=Paper=
{{Paper
|id=None
|storemode=property
|title=Semantic Search Architecture for Retrieving Information in Biodiversity Repositories
|pdfUrl=https://ceur-ws.org/Vol-1041/ontobras-2013_paper29.pdf
|volume=Vol-1041
|dblpUrl=https://dblp.org/rec/conf/ontobras/AmanquiSLASM13
}}
==Semantic Search Architecture for Retrieving Information in Biodiversity Repositories==
https://ceur-ws.org/Vol-1041/ontobras-2013_paper29.pdf
Semantic Search Architecture for Retrieving Information in
Biodiversity Repositories
Flor K. Amanqui1 , Kleberson J. Serique1 , Franco Lamping1 ,
Andréa C. F. Albuquerque22 , José L. C. Dos Santos2 , Dilvan A. Moreira1
1
University of São Paulo (USP) – CEP: 13566-590 – São Carlos – SP – Brazil
2
National Institute for Amazonian Research (INPA)
CEP.: 69060-001 – Manaus – AM – Brazil
{flork,serique,dilvan}@icmc.usp.br, lamping@grad.icmc.usp.br
andreaalb.1993@gmail.com, lcampos@inpa.gov.br
Abstract. The amount of biological data available electronically is increasing
at a rapid rate; for instance, over 16.500 specimens are available today in the
National Institute for Amazonian Research (INPA) collections. However, this
data is not semantically categorized and stored and thus is difficult to search.
To tackle this problem, we present a semantic search architecture, implemented
using state of the art semantic web tools, and test it on a set of representative
data about biodiversity from INPA. This paper describes how the mechanism of
mapping is designed so that the semantic search can find information, based on
ontologies. We show a series of SPARQL queries and explain how the mapping
mechanism works. Our experiments, using a prototype of the proposed architec-
ture, showed that the prototype had better precision and recall then traditional
keyword based search engines.
Keywords: Biodiversity, Ontology, Data Integration, Semantic Search
1. Introduction
Biological diversity, or biodiversity, is the term given to the variety of life on Earth. Bio-
diversity is the combination of life forms and their interactions with one another, and with
the physical environment that has made Earth habitable.
The biodiversity information that can be obtained via Internet continues to grow
significantly. Every day, new collections, databases, and applications are being added.
This information is stored in a variety of formats (spreadsheets, html, xml, pdf and cata-
logues, amongst others). This proliferation of information from different sources means
that the search for information could be met by a variety of available resources, which
store data about the same domains but have different characteristics. For that reason,
much of this information is never found. The need for integration and analysis of biodi-
versity information becomes evident.
In this context, finding relevant and recent information is a hard task that is not
particularly well supported by current biodiversity software tools. Keyword-based search
have serious problems associated with its use: low or no recall; high recall, low precision;
initial keywords in search often do not get the wanted results.
83
The semantic web (an extension of the current Web) tries to represent information
in such a way that it can be used by machines, not just for display purposes, but also for
automation, integration and reuse across applications [Boley et al. 2001].
There are a number of important technologies related to the Semantic Web: on-
tologies, languages for the Semantic Web, semantic search, semantic markup of pages and
services (that the Semantic Web is supposed to provide). Ontologies, one of the most im-
portant ones, are implemented in the RDF(S) (Resource Description Framework/Schema)
and OWL (Web Ontology Languages) languages, two W3C recommended data represen-
tation models.
In this article, we propose a semantic search architecture that supports mapping
between biodiversity data, from INPA’s (National Institute for Amazonian Research) col-
lections, stored in relational databases and the ontologies describing it.
The rest of this article is organized as follows: Section 2 describes related works.
Section 3 describes our biodiversity ontology. Section 4 presents our semantic search
architecture. Section 5 presents a synopsis of our experiments and Section 6 concludes
by summarizing our results and describing future works.
2. Related Works
Researchers have proposed various techniques and approaches designed to perform se-
mantic search. We studied a number of them that could be used in the area of biodiversity.
In [Xiong et al. 2009], a method of search based on a smart query agent is pro-
posed (Geoonto). It retrieves information from data catalogs/databases using ontologies.
This method associates semantic information in the search process, and generates a re-
fined query string.
In [Latiri et al. 2012], an automatic method of query expansion is proposed in
which user requests are expressed in natural language.
In [Mittal et al. 2010], a method hybrid of personalized web information is pro-
posed in which ontology for retrieval of user context is used and a user profile is being
maintained.
In [Li and Yang 2008], a method to construct a semantic search engine is pro-
posed. It provides a uniform platform to search, view and operate spatial on information.
In [Santos et al. 2011], an architecture to support semantic search in a metadata
repository is proposed. This work discover similar concepts even when different terms
are used in their designation or description, since a domain ontology is used to anno-
tate information sources and to expand the user query with terms from the universe of
discourse.
A number of techniques have been developed for using ontologies to retrieve rel-
evant documents in response to a query. However, none of them focused on the problem
of storage and retrieval of RDF triples. Most of these techniques require complex analy-
sis, involving natural language processing, to discover the context and semantics of query
terms. Also, an additional limitation, in many of the existing approaches, is the lack of a
quality evaluation of results.
84
We have developed a semantic search application that uses key semantic web con-
cepts for information retrieval and also technologies such as mapping, triple store and
SPARQL queries.
3. The Biodiversity ontology
OntoBio is a biodiversity ontology developed by INPA and UFAM (Federal University
of Amazonas) and extended by USP (University of São Paulo). Its main objective is to
provide a clear and precise conceptualization of the aspects considered in biodiversity
data collection, regardless of a specific application.
The original version of OntoBio is presented in details at [Albuquerque 2011].
One of the advantages of having data annotated using OntoBio concepts is that it can be
reused as Linked Data. Linked Data describes a method of publishing structured data so
that it can be interlinked and become more useful [Kauppinen and de Espindola 2011].
To better archive that, data annotated using OntoBio has to be easily interlinked
with other biodiversity data, already available on the web (as part of the wider Linked
Data community), through the use of as many shared concepts as possible. With that in
mind, we rewrote the first version of OntoBio to reuse, whenever possible, terms from
other public available ontologies to allow better ”linkability” with data already annotated
using them.
We added terms from the following public ontologies:
• The Phenotypic Quality Ontology[PATO 2010], which is an ontology of pheno-
typic qualities, intended for use in a number of applications, primarily defining
composite phenotypes and phenotype annotation;
• Basic Geo Vocabulary [WGS84 2003] is a basic RDF vocabulary that pro-
vides the semantic web community with a namespace for representing lat(itude),
long(itude) and other information about spatially-located things, using WGS84 as
a reference datum, and;
• The Geoname Ontology[GeoNames 2011] makes it possible to add geospatial se-
mantic information to the Word Wide Web. All over 8.3 million geonames to-
ponyms now have a unique URL with a corresponding RDF web service.
The OntoBio ontology is presented in the Figure 1. The Protégé
4 ontology editor was used to write the OntoBio ontology in OWL 2 DL.
The new version of OntoBio is available through the NCBO’s Bioportal
http://bioportal.bioontology.org/ontologies/50517. There, users can download, browse
and suggest terms for the ontology.
4. An Architecture for Semantic Search
We have proposed the architecture of a semantic search that follows the mechanism of
mapping between OntoBio domain ontology, and Database from INPA the collections of
insects, fishes, and mammals.
The system overall architecture is shown in Figure 2. It consists of four basic
modules: User Interface Layer, Query Reformulation, Mapping Component and Data
Access Layer.
85
Figure 1. New version of biodiversity ontology
1. User Interface Layer is responsible for the interaction between users and system.
The search process begins with an initial keyword list, entered by the users, that
represents his/her search intentions.
2. The Query Reformulation component receives the input of search terms from
the user, selects and expands keyword lists by adding semantically related terms,
using techniques of expansion and semantic similarity. It uses the SPARQL Writer
component to take keyword lists and generate SPARQL queries from them. It uses
an algorithm that will be described on the following sections.
3. The Mapping Component loads the domain ontologies, taxonomic information
and the collection database and transforms them in a set of Resource Descrip-
tion Framework (RDF) triples. We used Ontop, a platform to query databases as
Virtual RDF Graphs using SPARQL, to do the mapping between the relational
databases records and the OWL ontologies.
Ontop is a platform to query databases as Virtual RDF Graphs using SPARQL. It
does the mapping between the relational databases records and the OWL ontolo-
gies. Ontop has two tools: OntopPro, which is a Protege 4 plugin that implements
a graphic mapping editor; and Quest, which is a SPARQL query engine/reasoner
that supports RDFS and OWL 2 QL entailment regimes and SPARQL-to-SQL
query rewriting (Mariano R and Calvanese, 2012). The mapping process is di-
vided in three steps:
(a) Creation of Mapping Axioms: OntopPro mappings are done using map-
ping axioms. A mapping axiom is defined by an SQL query and an ABox
86
Figure 2. Architecture for Semantic Search
assertion template (Figure 3). An Abox assertion template is a set of RD-
F/OWL triples, written in a turtle-like syntax, in which the subject and
object of the triples allow for variables that reference columns of the SQL
query result [Mariano R and Calvanese 2012].
In other words, a mapping axiom defines how the values in each row of
the results (of an SQL query) can be used to generate a set of ABox as-
sertions. The mapping axioms were created using information from the
OntoBio ontology and INPA experts. Each mapping must contain one or
more mapping axioms. Figure 3 shows a valid mapping.
(b) Generation of RDF Triples: Mapping axioms generate RDF triples. This
generation is done using the Quest tool from Ontop. The Quest reasoner
uses query-rewriting techniques to generate triples. The triples are created
by replacing the placeholders in the target with the values from the SQL
row.
(c) RDF Triples Loader: Using OntopPro, it is possible to export the RDF
triples generated by the Quest tool to a file. That file is then loaded into
the Virtuoso triple store, which is now ready to answer queries using them.
The Mapping Component can repeat the process described here, whenever
INPA releases updates to its collection records.
4. Data Access layer that is the architecture layer that provides access to the RDF
triples stored in the Virtuoso Triple Store, using SPARQL, both for the layer above
it and for other machines on the network. Triple Store is the common name given
to a database management system for RDF Data.
87
Figure 3. Mapping axiom
4.1. Semantic Search Algorithm
The basic idea of our algorithm is to compare input keywords with OntoBio resources
(subject, predicate and object) in the Virtuoso triple store. The Virtuoso platform was
chosen because it can store the triples generated from INPA data and work with multiple
graphs at the same time.
I / P − S t r i n g nameClass , name o f t h e c l a s s s e l e c t e d by u s e r
O/ P − S t r i n g r e s u l t , r e s u l t o f t h e q u e r i e s
BEGIN
Step 1 : E s t a b l i s h c o n n e c t i o n with V i r t u o s o T r i p l e S t o r e
and o n t o l o g y OntoBio
S t e p 2 : e x t r a c t s OntoBio i n f o r m a t i o n and f i n d h i e r a r c h y
A t t r i b u t e i n i t i a l N a m e C l a s s as nameClass
While ( n a m e C l a s s h a s S u p e r C l a s s )
BEGIN
S t e p 3 : S u b m i t SPARQL q u e r y w i t h q u e r y u s e r a s o b j e c t and f i n d
t h e S u p e r C l a s s ( s u b j e c t ) comparing s i m i l a r i t y with p r e d i c a t e s
Step 4: Attach r e s u l t
END
S t e p 5 : S u b m i t SPARQL q u e r y w i t h i n i t i a l N a m e C l a s s a s s u b j e c t and
f i n d t h e g e o g r a p h i c a l l o c a t i o n ( s u b j e c t , p r e d i c a t e and o b j e c t ) .
Step 6: Attach r e s u l t
Step 7: Return r e s u l t
End
88
We implemented this algorithm in a prototype using: Java, Eclipse Indigo (as IDE),
Google Web Toolkit 2.5.1 to create a web client, Jena RDF framework to process (sim-
plified) SPARQL queries and Virtuoso Server as triple store.
Figure 4 shows graphic interface to support user queries. We implemented a
SPARQL Endpoint for INPA http://143.107.231.220:8890/sparql and implemented a set
of queries described on experiments section.
Figure 4. Web application for searching biodiversity information
5. Experiments
In order to validate our proposed architecture, researchers from our group and biodiversity
scientists were interviewed to categorize important information from the INPA data.
We defined use cases (Table 1) with scenarios to identify the various user tasks
and built SPARQL queries related with these use cases.
For each of the previous use cases, biodiversity experts identified the information
set each user needed for each task and examples of queries that should have returned this
information. After we tested each query, the same experts judged which results were
relevant and non relevant (relevance non relevance judgment).
This process of information feedback is commonly referred to, in the literature, as
relevance feedback [Salton 1971] when experts explicitly provide information on relevant
documents to a query [Baeza-Yates and Ribeiro-Neto 1999]. In its original formulation,
expert users inspect the query results and indicate those that are really relevant to the
search. Table [tab:InfoNeeds] shows examples of users tasks and possible query strings
to get the relevant biodiversity information.
Scientists can identify species using the taxonomic classification system no mat-
ter what their language. The taxonomic classification system is composed by a hierar-
chy (series of ranks) that shows the kinship of organisms and also, whenever possible,
ancestor-descendant relationships.
89
Table 1. Biodiversity Use Cases
Use Cases Goals Queries
Use Case 01 Identification of a species. Query1: fish ocellatus
Query2: fish brasiliensis
Query3: fish Corydoras splendens
Use Case 02 Determine information of a col- Query4: fish Hemigrammus gracilis
lect. Query5: Potamorrhaphis guianensis
Query6: Hemigrammus guyanensis
Query7: Iguanodectes spilurus
Use Case 03 Determine the best areas for Query8: Gnathocharax steindachneri
aquaculture considering differ-
ent types of species and geo-
graphical location of a collect.
The basic ranks of the taxonomic classification system are kingdom, phylum,
class, order, family, genus and species. The following SPARQL query (Listing 1) shows
taxonomic system of classification for the kingdom Animalia.
Listing 1. SPARQL query returning the taxonomy of a specie
PREFIX oo : < h t t p : / / www. owl−o n t o l o g i e s . com /
B i o d i v e r s i t y O n t o l o g y F u l l . owl#>
PREFIX owl : < h t t p : / / www. w3 . o r g / 2 0 0 2 / 0 7 / owl#>
PREFIX r d f : < h t t p : / / www. w3 . o r g / 1 9 9 9 / 0 2 / 2 2 − r d f −s y n t a x −n s#>
PREFIX r d f s : < h t t p : / / www. w3 . o r g / 2 0 0 0 / 0 1 / r d f −schema#>
s e l e c t ? phylum ? c l a s s ? o r d e r ? f a m i l y ? g e n u s ? s p e c i e s
where { oo : kingdom−A n i m a l i a oo : s u b k i n o f P h y K i n g ? phylum .
? phylum oo : s u b k i n o f C l a s s P h y ? c l a s s .
? c l a s s oo : s u b k i n o f O r d C l a s s ? o r d e r .
? o r d e r oo : s u b k i n o f F a m O r d ? f a m i l y .
? f a m i l y oo : subkindOfGenFam ? g e n u s .
? g e n u s oo : s u b k i n d O f E s p G e n ? s p e c i e s .
}
The following SPARQL query (Listing 2) shows important information from a
collect such as Collect, Research Institution, Method, Determinate Name.
Listing 2. SPARQL query returning information of a collect
PREFIX : < h t t p : / / www. owl−o n t o l o g i e s . com /
B i o d i v e r s i t y O n t o l o g y F u l l . owl#>
PREFIX owl : < h t t p : / / www. w3 . o r g / 2 0 0 2 / 0 7 / owl#>
PREFIX r d f : < h t t p : / / www. w3 . o r g / 1 9 9 9 / 0 2 / 2 2 − r d f −s y n t a x −n s#>
PREFIX r d f s : < h t t p : / / www. w3 . o r g / 2 0 0 0 / 0 1 / r d f −schema#>
s e l e c t ? c o l l e c t ? R e s e a r c h I n s t i t u t i o n ? MethodCollect
? N a m e D e t e r m i n a t e C o l l e c t where {
? collect : mediationInstituicaoVinculo ? ResearchInstitution .
? c o l l e c t : isClassifiedAsColetaTipoColeta ? MethodCollect .
? c o l l e c t : mediationColetaRespColeta ? NameDeterminateCollect . }
90
The following SPARQL query (Listing 3) shows the geographical location of a specimen
collect and other data, such as collect local, geographic space, latitude and longitude.
Listing 3. SPARQL query returning geographical location of a collect
PREFIX : < h t t p : / / www. owl−o n t o l o g i e s . com /
B i o d i v e r s i t y O n t o l o g y F u l l . owl#>
PREFIX owl : < h t t p : / / www. w3 . o r g / 2 0 0 2 / 0 7 / owl#>
PREFIX r d f : < h t t p : / / www. w3 . o r g / 1 9 9 9 / 0 2 / 2 2 − r d f −s y n t a x −n s#>
PREFIX r d f s : < h t t p : / / www. w3 . o r g / 2 0 0 0 / 0 1 / r d f −schema#>
s e l e c t ? CollectLocal ? GeographicSpace ? l a t i t u d e ? l o n g i t u d e
where {
? C o l l e c t L o c a l : localizationEspaGeoCoordGeo ? GeographicSpace .
? GeographicSpace : l a t i t u d e ? l a t i t u d e .
? GeographicSpace : l o n g i t u d e ? l o n g i t u d e .
}
To evaluate our semantic search architecture, we measured precision and recall to assess
the performance of each approach dependent on input variable such as the user query. The
recall value measures whether a tool retrieves all possible items related to the search terms
contained in the data store, while precision measures to what extent only the relevant items
were actually returned.
We compared the result in two search systems, our semantic search and keyword
based search from SpeciesLink with data from INPA. We used a total of 16 queries (8 for
each system).
To compare the results of only two systems, we will employ the Students T-tests,
since they are designed for testing two data sets [B. Rasch and Naumann 2004]. When
checking two data sets, each characterized by its average, standard deviation, and number
of data points, it is possible to apply the T-test to identify, whether the means are in
fact distinct or not. A probability value (p-values) below 0.05 indicates a statistically
significant difference, whereas a p-value equal or exceeding 0.05 indicates no significant
evidence, that there exists no significant difference between the performance values of
two or more tools [Sachs 2003].
In our experiments, Semantic Search resulted in is significant difference in recall
(p=0.0201 by t-test) and precision (p= 0.0006 by t-test) when compared to Keyword based
search. One reason might be that keyword based search is not enough to capture the un-
derlying semantics of user information needs, since it is content-oriented. This evaluation
is shown in Table 2.
There is a significant difference in the mean of precision in Semantic Search mi-
nus the mean precision in Keyword Search equals 0.50416. The confidence interval of
this difference from 0.302420283 to 0.705913042 is 95%. The mean of recall in Seman-
tic Search minus the mean precision in Keyword Search is equals 0.20624745663. The
confidence interval of this difference from 0.04330054489 to 0.36919436836 is 95%.
91
Table 2. Students T-tests
Group Semantic Search Keyword Based Semantic Search Keyword Based Search
(Recall) Search (Recall) (Precision) (Precision)
Mean 0.587638057 0.3813906 0.975 0.470833338
Queries 8 8 8 8
6. Conclusions and Future Work
The architecture presented in this work provides a new document retrieval process by
exploiting query terms to support scientists in the process of discovery and integrating
biodiversity data and domain knowledge. This architecture can be classified, according to
the categorization schema proposed by [Mangold 2007], as a Stand-Alone Search Engine.
The search process uses resources labels from classes, properties, mappings and instances
from domain ontologies represented in the OWL language.
We defined a mapping mechanism between relational database data and OntoBio
ontology terms resulting in the generation of RDF triples (subject, object and predicate)
saved in a triple store (Virtuoso). The triple stores make it much easier to add new predi-
cates and write complicated queries or perform inferencing and rule processing.
A comparative analysis showed a significant increase in recall and precision in
the semantic search. The possibility of creating queries that seek information based on
relationships between data offers many alternatives to semantic search systems, since the
results of these queries are not based only on specific information. Users can thus receive
data that, in traditional systems, would not be considered by the query, but by analyzing
their relations with other information, semantic search queries can consider them relevant.
As future work, we intend to extend our current implementation with more ad-
vanced structured searches in partnership with researches from INPA.
7. Acknowledgment
The authors would like to thank INPA for supporting this work. Thanks are also due to
researchers of INPA’s biological collections. This research was financed by the Brazilian
funding agency CNPq.
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