Learning Web Service Ontologies: Two Extraction Methods and their Evaluation
This event took place on Monday 11 October 2004 at 12:30
Dr. Marta Sabou KMi, The Open University
The promise of Semantic Web Services, that of automatic discovery and configuration of semantically described web services, depends on the existence of high quality ontologies that describe the domains of web services as well as their main functionalities. While only few ontologies designed for web service description exist, building such ontologies is time consuming and difficult.
To address this problem, we have built two semi-automatic methods for extracting web service ontologies from textual sources attached to web services (or their underlying implementations). The first method uses extraction patterns applied on the output of a Part Of Speech Tagger, also called surface patterns. The second method relies on deeper linguistic analysis, by employing a dependency checker. This allows writing more complex extraction patterns (called syntactic patterns) and therefore identifying more ontological elements (subsumption hierarchy, meronymy) than with the first method. The talk will describe these extraction methods and their evaluation in two real-life case studies.
Download PowerPoint Presentation (512Kb ZIP file)
This event took place on Monday 11 October 2004 at 12:30
The promise of Semantic Web Services, that of automatic discovery and configuration of semantically described web services, depends on the existence of high quality ontologies that describe the domains of web services as well as their main functionalities. While only few ontologies designed for web service description exist, building such ontologies is time consuming and difficult.
To address this problem, we have built two semi-automatic methods for extracting web service ontologies from textual sources attached to web services (or their underlying implementations). The first method uses extraction patterns applied on the output of a Part Of Speech Tagger, also called surface patterns. The second method relies on deeper linguistic analysis, by employing a dependency checker. This allows writing more complex extraction patterns (called syntactic patterns) and therefore identifying more ontological elements (subsumption hierarchy, meronymy) than with the first method. The talk will describe these extraction methods and their evaluation in two real-life case studies.
Download PowerPoint Presentation (512Kb ZIP file)
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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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