We present RealFoodTrade (RFT), a system that allows farmers

and fisher-

men to sell their products directly to the end-buyer. RFT mak

es use of Linked

Data sets, together with a domain ontology designed by expert

s, to perform

semantic search over products on sale. RFT employs geo-locat

ion technology

on mobile devices to match demand and supply according to the l

ocation.

We sketch the semantic search techniques in RFT and illustrat

e a prototype

tailored to the fishing industry

Calì, Andrea

De Virgilio, R.

Di Noia, T.

Menichetti, L.

Mirizzi, R.

Nardini, L.M.

Ostuni, V.C.

Rebecca, F.

Ungania, M.

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 severa

Roberto De Virgilio

Tommaso Di Noia

Luca Menichetti

Roberto Mirizzi

Luca Nardini

Vito Claudio Ostuni

Fabrizio Rebecca

Marco Ungania

CiteSeerX

Semantic Search in RealFoodTrade

Birkbeck Institutional Research Online

Semantic Search in RealFoodTradeAndrea Cal`ı1,4, Roberto De Virgilio2, Tommaso Di Noia3, Luca Menichetti2,Roberto Mirizzi5, Luca Nardini2, Vito Claudio Ostuni3, Fabrizio Rebecca2,and Marco Ungania21Dept. of Computer Science and Inf. Systems, Birkbeck University of London, UK2Dip. di Ingegneria, Universita` Roma Tre, Italy3Dept of Electrical and Electronic Engineering, Politecnico di Bari, Italy4Oxford-Man Institute of Quantitative Finance, University of Oxford, UK5Hewlett-Packard Laboratories, USAandrea@dcs.bbk.ac.ukdvr@dia.uniroma3.itt.dinoia@poliba.it, ostuni@deelab.poliba.it{meniluca,lucamarionardini, fabri.rebecca,mar.ungania}@gmail.comroberto.mirizzi@hp.comAbstractWe present RealFoodTrade (RFT), a system that allows farmers and fisher-men to sell their products directly to the end-buyer. RFT makes use of LinkedData sets, together with a domain ontology designed by experts, to performsemantic search over products on sale. RFT employs geo-location technologyon mobile devices to match demand and supply according to the location.We sketch the semantic search techniques in RFT and illustrate a prototypetailored to the fishing industry.1 Aim and ScopeIn this paper we introduce the system Real Food Trade (RFT), which providesa marketplace for any food producer to sell their produce directly, freeing theproducer both from the wholesaler and from the effort of retail. The main ideais that the producer uses the system to advertise flash stands, each of whichconsists of (1) a set of products, with their quantities, and (2) a delivery timeand location. From the economic point of view, this is important as severalfarmers 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 potentialbuyers.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 identifiesthe stands that sell products which are semantically close to the input keywords.To do this, we aim at employing novel techniques for keyword search, employingas 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 withthe 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 methodsderive statistical information from text corpora and combine that informationwith lexical sources [8,2]. The lack of domain-specific coverage of the resourcesused by these measures makes them ineffective for use in domain specific taskswhere the context plays an important role. Most classical methods to computesemantic measures exploit particular lexical sources: corpus, dictionaries, or wellstructured taxonomies such as WordNet [7]. Some of these methods explore pathlengths among nodes in taxonomies [10]; others exploit textual descriptions ofconcepts in dictionaries [8], while a last group relies on annotated corpora tocompute information content [3,9]. Some recent research efforts has focused onusing Wikipedia to improve coverage with respect to traditional thesauri-basedmethods [11,5]. In particular, the work in [5] proposes a method to represent themeaning of texts or words as weighted vectors of Wikipedia-based concepts, usingmachine learning techniques. Wikipedia seems to be unable to effectively discoverand 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 similarityscores based on page counts, with another one based on lexico-syntactic patternsextracted from text snippets.In the rest of the paper we will describe the Real Food Trade system, and howit deals (and plans to deal) with the problem of keyword search on ontologiesrepresented as Linked Data.2 OverviewIn this section we sketch how RFT works and overview its architecture, as shownin Fig. 1.The system exploits the Google data cloud driven architecture2. In particularthe essential components in the proposed mobile solution architecture are:– Android mobile clients;– Google Cloud Endpoints used for communications between the clients andthe backend over REST API with OAuth2 authentication;– A backend application code running on Google App Engine3 and responsiblefor serving requests from the clients.A typical requirement for a mobile solution with a backend is to store dataoutside 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 referencelarge, and typically binary objects (e.g. images) and (ii) fine-grained propertiesand entities, which can also include a reference, for example, object name andoptionally bucket name or URL, to objects stored in Google Cloud Storage.Both groups of data are managed by two modules in RFT: (i) App EngineAuthentication and (ii) the App Engine Query Analysis. The first applicationis responsible to store and manage all user profiles. In particular we embeddedOpenID4 technologies to exploit existing email or social network accounting (e.g.Gmail, Yahoo, Facebook, twitter and so on). Once the user is authenticated, theApp Engine Query Analysis is responsible to analyze the user request and toinvoke several instances of the application to retrieve all the data best fittingthe user query. To this aim, the App Engine Ontology Manager is responsible toimplement semantic matchmaking techniques exploiting the Linked Open Datacloud.In particular, RFT employ different RDF taxonomies that characterize thedomains of interest. Such taxonomies are indexed in the Google cloud and linkedto the LOD network (i.e. DBPedia). Then by using different heuristics (i.e. userrating, usage statistics, and lexicographic analysis) we rank the similarities be-tween concepts in the taxonomies. In this way RFT can exploit a weightedsimilarity search based on the rank. Intuitively depending on how many prod-ucts the user would view, the similarity distance from a concept matching the4 http://openid.net/required product can increase or decrease. In the next section we will discuss acase study for our system.Finally, a natural place to store this kind of data is App Engine Datastore. Itprovides a NoSQL schemaless object data store, with a query engine and atomictransactions. These entities will often map to the Resources exposed over GoogleCloud Endpoints API.3 Case StudyAs 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 byexploiting e-commerce technology (e.g. eBay model); in this case RFT providesreal time points of interest (POIs) through Google Maps representing marketannouncements for all people interested in buying fish. Such POIs are invokedby producers and generated through RFT.For instance, Fig. 2 shows our system at work: (i) the user compiles a keywordsearch query, (ii) the system retrieves all POIs on the map and (iii) the userselects 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 provided5 http://aims.fao.org/network-fisheries-ontologiesby the Food and Agriculture Organization of the United Nations (FAO). Suchontology is a fine-grain classification of species: the classification is made by bothbiological nature and regional proximity. Whenever a seller (fisherman) enters aproduct, 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 semanticallyclosest to the entered one. The association between the input and the concept inthe ontology is established in a fashion similar to that of [4], where ontologicalinformation extracted off-line from Linked Data sets is also employed. Similarly,whenever a buyer searches for a particular kind of fish, the semantically closestone 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 usercan receive suggestions from the interface in order to save input time — this isespecially useful when entering input on mobile devices.4 DiscussionWe have sketched the main features of the RFT system, which provides alocation-based infrastructure for the sale of food. After having briefly illustratedthe 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 particularsmall fishing boats. We have shown how we integrate the FAO’s Network ofFisheries Ontology with linked data sources in order to match entities in theontology 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-qualityontologies from Linked Data sets. Moreover, we intend to incorporate learningalgorithms that incorporate knowledge from users’ behaviour in order to enhancethe knowledge base used in the system. Last but not least, we intend to carryout extensive experiments on real fish markets and report on the results, bothfrom the system point of view and from the socio-economic point of view.Acknowledgments. Andrea Cal`ı acknowledges support by the EPSRC project“Logic-based Integration and Querying of Unindexed Data” (EP/E010865/1).References1. Bollegala, D., Matsuo, Yutaka, M.: Measuring semantic similarity between wordsusing web search engines. In: Proceedings of the 16th international conference onWorld Wide Web. pp. 757–766. WWW ’07, ACM, New York, NY, USA (2007),http://doi.acm.org/10.1145/1242572.12426752. Budanitsky, A., Hirst, G.: Semantic distance in wordnet: An experimental,application-oriented evaluation of five measures. In: In Workshop on WordNetand other lexical resources, second meeting of the North American chapter of theassociation for computational linguistics (2001)3. Budanitsky, A., Hirst, G.: Evaluating wordnet-based measures of lexi-cal semantic relatedness. Comput. Linguist. 32(1), 13–47 (Mar 2006),http://dx.doi.org/10.1162/coli.2006.32.1.134. Cal`ı, A., Capuzzi, S., Dimartino, M.M., Frosini, R.: Recommendation of text tags insocial applications using linked data. In: 2nd Int. Workshop on Data Managementin the Social Semantic Web (DMSSW 2013). pp. 187–191 (2013)5. Gabrilovich, E., Markovitch, S.: Computing semantic relatedness usingwikipedia-based explicit semantic analysis. In: Proceedings of the 20th in-ternational joint conference on Artifical intelligence. pp. 1606–1611. IJ-CAI’07, Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (2007),http://dl.acm.org/citation.cfm?id=1625275.16255356. Gracia, J., Mena, E.: Web-based measure of semantic relatedness. In: In Proc.of 9th International Conference on Web Information Systems Engineering (WISE2008), Auckland (New Zealand. pp. 136–150. Springer (2008)7. Miller, G.A.: Wordnet: A lexical database for english. Commun. ACM 38(11), 39–41 (Nov 1995), http://doi.acm.org/10.1145/219717.2197488. Patwardhan, S., Banerjee, S., Pedersen, T.: Using measures of semantic re-latedness for word sense disambiguation. In: Proceedings of the 4th inter-national conference on Computational linguistics and intelligent text pro-cessing. pp. 241–257. CICLing’03, Springer-Verlag, Berlin, Heidelberg (2003),http://dl.acm.org/citation.cfm?id=1791562.17915929. Pedersen, T., Banerjee, S., Patwardhan, S.: Maximizing Semantic Relat-edness to Perform Word Sense Disambiguation. Research Report UMSI2005/25, University of Minnesota Supercomputing Institute (March 2005),http://www.patwardhans.net/papers/PedersenBP05.pdf10. Rada, R., Mili, H., Bicknell, E., Blettner, M.: Development and application of ametric on semantic nets. IEEE Transactions on Systems, Man and Cybernetics19(1), 17–30 (1989)11. Strube, M., Ponzetto, S.P.: Wikirelate! computing semantic relatedness us-ing wikipedia. In: proceedings of the 21st national conference on Artifi-cial intelligence - Volume 2. pp. 1419–1424. AAAI’06, AAAI Press (2006),http://dl.acm.org/citation.cfm?id=1597348.1597414

Semantic search in RealFoodTrade

Andrea Calì

DE VIRGILIO, ROBERTO

Archivio della Ricerca - Università di Roma 3

We present RealFoodTrade (RFT), a system that allows farmers\ud
and fisher-\ud
men to sell their products directly to the end-buyer. RFT mak\ud
es use of Linked\ud
Data sets, together with a domain ontology designed by expert\ud
s, to perform\ud
semantic search over products on sale. RFT employs geo-locat\ud
ion technology\ud
on mobile devices to match demand and supply according to the l\ud
ocation.\ud
We sketch the semantic search techniques in RFT and illustrat\ud
e a prototype\ud
tailored to the fishing industry

Name not available

Semantic search in RealFoodTrade

Abstract

Similar works

Full text

Available Versions

CiteSeerX

Birkbeck Institutional Research Online

Archivio della Ricerca - Università di Roma 3

Name not available