Download - Enterprise Multimedia Integration and Search

Future Enterprise Systems September 20th, 2010

Ozelin López, Katharina Siorpaes www.playence.com

Enterprise Mul-media Integra-on and Search

MoAvaAon •  With increasing bandwidth, cheaper storage of data, and improved hardware, mulAmedia content is gaining importance.

•  40% of worldwide traffic in Internet in 2010 will be consumed by viewing and/or downloading videos

•  In 2014, the sum of all video forms will consume 90% of traffic

20 sepAembre 2010 www.playence.com 2

Source: Cisco

MoAvaAon

•  As rich medium, video can transport and conserve more informaAon than text ever could.

•  This type of content also creates new issues with respect to search, integraAon, management and preservaAon

•  A new challenge arises, when trying to integrate text with mulAmedia


MoAvaAon

•  So far, only small parts of data comes out of automaAc analysis on mulAmedia assets


•  The work is finally done, mostly, by humans.

•  MulAmedia informaAon is isolated

MulAmedia HolisAc View in the Enterprise

•  Knowledge AcquisiAon process is criAcal to any semanAcally enhanced system

•  Current informaAon systems can only rely on weak annotaAon processes for mulAmedia assets

•  When needed, manual annotaAon tagging is performed with the help of shared vocabularies and thesauri.

•  The level of integraAon with exisAng InformaAon Systems is limited


MulAmedia HolisAc View in the Enterprise


•  Interlinking

•  AnnotaAon

•  IntegraAon

•  Search

•  Management

AnnotaAon Process

•  The annotaAon process can be done automaAcally by the system or manually. The accuracy and precision of this process depends on the type of content.

•  Textual annotaAon is done automaAcally, using Ontologies and NLP techniques to pinpoint textual references to concepts and instances in the source.

•  Several domain ontologies can be used to this purpose, providing a mulA-‐view perspecAve on the same resource.


AnnotaAon Process

•  In the case of video or audio, automaAc analysis has less accuracy.

•  ASR can done the work up to some extent (60-‐80%)

•  For video or image analysis, high-‐level features can be obtained, like face detecAon, detecAon of objects, daylight classificaAon and other basic features.

•  In these cases, collaboraAon human annotaAon must be supported.


AnnotaAon Process

•  In the case of video or audio, automaAc analysis has less accuracy.

•  ASR can done the work up to some extent (60-‐80%)

•  For video or image analysis, high-‐level features can be obtained, like face detecAon, detecAon of objects, daylight classificaAon and other basic features.

•  In these cases, collaboraAon human annotaAon must be supported.


IntegraAon

•  AnnotaAon process will leave a set of resources linked to the same semanAc content

•  Data can easily be located, mashed-‐up and displayed regardless its original source.

•  IntegraAon in playence Media empowers the user to locate a meeAng recording and being able to find related videos, audios, pictures and documents.

•  Once annotated, everything can be queried using the same model.


IntegraAon


Search

•  playence Media performs semanAc search, using annotaAons and applying faceted search and semanAc navigaAon to narrow the set of results

•  When searching, playence Media makes use of Natural Language Processing techniques like lemmaAzaAon or spell check.

•  SemanAc features are used in query expansion, like synonym expansion through SKOS, or generalizaAon-‐specializaAon expansion, using the “is-‐a” relaAonship and instances from concepts involved, or using more complex relaAons in query expansion.


Search


Intelligent mulAmedia management including: automaAc semanAc annotaAon, interlinking, in-‐video search and browsing.

•  Automa-c mul--‐language knowledge extrac-on –  LocaAon of relevant key-‐words and domain

knowledge through batch ASR. –  Efficient video annotaAon for later use.

•  In-‐video Search –  Land a search at the exact second where the

relevant content is being played. –  Land a search at the exact second where the

relevant actor is talking. •  Mul-linguality

–  Support for a wide variety of languages. –  Complex cross-‐language query and mulAmedia

asset retrieval. •  Mul-format support

–  AVI, MPEG, FLV, etc.


playence Media

•  Dynamic ra-ng –  On-‐the-‐go signalling on the most interesAng

porAons of a rich media asset. –  Beher locaAon of releant content and improve

search results. •  Manual annota-on refinement

–  Refinement of automaAc annotaAons. –  AddiAon of annotaAons.

•  Browsing and asset naviga-on –  Powerful browsing engine to keep sight of

huge amounts of media content. –  Eases finding, discovering and accessing the

required media resources. •  Rela-onship viewer

–  Snapshot view of a porAon of the subjacent data model.

–  Document set filtering by way of query expansion and reducAon.


playence Media

Further Steps

•  The challenges associated with mulAmedia integraAon in the enterprise are manifold

•  Relevance and in-‐video search •  Ontology evoluAon from customer perspecAve •  Workflow and collaboraAve processes are needed.

•  Interlinking – Linked Open Data – Linked Closed Data


Conclusion

•  Media is going to be the next enterprise communicaAon mechanism

•  Media is a bitch! – We need completely new ways of manage it –  Business is beyond text, but technology is not

•  Providing a holisAc view empowers enterprises to manage mulAmedia assets

•  This holisAc view comprises heavily informaAon related processes: annotaAon, integraAon, search, interlinking

•  playence Media comes to the playground to help companies dealing with these challenges.


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