=Paper=
{{Paper
|id=Vol-2198/paper_101
|storemode=property
|title=Semantic Web: Ontological Search Approach
|pdfUrl=https://ceur-ws.org/Vol-2198/paper_101.pdf
|volume=Vol-2198
|authors=Marco Franke,Shantanoo Desai,Quan Deng,Stefan Wellsandt,Karl A. Hribernik,Klaus-Dieter Thoben
|dblpUrl=https://dblp.org/rec/conf/i-semantics/FrankeDDWHT18
}}
==Semantic Web: Ontological Search Approach==
https://ceur-ws.org/Vol-2198/paper_101.pdf
Semantic Web: Ontological Search Approach
Marco Franke1, Shantanoo Desai1, Quan Deng1, Stefan Wellsandt1, Karl A. Hribernik1
and Klaus-Dieter Thoben1,2
1 BIBA - Bremer Institut für Produktion und Logistik GmbH, Hochschulring 20, 28359
Bremen, Germany
fma,des,dqu,wel,hri@biba.uni-bremen.de
2 Faculty of Production Engineering, University of Bremen, 28359 Bremen, Germany
tho@biba.uni-bremen.de
Abstract. This demonstrator provides a semantic search service for a business-
to-business (B2B) platform. It bases on an ontology network that describes the
furniture industry. Platform users typically do not know its internal structure.
They cannot search through its resources quickly and unambiguously. Facet
Search services often have pre-defined facets. They do not scale well with an
increasing number of products. Our demonstrator provides web interfaces for an
Explorative Search to address this issue. It supports platform users to explore the
ontology network quickly and precisely. The demonstrator creates SPARQL que-
ries dynamically from user interactions, such as clicking and typing keywords. It
has two search modes that apply product taxonomies, filters and joins. The user
does not have to use SPARQL directly. The demonstrator is available on
http://hydra2.ikap.biba.uni-bremen.de:9092. It is part of a B2B platform which is
available on http://bit.ly/nimble-explorative.
Keywords: Federated Platform, Semantic Search, Semantic Web, Usability
1 Introduction
The demonstrator described in this article bases on a new, federated, web-based, open-
source B2B platform.1 It has a service-oriented architecture. Companies can publish
digital versions of product/service catalogues on this platform. Buyers can search them
to identify relevant offers and start a negotiation process about prices and delivery con-
ditions. This paper focuses on the platform’s product search. Search filters can use one
or more properties (facets). Table 1 shows important differences between the platforms
facetted search (Solr service) and its explorative search (new service).
Table 1. Comparison of Facetted Search and Explorative Search
Facetted search Explorative search
Developer creates facets (static) User defines properties for facet creation at
runtime (dynamic)
1 NIMBLE Project: https://www.nimble-project.org
2
Visualization of defined facets Visualization of relevant facets
Facet visualization does not scale Facet visualization limited to relevant ones
The total number of products and services is large and distributed over many cate-
gories. Their conceptual structure contains multi-stage taxonomies and similar proper-
ties. It is challenging to find the needed information in this structure from the user’s
perspective. Similar products in a catalogue differ from each other in a few properties.
This means that entering a generic keyword in the search interface would retrieve many
irrelevant results. The user needs an improved search functionality to focus on property-
based information. The proposed search service takes into consideration the following:
Apply ontological structure to allow generic and specific search.
Allow the exact formulation of the search query for specific product features.
Support the search with consistent and stable terminology.
The common terminology is independent of a specific catalogues but rather it offers
high-level and product-specific concepts. To achieve these search capabilities via an
ontology, a search service is necessary that simplifies complex query languages. This
demonstrator relies on catalogues which are represented as ontologies in Web Ontology
Language.
We present a graph-based search and a semantic pattern search. Graph-based search
visualizes the available concepts and properties in an interactive graph. The user can
observe the ontological meaning for an entity making it useful for dynamic catalogues.
The semantic pattern search uses list of properties within panels to enable a direct
search without obtaining deeper explanations of the underlying product structure. The
next section presents both search types.
2 Approach for Search Services
This section describes the approaches of the developed search service. They have dif-
ferent assumptions and goals as summarized in Table 2.
Table 2. Comparison of Graph Based Navigation and Semantic-Pattern-Based Approach
Graph-Based Navigation Semantic-Pattern-Based Approach
User has a product category in User has a specific product from a B2B
mind and searches a keyword catalog in mind. She knows the relevant
Assump-
such as a chair. The user has properties and wishes to reach the desired
some product properties in product by applying filters quickly.
tions
mind.
3
User shall have complete Usage of pre-defined terminology for
freedom of the product se- quick search
lection Reduction of search space to the point
The search offers all possi- of interest
ble search direction Creation of complex queries through
The user familiarizes the
Goals
minimal set of actions
terminology while search-
ing for a product
2.1 Graph-Based Navigation Approach
The example user searches a product category through the Explorative Search. She en-
ters the keyword “chair” and has some of its relevant properties in mind. The user wants
to explore more properties for the product. The goal of the graph-based search is to
provide the user complete freedom of selection. It helps her to visualize the complex
Ontology through an interactive, radial net graph. Its center is the searched product and
the connected nodes are direct datatypes, derived datatypes, or object properties of the
product (left part of Fig.1). Interactions, such as double-clicking a node, select the prop-
erty and provide a filter for numeric values. A click on a selected node removes it from
the selection.
Fig. 1. Left part illustrates a part of a complete ontology for a product with related datatype
(green) and object properties (red). Right side illustrates the change in the graph when selection
of an object property occurs.
A double click on the object properties (red colored property) of the root concept up-
dates the graph with new property of the already selected object property. It displays
the intermediate nodes as a single node with names of these nodes separated using a
forward slash.
4
The right part of Fig. 1 illustrates when the user wants to filter products based on
properties in Legislation. Intermediate nodes collapse to support the user in keeping the
focus on relevant concepts. The datatype property hasLegislationName appears and re-
places the node Legislation.
2.2 Semantic-Pattern-Based Approach
We assume that the user has a specific product from the catalogue in mind. She is aware
of its relevant properties and wants to apply filters quickly. This approach relies on two
panels as illustrated in Fig. 2. The left panel provides the possible datatype properties
and object properties. Upon clicking on a datatype property, the values for filtering
become available on the right panel. If the user selects any of the filter properties, the
backend selects this property for the SPARQL execution. If the user does not select a
filter pertaining to a particular property then we assume that the property is not of in-
terest and the user would only like to observe the filter values.
If the user selects an object property, the left panel updates with a new set of prop-
erties and references associated with this object property. A “breadcrumb” view on top
of the panel provides an interaction history. The left panel updates with the relevant
datatype properties and object properties upon clicking a breadcrumb.
Fig. 2. Semantic Query Pattern Interface for a product
Four application scenarios provide product catalogs and domain experts for the demon-
strator’s evaluation. They cover the wooden furniture, wooden house, white goods and
textile industries. The demonstrator can use any product catalog as long as it bases on
an ontology. This makes the explorative search service applicable to many domains.
Acknowledgments
This project has received funding from the European Union's Horizon 2020 research
and innovation programme under grant agreement no. 723810 (NIMBLE).