=Paper=
{{Paper
|id=Vol-1741/paos2016_paper2
|storemode=property
|title=Development of Faceted and Synonym Search Support for the Ontology Application Management Framework
|pdfUrl=https://ceur-ws.org/Vol-1741/paos2016_paper2.pdf
|volume=Vol-1741
|authors=Marut Buranarach,Pattama Krataithong,Nichanan Poovanavirote,Palita Anantanitivate,Nussara Siriset
|dblpUrl=https://dblp.org/rec/conf/jist/BuranarachKPAS16
}}
==Development of Faceted and Synonym Search Support for the Ontology Application Management Framework==
https://ceur-ws.org/Vol-1741/paos2016_paper2.pdf
Development of Faceted and Synonym Search Support
for the Ontology Application Management Framework
Marut Buranarach1, Pattama Krataithong1,2, Nichanan Poovanavirote3,
Palita Anantanitivate3, and Nussara Siriset3
1
Language and Semantic Technology Laboratory
National Electronics and Computer Technology Center (NECTEC), Pathumthani, Thailand
{marut.bur,pattama.kra}@nectec.or.th
2
Department of Computer Science, Faculty of Science and Technology
Thammasat University, Pathumthani, Thailand
3
Department of Computer Science, Faculty of Science, Kasetsart University, Bangkok, Thailand
Abstract. Semantic search is a form of search that goes beyond keyword-based
searching. Searching based on keywords typically has several disadvantages
including homonym and synonym problems which can reduce the retrieval
effectiveness of a search system. Ontology-based search is a form of semantic
search that can be applied to searching structured data, i.e. RDF data, which are
exported from relational database. The Ontology Application Management
(OAM) framework can provide support for ontology-based search application
development over RDF data using an application template that generates
queries based on SPARQL template. However, OAM still relies on keyword-
based search when the properties are datatype properties, i.e. those having
property values as literals. In this paper, we propose to use faceted and
synonym search to augment the keyword-based search over datatype property
values. One of the main goals is to provide a generic framework for improving
the effectiveness of searching RDF data. Our system design adopted the
service-oriented architecture (SOA) approach in creating reusable service
components. Two key components are synonym and aggregation service Web
APIs. The SPARQL query templates for implementing synonym and faceted
search are described. Finally, we demonstrate an adoption of the framework in
searching a large-scale database in the professional qualification domain.
Keywords: ontology-based search, semantic search, RDF, SPARQL aggregation
1 Introduction
Semantic search is a form of search that goes beyond keyword-based searching.
Searching based on keywords typically has several disadvantages. First, there are
many different terms that share the same meanings, i.e. synonyms. If the user's query
terms do not match with the terms in the document or database, the retrieval
effectiveness will be reduced. Second, one term can mean different things, i.e.
homonyms. When the user's query terms are ambiguous, the retrieval effectiveness of
the search system will be reduced. Thus, one goal of semantic search is to improve the
retrieval effectiveness of traditional keyword-based searching.
Ontology-based search is a form of semantic search that can be applied to
searching structured data. Ontology in the OWL (Web Ontology Language) standard
can be used to define structure of the RDF data exported from some database sources
using some mapping languages, e.g. D2RQ mapping language [1], R2RML [2]. The
resulted RDF data can be queried using SPARQL [3]. By applying ontology over
RDF data, concept-based search can be conducted over the ontology-based inferenced
data. The OAM Framework [4] is an application framework that can simplify
ontology-based application development. The OAM framework can provide support
for ontology-based search application development over RDF data using an
application template that generates queries based on a SPARQL template.
OAM provides support for concept-based search based on property-value search
conditions. Although this technique is effective when the properties in the search
conditions are object properties, i.e. those having instances of some concepts as
property values, it still relies on keyword-based search when the properties are
datatype properties, i.e. those having property values as literals. In this paper, we
propose to use faceted and synonym search to augment the keyword-based search
over datatype property values. Synonym search using query expansion technique can
improve coverage of retrieved resources when the search terms do not match with the
database terms. Faceted browsing over the retrieved results can additionally group the
results based on different properties and values which would allow the user to
eliminate non-relevant results. In our framework, the service-oriented architecture
(SOA) is adopted to allow for reusable components. The synonym and aggregation
service Web APIs are among the key components of the framework. We describe
some SPARQL query templates that enable synonym and faceted search support.
Finally, we show our adoption of the framework in a search system over the Thailand
Professional Qualification Database.
2 Background
2.1 SPARQL Aggregation Query
SPARQL 1.1 [3] provides support for aggregation operators. Aggregation functions
allow the search results of multiple rows to be reduced into single values. Common
aggregate functions include COUNT, COUNT DISTINCT, SUM, AVERAGE, MIN,
and MAX. The results of aggregations can be partitioned into one or more groups
based on the specified values in columns, i.e. GROUP BY. The partitioned results
contain one row per aggregated group. The syntax of aggregations in SPARQL
queries is similar to those of SQL aggregation queries. Fig. 1 shows an example of
SPARQL aggregation query.
Fig. 1. Example of SPARQL Aggregation Query
2.2 Concepts and Terms
From an ontology viewpoint, concepts are abstract representation of objects in the
real-world. Concept meanings are independent of the terms used to refer to the
concepts. A term may represent different concepts, i.e. homonyms. Different terms
may also represent the same concept, i.e. synonyms. Fig. 2 shows the relationship
between terms, concepts and objects.
Fig. 2. Relationship between term, concept and object
The ambiguity of meanings of some terms can cause problems in searching.
Specifically, a ‘hyponym’ or ambiguous search term can reduce precision of the
retrieval while a search term that has synonyms can result in reduced recall. Thus,
different concept-based searching techniques based on ontology are proposed to
overcome limitation of keyword search [5, 6]. These techniques include query
expansion and faceted search [7].
2.3 The Ontology Application Management (OAM) Framework
The Ontology Application Management (OAM) Framework [4] is a java-based web
application development platform which helps user to build a semantic web
application without programming skill required. The underlying technology of OAM
is Apache Jena [8], D2RQ and RDF data storage. OAM includes three main modules
follows as:
Database-to-Ontology Mapping provides a user interface for mapping
between an existing relational database schema and ontology file (OWL).
This process helps users who do not have a programming skill in mapping
and converting relational database data to RDF format.
Ontology-based Search Engine provides a form-based SPARQL data
querying service for users to query each dataset by defining search
conditions.
Recommendation Rule System provides a simplified interface for rule
management. Users can define a condition of rules that do not require
knowledge of the rule syntax of reasoning engine.
OAM has been used to support development of many ontology-based applications
in different domains including smart home [9], excise duty [10] and open data [11]
domains.
3 Faceted and Synonym Search Support Framework for OAM
3.1 System Architecture
Fig. 3. System architecture of the faceted and synonym search support component of OAM
The faceted and synonym search support component is developed using the service-
oriented architecture (SOA). Fig. 3 shows an overall architecture of the system. The
components consist of two main RESTFul Web APIs: synonym service and
aggregation service APIs. The synonym service API is used by the query expansion
module, which generates an expanded set of the user query terms. The aggregation
service API is used by the faceted browsing module, which summarizes the search
results according to different property values and their unique counts. Both
components will interact with the SPARQL query template generation module to
generate the SPARQL queries accordingly.
The faceted and synonym search support component is reusable and can be used by
the OAM framework or other applications. The end-users can use the searching
functions by means of the OAM search application template. Using the application
template, the user can define search conditions in a search form which will be
transformed to SPARQL queries in searching the RDF data. The query expansion
technique will be applied to the user query terms for synonym-based search. After the
results are retrieved, the faceted browsing module will list groups of the results by
property values of different properties and unique counts of their members. When the
user clicks on label of a group, the system adds a filter condition to the original search
conditions of the user query to filter the search results. The synonym and aggregation
service Web API may also be used independently of the OAM search application.
3.2 OAM Aggregation Web API
The OAM aggregation Web API applies aggregation functions on top of the OAM
search Web API [4]. The OAM search Web API accepts the following parameters:
dataset name, search properties, search operators and search values. The aggregation
Web API adds the following parameters to the search Web API: aggregation function,
aggregation property and groupby property. Fig. 4 shows an example of the
aggregation API results in JSON format. In this example, searching in qualification
database applied an aggregation function to count the number of search results based
on a property of industry name. The parameters for the Web API are:
- Aggregation function : count
- Aggregation property : has_id
- Groupby property : has_industry>>has_name
Aggregation Property (select property)
Aggregation Function
Groupby property
Fig. 4. Example of results of OAM aggregation Web API in JSON format
The results of unique property values and counts of their members are then
returned. The results will be used to further refine the filter condition of the original
query. Specifically, the returned group labels which represent each property value will
be used as a filter condition added to the original query.
3.3 Synonym Service Web API and Synonym Management System
The synonym service Web API retrieves the synonym sets from the synonym
database given a query term. Fig. 5 shows an example of the API results in JSON
format. In this example, a query term ‘วัด’ is a homonym which can have the meaning
of ‘measurement’ or ‘temple’. In this case, the synonym service API returns two
synonym sets (‘วัด’,‘ตวง’) (‘วัด’,‘อาราม’) for the two different meanings accordingly.
Fig. 5. Example of results of the synonym service Web API in JSON format for a hyponym
A synonym management system was created to support domain experts in
management of synonym sets. The system consists of a keyword extraction system,
which selects some key terms from a database and list them as the seed words for the
domain experts to define synonym sets. The system also provides synonym set
merging function to allow the experts to group multiple synonym sets into one set.
The system also provides a function of linking synonym set to a concept URI in
ontology file.
3.4 SPARQL Query Templates
3.4.1 SPARQL Query Template for Faceted Search
PREFIX ns:
PREFIX rdf:
SELECT (count(?agg_prop) AS ?agg_prop_count) ?grpby_prop
WHERE
{ ?x rdf:type ns:Class1 .
?x ns:agg_prop ?agg_prop .
?x ns:grpby_prop ?grpby_prop .
?x ns:search_prop ?search_prop
FILTER regex(?search_prop, "term1", "i")
}
GROUP BY ?grpby_prop
Fig. 6. Example of SPARQL query template for listing values of a facet and their counts
In order to implement a faceted search, SPARQL aggregation queries must be formed
in order to generate list of facets and its values. Fig. 6 shows an example of a
SPARQL query template for listing values of a facet (‘ns:grpby_prop’) and their
counts (counting values of ‘ns:agg_prop’) given a search conditions (e.g.,
‘ns:search_prop’ contains “term1”). When the user click on a facet value, a filter
condition is added to the original search condition as shown in Fig. 7.
FILTER (regex(?search_prop, "term1", "i") && ?grpby_prop = "-user_selected_value-")
Fig. 7. Modified FILTER condition of a query for faceted browsing when a value is selected
3.4.2 SPARQL Query Template for Synonym Search
PREFIX ns:
PREFIX xsd:
PREFIX rdf:
SELECT DISTINCT ?id
WHERE
{ { ?x ns:search_prop1 ?a0
FILTER ( ( regex(?a0, "syn1", "i") || regex(?a0, "syn2", "i") ) || regex(?a0, "syn3", "i") )
?x ns:id_prop ?id .
?x rdf:type ns:Class1
}
UNION
{ ?x ns:search_prop2 ?a1
FILTER ( ( regex(?a1, "syn1", "i") || regex(?a1, "syn2", "i") ) || regex(?a1, "syn3", "i") )
?x ns:id_prop ?id .
?x rdf:type ns:Class1
} }
Fig. 8. Example of SPARQL query template for synonym-based search
In order to implement a synonym search, a SPARQL query combining query
expansion must be formed in order to include the related synonym sets in the query.
Fig. 8 shows an example of a SPARQL query template for creating a synonym search
given a keyword as a search term over all the properties (‘ns:search_prop1’,
‘ns:search_prop2’). In this example, the synonym set for the search term consists of
the following terms: “syn1”, “syn2”, and “syn3”. The results are a list of ID values
(‘ns:ip_prop’) of the matched resource.
4 System Implementation and Scenario
The framework has been adopted in a search system for the Thailand Professional
Qualification Institute (TPQI)’s TPQI-Net database1. The search system was built
1
http://tpqi-net.tpqi.go.th/tpqi_sa/
using the OAM framework. As of April 2016, the database contains the information
of thousands of units of competencies of over 200 qualifications developed for over
25 industries.
One of the problems in searching the database was that the user’s search terms
are frequently informal terms that do not match with the technical terms used in the
database. The synonym search approach was adopted in order to allow the user to use
informal terms to search the qualification database. In addition, the faceted search
approach was adopted to allow the users to filter the results according to the facets
that they are interested in to improve the result accuracy.
The development of synonyms database starts with the keyword extraction service,
which extracts the technical terms appeared in the standards database. The human
experts then define some casual terms, which are those likely used as the user’s query
terms, as synonyms of each term. This results in the synonym sets database.
In processing a query, the search system uses the query expansion technique,
which maps the user’s query terms with the related synonym sets via the synonym
service Web API. If matched, the user’s query terms are then extracted to the database
terms which will result in the retrieval of the related qualifications. Using this
technique, when the user’s query term does not appear in the database but is defined
in a synonym set, the search results will be equivalent to those of the keyword-based
search using the associated term appeared in the database.
Query expansion
Faceted browsing
Fig. 9. Example of synonym search combined with faceted browsing in TPQI-Net system
Fig. 9 shows an example search results when using synonym-based search in
combination with faceted browsing. In this example, the user uses a query term
“NGV” which does not appear in the database. When the synonym search is applied,
the related database terms included in its synonym set will be used. Thus the relevant
results will be returned. The user can further filter the results based on a facet of
industry name, which subsequently improves the accuracy of the search results.
5 Conclusion
In this paper, we describe an approach to extend the OAM framework to support
faceted and synonym search over RDF data. One of the main goals is to provide a
generic framework for improving the effectiveness of searching RDF data. Our
system design adopted the SOA approach in creating reusable service components.
Two key components are synonym and aggregation service Web APIs. The SPARQL
query templates for implementing synonym and faceted search are described. Finally,
we demonstrate an adoption of the framework in searching a large-scale database in
the professional qualification domain. Our future work will focus on supporting
faceted browsing of hierarchical structure of property values and extending the
synonym service to support more relationship types of terms, e.g. is-a and part-of
relations.
References
1. Bizer, C., Seaborne, A.: D2RQ-Treating Non-RDF Databases as Virtual RDF Graphs. In:
Poster at the 3rd International Semantic Web Conference (ISWC2004) (2004).
2. W3C: R2RML: RDB to RDF Mapping Language, https://www.w3.org/TR/r2rml/.
3. W3C: SPARQL 1.1 Query Language, https://www.w3.org/TR/sparql11-query/.
4. Buranarach, M. et al.: OAM: An Ontology Application Management Framework for
Simplifying Ontology-Based Semantic Web Application Development. Int. J. Softw. Eng.
Knowl. Eng. 26, 01, 115–145 (2016).
5. Mäkelä, E.: Survey of semantic search research. In: Proceedings of the seminar on
knowledge management on the semantic web (2005).
6. Mangold, C.: A Survey and Classification of Semantic Search Approaches. Int. J.
Metadata Semant. Ontologies. 2, 23–34 (2007).
7. Hearst, M.: Design recommendations for hierarchical faceted search interfaces. In: ACM
SIGIR Workshop on Faceted Search (2006).
8. Carroll, J.J., Reynolds, D., Dickinson, I., Seaborne, A., Dollin, C., Wilkinson, K.: Jena:
Implementing the Semantic Web Recommendations. In: Proc. of the 13th International
World Wide Web Conference on Alternate Track Papers & Posters, pp. 74–83 (2004).
9. Wongpatikaseree, K., Ikeda, M., Buranarach, M., Supnithi, T., Lim, A. O., and Tan Y.:
Activity Recognition using Context-Aware Infrastructure Ontology in Smart Home
Domain. In: Proc. of the 7th International Conference on Knowledge, Information and
Creativity Support Systems (KICSS2012) (2012).
10. Buranarach, M., Ruangrajitpakorn, T., Anutariya, C., Wuwongse, V.: Ontology Design
Approaches for Development of an Excise Duty Recommender System. In: Kawtrakul, A.,
Laurent, D., Spyratos, N., and Tanaka, Y. (eds.) Information Search, Integration, and
Personalization. CCIS, vol. 421, pp. 119–127. Springer International Publishing (2014).
11. Krataithong, P., Buranarach, M., and Supnithi, T.: RDF Dataset Management Framework
for Data.go.th. In: Proc. of the 10th International Conference on Knowledge, Information
and Creativity Support Systems (KICSS2015) (2015).