KMi Seminars
Interpreting Linked Data as ontologies: doctrines and creeping issues
This event took place on Wednesday 15 May 2013 at 11:30

Dr. Alessandro Adamou The Open University


Years of advancements in the Semantic Web are determining a technological gap between the Linked Data levels of the traditional Semantic Web vision, and its higher layers. While the core knowledge representation and interlinking mechanisms have consolidated rather rapidly, standardisation efforts for reasoning, unifying logics, proofing and interaction are striving to reach maturity. This has given rise to alternative schools of thought concerning the nature of the Semantic Web infrastructure, some of which are even putting the very need for ontology languages in question. Part of this phenomenon is due to unexpected results in interpreting combined Linked Data along with their schemas, alignments and other ontologies, with subsequent declining trust in high-level semantics from application developers. This talk will explore some possible research directions that can help keep ontology management on track with the evolution of Linked Data. One such effort will be described in greater detail, which proposes virtualisation as a technique for dynamically assembling multiple semantic data sources into makeshift ontology networks. Experiments on the interpretation of virtual ontology networks have shown promising results in several recurring distribution scenarios of Linked Data statements, with the highest possible axiom expressivity being reached with a reduced assembly effort.



 
KMi Seminars Event | SSSW 2013, The 10th Summer School on Ontology Engineering and the Semantic Web Journal | 25 years of knowledge acquisition
 

Multimedia and Information Systems is...


Multimedia and Information Systems
Our research is centred around the theme of Multimedia Information Retrieval, ie, Video Search Engines, Image Databases, Spoken Document Retrieval, Music Retrieval, Query Languages and Query Mediation.

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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