Discovering the semantics of user keywords
This event took place on Wednesday 05 September 2007 at 11:30
Jorge Gracia University of Zaragoza, Spain
Nowadays the Web is an information resource with an enormous potential. However this potential is not fully exploited by traditional search methods which not consider explicit semantics. In this talk, a system that discovers the intended meaning of a set of user keywords will be described. Firstly, the system discovers the semantics of the user keywords at run-time by harvesting the Semantic Web, obtaining a list of possible senses for each keyword. Secondly, it removes possible redundancies by using a synonymy probability measure. Finally, a disambiguation method is applied to select the most probable intended sense of each keyword according to the context. For example, it is expected that, for the keyword set "life of film stars", the meaning of "star" as "a famous actor" arises instead of its astronomical meaning. The output of this step can be used to build formal queries which represent the initial user query in a knowledge representation language.
In this new paradigm of applications that exploit the huge amount of formally specified information available on the Web, we can find another important example in the Ontology Matching field. A new method has been proposed to derive mappings from an exploration of multiple and heterogeneous online ontologies. In this talk I will describe how this method can be improved by using some techniques from the above mentioned system, in combination with the PowerMap system.
This event took place on Wednesday 05 September 2007 at 11:30
Nowadays the Web is an information resource with an enormous potential. However this potential is not fully exploited by traditional search methods which not consider explicit semantics. In this talk, a system that discovers the intended meaning of a set of user keywords will be described. Firstly, the system discovers the semantics of the user keywords at run-time by harvesting the Semantic Web, obtaining a list of possible senses for each keyword. Secondly, it removes possible redundancies by using a synonymy probability measure. Finally, a disambiguation method is applied to select the most probable intended sense of each keyword according to the context. For example, it is expected that, for the keyword set "life of film stars", the meaning of "star" as "a famous actor" arises instead of its astronomical meaning. The output of this step can be used to build formal queries which represent the initial user query in a knowledge representation language.
In this new paradigm of applications that exploit the huge amount of formally specified information available on the Web, we can find another important example in the Ontology Matching field. A new method has been proposed to derive mappings from an exploration of multiple and heterogeneous online ontologies. In this talk I will describe how this method can be improved by using some techniques from the above mentioned system, in combination with the PowerMap system.
Future Internet
KnowledgeManagementMultimedia &
Information SystemsNarrative
HypermediaNew Media SystemsSemantic Web &
Knowledge ServicesSocial Software
Multimedia and Information Systems is...
We focus on content-based information retrieval over a wide range of data spanning form unstructured text and unlabelled images over spoken documents and music to videos. This encompasses the modelling of human perception of relevance and similarity, the learning from user actions and the up-to-date presentation of information. Currently we are building a research version of an integrated multimedia information retrieval system MIR to be used as a research prototype. We aim for a system that understands the user's information need and successfully links it to the appropriate information sources, be it a report or a TV news clip. This work is guided by the vision that an automated knowledge extraction system ultimately empowers people making efficient use of information sources without the burden of filing data into specialised databases.
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