar, M., 2001. Mercure
and MercureFiltre Applied for Web and Filtering
Tasks at TREC-10. Proceeding of TREC.
Charnois, T., Mathet, Y., Enjalbert, P., Bilhaut, F., 2004.
Geographic reference analysis for geographic
document querying. Workshop on the Analysis of
Geographic References, Human Language Technology
Conference, NAACL-HLT.
Chen, Y-Y., Suel, T., Markowetz, A., 2006. Efficient
query processing in geographic web search engines,
Proceedings of the 2006 ACM SIGMOD international
conference on Management of data, pp. 277 – 288.
Clementini, E., Sharma, J., and Egenhofer, M., 1994.
Modeling topological spatial relations: Strategies for
query processing. Computers and Graphics.pp. 815-
ICEIS 2007 - International Conference on Enterprise Information Systems
Cohn, A. G., and Hazarika, S. M., 2001. Qualitative
spatial representation and reasoning: An overview.
Fundamenta Informaticae, 46(1-2):1-29.
Da Silva, J., Times, V.C., Salgado, A.C., 2006. An Open
Source and Web Based Framework for Geographic
and Multidimensional Processing. Advances in Spatial
and Image based Information Systems track, ACM
Egenhofer, M. J., Franzosa, R.D., 1991. Point-Set
Topological Relations. International Journal for
Geographic Information Sytems, 5(2):161-174.
Egenhofer, M. J., 2002. Toward the semantic geospatial
web. In GIS ’02: Proceedings of the 10th ACM
international symposium on Advances in geographic
information systems, pp. 1–4. ACM Press.
Freeman, J., 1975. The Modelling of Spatial Relations.
Computer Graphics and Image Processing, 4:156-171.
Gaizauskas, R., Wilks, Y., 1998. Information extraction:
Beyond document retrieval. Journal of
Documentation, 54(1): 70–105.
Gaizauskas, R., 2002. An information extraction
perspective on text mining: Tasks, technologies and
prototype applications. Euromap TextMining Seminar.
Hill, L., 1999. Indirect geospatial referencing through
place names in the digital library: Alexandria digital
library experience with developing and implementing
gazetteers. 62nd Annual Meeting of the American
Society for Information Science, pp. 57-69. Medford,
Hill, L., 2000. Core elements of digital gazetteers: Place
names, categories, and footprints. In ECDL ’00:
Proceedings of the 4th European Conference on
Research and Advanced Technology for Digital
Libraries, pp. 280–290. Springer-Verlag.
Jones, C.-B., Abdelmoty, A.-I., Finch, D., Fu, G., Vaid, S.,
2004. The Spirit Spatial Search Engine: Architecture,
Ontologies and Spatial Indexing. Third International
Conference - Geographic Information Science,
Adelphi, Usa, pp. 125 – 139.
Lesbegueries, J., Gaio, M., Loustau, P., and Sallaberry, C.,
2006. Geographical information access for non-
structured data. ACM SAC - Advances in Spatial and
Image based Information Systems track.
Lesbegueries, J., Sallaberry, C., and Gaio, M., 2006b.
Associating spatial patterns to text-units for
summarizing geographic information. Workshop GIR
Malandain, N., Gaio, M., Madelaine, J., 2001. Improving
retrieval effectiveness by automatically creating some
multiscaled links between text and pictures. In
Proceedings of SPIE, Document Recognition and
Retrieval VIII, volume 4307, pages 89–99.
Martins, B., M. Silva, M-J., and Andrade, L., 2005.
Indexing and ranking in Geo-IR systems. In Proc. of
the 2nd Int. Workshop on Geo-IR (GIR).
Muller, P., 2002. Topological spatio-temporal reasoning
and representation. Computational Intelligence, pp.
Porter, M., 2001. Snowball: A language for stemming
Robertson, S.E., Walker, S., Hancock-Beaulieu, M.,
Gatford, M., Payne, A., 1995. Okapi at TREC-4.
Sallaberry, C., Gaio, M., Lesbegueries, J., and Loustau, P.,
2007. A Semantic Approach for Geospatial
Information Extraction from Unstructured Documents.
In The Geospatial Web, Springer. ISBN 1-84628-826-
Sanderson, M. and Kohler, J., 2004. Analyzing geographic
queries. In Proceedings of the Workshop on
Geographic Information Retrieval, SIGIR,
Torres, M., 2002. Semantics definition to represent spatial
data. International Workshop -Semantic Processing of
Spatial Data -Geopro.
Vaid, S., Jones, C. B., Joho, H., and Sanderson, M., 2005.
Spatio-textual indexing for geographical search on the
web. In Proc. of the 9th Int. Symp. on Spatial and
Temporal Databases (SSTD).
Vandeloise, C., 1986. L’espace en français. Travaux
Linguistiques. Seuil.
Wildöcher, A., Faurot, E., Bilhaut, F., 2004. Multimodal
indexation of contrastive structures in geographical
documents. In RIAO, pp.555–570.
Widlocher, A., Bilhaut, F., 2005. La plate-forme
linguastream : un outil d’exploration linguistique sur
corpus. In Actes de la 12e Conférence Traitement
Automatique du Langage Naturel.
Woodruff, A.G., Plaunt, C., 1994. GIPSY: Automated
Geographic Indexing of Text Documents. Journal of
the American Society for Information Science,
Zipf., 1949. Human Behaviour and the Principle of Least
Effort. Addison Wesley.