=Paper=
{{Paper
|id=Vol-1189/paper_35
|storemode=property
|title=Semantic Search in RealFoodTrade
|pdfUrl=https://ceur-ws.org/Vol-1189/paper_35.pdf
|volume=Vol-1189
|dblpUrl=https://dblp.org/rec/conf/amw/CaliVNMMNORU14
}}
==Semantic Search in RealFoodTrade==
https://ceur-ws.org/Vol-1189/paper_35.pdf
Semantic Search in RealFoodTrade
Andrea Calı̀1,4 , Roberto De Virgilio2 , Tommaso Di Noia3 , Luca Menichetti2 ,
Roberto Mirizzi5 , Luca Nardini2 , Vito Claudio Ostuni3 , Fabrizio Rebecca2 ,
and Marco Ungania2
1
Dept. of Computer Science and Inf. Systems, Birkbeck University of London, UK
2
Dip. di Ingegneria, Università Roma Tre, Italy
3
Dept of Electrical and Electronic Engineering, Politecnico di Bari, Italy
4
Oxford-Man Institute of Quantitative Finance, University of Oxford, UK
5
Hewlett-Packard Laboratories, USA
andrea@dcs.bbk.ac.uk
dvr@dia.uniroma3.it
t.dinoia@poliba.it, ostuni@deelab.poliba.it
{meniluca,lucamarionardini, fabri.rebecca,mar.ungania}@gmail.com
roberto.mirizzi@hp.com
Abstract
We present RealFoodTrade (RFT), a system that allows farmers and fisher-
men to sell their products directly to the end-buyer. RFT makes use of Linked
Data sets, together with a domain ontology designed by experts, to perform
semantic search over products on sale. RFT employs geo-location technology
on mobile devices to match demand and supply according to the location.
We sketch the semantic search techniques in RFT and illustrate a prototype
tailored to the fishing industry.
1 Aim and Scope
In this paper we introduce the system Real Food Trade (RFT), which provides
a marketplace for any food producer to sell their produce directly, freeing the
producer both from the wholesaler and from the effort of retail. The main idea
is that the producer uses the system to advertise flash stands, each of which
consists of (1) a set of products, with their quantities, and (2) a delivery time
and location. From the economic point of view, this is important as several
farmers and fishermen do not have the possibility of advertising their produce,
nor to reach the end buyer in a consistent way (e.g. by means of a shop or door-
to-door delivery). The economic aspects of RFT will be presented elsewhere;
here we concentrate on the issues regarding the search of products by potential
buyers.
In RFT, buyers search for flash stands selling a certain product within a cer-
tain area. The wanted product is specified by keywords, and the system identifies
the stands that sell products which are semantically close to the input keywords.
To do this, we aim at employing novel techniques for keyword search, employing
as data sets both the ones available in the Linked Open Data cloud1 and “hand-
crafted” ontologies. We intend to empower the ontologies of the latter type with
the less-polished, but richer, information available at Linked Data stores.
Related work. Several techniques have been proposed to automatically cal-
culate the semantic relatedness between words, texts or concepts in a way corre-
sponding closely to that of human subjects. Some of the commonly used methods
derive statistical information from text corpora and combine that information
with lexical sources [8,2]. The lack of domain-specific coverage of the resources
used by these measures makes them ineffective for use in domain specific tasks
where the context plays an important role. Most classical methods to compute
semantic measures exploit particular lexical sources: corpus, dictionaries, or well
structured taxonomies such as WordNet [7]. Some of these methods explore path
lengths among nodes in taxonomies [10]; others exploit textual descriptions of
concepts in dictionaries [8], while a last group relies on annotated corpora to
compute information content [3,9]. Some recent research efforts has focused on
using Wikipedia to improve coverage with respect to traditional thesauri-based
methods [11,5]. In particular, the work in [5] proposes a method to represent the
meaning of texts or words as weighted vectors of Wikipedia-based concepts, using
machine learning techniques. Wikipedia seems to be unable to effectively discover
and evaluate implicit relationships [6]. In order to guarantee maximum coverage,
the work in [6] focuses on methods that exploit the Web as source of knowledge.
In [1] the authors propose a similarity measure that combines various similarity
scores based on page counts, with another one based on lexico-syntactic patterns
extracted from text snippets.
In the rest of the paper we will describe the Real Food Trade system, and how
it deals (and plans to deal) with the problem of keyword search on ontologies
represented as Linked Data.
2 Overview
In this section we sketch how RFT works and overview its architecture, as shown
in Fig. 1.
The system exploits the Google data cloud driven architecture2 . In particular
the essential components in the proposed mobile solution architecture are:
– Android mobile clients;
– Google Cloud Endpoints used for communications between the clients and
the backend over REST API with OAuth2 authentication;
– A backend application code running on Google App Engine3 and responsible
for serving requests from the clients.
A typical requirement for a mobile solution with a backend is to store data
outside of client devices. These data can be categorized into two groups: (i)
1
http://lod-cloud.net/
2
https://cloud.google.com/
3
https://developers.google.com/appengine/
Fig. 1. An architecture of reference
large, and typically binary objects (e.g. images) and (ii) fine-grained properties
and entities, which can also include a reference, for example, object name and
optionally bucket name or URL, to objects stored in Google Cloud Storage.
Both groups of data are managed by two modules in RFT: (i) App Engine
Authentication and (ii) the App Engine Query Analysis. The first application
is responsible to store and manage all user profiles. In particular we embedded
OpenID4 technologies to exploit existing email or social network accounting (e.g.
Gmail, Yahoo, Facebook, twitter and so on). Once the user is authenticated, the
App Engine Query Analysis is responsible to analyze the user request and to
invoke several instances of the application to retrieve all the data best fitting
the user query. To this aim, the App Engine Ontology Manager is responsible to
implement semantic matchmaking techniques exploiting the Linked Open Data
cloud.
In particular, RFT employ different RDF taxonomies that characterize the
domains of interest. Such taxonomies are indexed in the Google cloud and linked
to the LOD network (i.e. DBPedia). Then by using different heuristics (i.e. user
rating, usage statistics, and lexicographic analysis) we rank the similarities be-
tween concepts in the taxonomies. In this way RFT can exploit a weighted
similarity search based on the rank. Intuitively depending on how many prod-
ucts the user would view, the similarity distance from a concept matching the
4
http://openid.net/
required product can increase or decrease. In the next section we will discuss a
case study for our system.
Finally, a natural place to store this kind of data is App Engine Datastore. It
provides a NoSQL schemaless object data store, with a query engine and atomic
transactions. These entities will often map to the Resources exposed over Google
Cloud Endpoints API.
3 Case Study
As case study for RFT, we analyzed Fish & Seafood Markets in Chile. In partic-
ular we have two specific users: the producer (i.e. the fisherman) and the buyer
(private or business). The producer is interested to spot the available produce by
exploiting e-commerce technology (e.g. eBay model); in this case RFT provides
real time points of interest (POIs) through Google Maps representing market
announcements for all people interested in buying fish. Such POIs are invoked
by producers and generated through RFT.
For instance, Fig. 2 shows our system at work: (i) the user compiles a keyword
search query, (ii) the system retrieves all POIs on the map and (iii) the user
selects and reserves items.
Fig. 2. RFT at work.
In this scenario the keyword search query processing involves different seman-
tic tasks. To this aim, we adopted the Network of Fisheries Ontology5 provided
5
http://aims.fao.org/network-fisheries-ontologies
by the Food and Agriculture Organization of the United Nations (FAO). Such
ontology is a fine-grain classification of species: the classification is made by both
biological nature and regional proximity. Whenever a seller (fisherman) enters a
product, typing its name, a concept (corresponding to a particular type of fish)
in the ontology is associated to the product; such a node is the semantically
closest to the entered one. The association between the input and the concept in
the ontology is established in a fashion similar to that of [4], where ontological
information extracted off-line from Linked Data sets is also employed. Similarly,
whenever a buyer searches for a particular kind of fish, the semantically closest
one in the ontology is returned, as well as other ones within the same species
(alternative names for the same fish, or sub-species). The match is also per-
formed during the input (when only a partial input is entered), so that the user
can receive suggestions from the interface in order to save input time — this is
especially useful when entering input on mobile devices.
4 Discussion
We have sketched the main features of the RFT system, which provides a
location-based infrastructure for the sale of food. After having briefly illustrated
the architecture of the system and the technologies employed in it, we have pre-
sented a case study, where we employ RFT to fisheries in Chile, in particular
small fishing boats. We have shown how we integrate the FAO’s Network of
Fisheries Ontology with linked data sources in order to match entities in the
ontology with fish names entered by users (fishermen or buyers).
We plan to extend the field of application of RFT to other markets, in particu-
lar that of agriculture. This will require the automated extraction of high-quality
ontologies from Linked Data sets. Moreover, we intend to incorporate learning
algorithms that incorporate knowledge from users’ behaviour in order to enhance
the knowledge base used in the system. Last but not least, we intend to carry
out extensive experiments on real fish markets and report on the results, both
from the system point of view and from the socio-economic point of view.
Acknowledgments. Andrea Calı̀ acknowledges support by the EPSRC project
“Logic-based Integration and Querying of Unindexed Data” (EP/E010865/1).
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