Revision as of 12:32, 28 November 2012

Person responsible for editing/maintaining this page

Pavlos Fafalios

Type

Libraries, Web application, deployed (and configured) applications

Description

Detailed description at https://gcube.wiki.gcube-system.org/gcube/index.php/X-Search

Related iMarine WP/Tasks

T10.4

Related iMarine Deliverables

Related Milestones

Cover page: http://bscw.research-infrastructures.eu/bscw/bscw.cgi/d247523/MS45_M6.pdf

Detailed: https://gcube.wiki.gcube-system.org/gcube/index.php/Semantic_Data_Analysis

Related Cluster

http://wiki.i-marine.eu/index.php/Semantic_cluster_achievements

Related Presentations/Tutorials

link to latest presentation ?

Current (development) status

Link to a document that describes the implemented features: http://bscw.research-infrastructures.eu/bscw/bscw.cgi/d258140/XSearch%20PrototypesYear1.docx

Current Deployments

X-Search has been designed to offer its functionality on top of other search systems. In particular (and according to the milestone) it offers:

• Clustering of the results. Clustering is performed on the textual snippets of the returned results. Clustering of the textual contents is also supported. Furthermore a ranking on the identified clusters is performed.
• Provision of extracted textual entities. Text entity mining can be performed either over the textual snippets or over the entire contents, and supports ranking of the identified entities.
• Provision of gradual faceted search. The user is able to quickly explore the results space by exploiting the identified entities that have been mined and the results of clustering.
• Ability to fetch semantic information about extracted entities. XSearch provides the necessary linkage between the mined entities and semantic information. In particular by exploiting appropriate knowledge bases (i.e. FactForge, DBPedia, FLOD, EcoScope KB, etc.) the user can retrieve more information about an entity by querying and browsing over these knowledge bases.
• Exploitation of the offered services in any web page. Text entity mining can be performed over the whole contents of a particular result (HTML and PDF web pages).

Prototypes (click to run)

• X-Search over Bing and FactForge: http://139.91.183.72/x-search/. This prototype runs on top of Bing web search engine, and analyzes the snippets of the top-K results (the default value of K is 50). In order to provide the linkage with semantic sources it uses the FactForge knowledge base (accessed through SPARQL). It also supports the analysis of more results (i.e. top 100, 200, 500), as well as the analysis over the whole content of the results (rather than just the snippets) upon user request. It is fully configurable in terms of the underlying web search engine or Knowledge Base that is used, the categories of the mined entities, etc.

• X-Search over FIGIS and FLOD: http://139.91.183.72/x-search-fao/. This prototype uses FAO FIGIS as the underlying search system, which searches for publications about fisheries and aquaculture. For supporting the entity enrichment, the FLOD dataset is queried.

• X-Search over ECOSCOPE: http://139.91.183.72/x-search-fao/ (by setting the Ecoscope configuration through http://139.91.183.72/x-search-fao/sesadmin.jsp). This prototype uses the ECOSCOPE search system and the ECOSOPE knowledge base (http://ecoscopebc.mpl.ird.fr/joseki/ecoscope).

Demo Scenarios

1) Suppose that a user is looking for publications about tuna. Specifically he wants to find experiments that were applied to several species of tuna. So, he submits the query tuna and gets a sorted list of results and various categories of entities like Regional Fisheries Body, Species, FAO Country, etc. User realizes that the category Species may contain interesting entities. He notices that there is an entity with the label yellowfin which is a species of tuna found in pelagic waters of tropical and subtropical oceans worldwide, and an entity with the label skipjack tuna which is another species in the tuna family. Both entities contain one (common) result; one related publication which is the 17th in the ranked list. So, user by performing just one click can locate that result which is very relevant to what he is looking for. Furthermore, user is able to locate fast results that are related to several FAO countries, Regional Fisheries Bodies, Persons, etc. For example, there are 4 results about tuna that are related to Madagascar.

Entity Enrichment: By clicking the small RDF icon next to the entity’s name, user can instantly (at that time) get information about that particular entity by querying the FLOD endpoint (or the TLO-SPARQL endpoint). For example, by clicking the icon next to yellowfin we could instantly get more information about yellowfin tuna and explore its characteristics (e.g. a list of is predator of, is prey of, etc.).

Related Papers

• P. Fafalios, I. Kitsos, Y. Marketakis, C. Baldassarre, M. Salampasis and Y. Tzitzikas, Web Searching with Entity Mining at Query Time, Proceedings of the 5th Information Retrieval Facility Conference, IRF 2012, Vienna, July 2012.

Paper: http://www.ics.forth.gr/~fafalios/files/pubs/fafalios_2012_irf.pdf
Presentation: http://www.ics.forth.gr/~fafalios/files/ppts/fafalios_2012_irfc_presentation.pdf
BIB entry: http://www.ics.forth.gr/~fafalios/files/bibs/fafalios2012websearching.bib

Status

.... in terms of stability/evaluation/testetc

Related tickets

numbers and links to the TRAC system

Plans of next steps and related tickets

• Hosting by gCube
• service
• ....
• Exploitation of forthcoming TLO: ...