Conceptual indexing

Technical Seminar Presentation - 2004

Presented by :-G.Susmita(EI 200157181)

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“CONCEPTUAL INDEXING”

Presented by

G.Susmita Roll# EI200157181

AtNIST,Berhampur

Under the guidance of

Mr. P.K.Jena



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Conceptual Indexing is a methodology that allows indexing systems to take advantage of the conceptual structure of phrases in the indexed material. It does this by automatically parsing each phrase into one or more conceptual structures that represent how the elements of the phrase are assembled to construct its meaning.

A key attribute of this technology is the ability to determine where a new description would belong, when the description itself is not already in the taxonomy. This is done with the help of the MSS algorithm,which can efficiently find the most specific subsumers of a new description. This allows information seekers to find concepts related to their requests, even when their request is not explicitly in the taxonomy.

CONCEPTUAL INDEXING----INTRODUCTION



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ogy CONCEPTUAL INDEXING----EXAMPLE

FRAGMENT OF A CONCEPTUAL TAXONOMY

Cleaning Automobile cleaning ! Automobile steam cleaning (1) Automobile upholstery cleaning (1) Automobile washing (1) Car washing (1) Industrial cleaning Industrial steam cleaning (1) Steam cleaning ! Automobile steam cleaning (2) Industrial steam cleaning (2) Upholstery cleaning Automobile upholstery cleaning (2) Washing Automobile washing (2) Car washing (2)



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ogy BUILDING BLOCKS

• Document Handling• Lexical analysis• Phrase Analysis• Taxonomic Classification• Browsing and retrieval



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ogy CONCEPTUAL INDEXING----ELEMENTS

Concept Extraction

1. A concept extractor

Identifies words and phrases to be indexed and records where in the material they occurred.

2. A concept assimilator

Analyzes the structure and meaning of a concept phrase to determine where in the conceptual taxonomy it belongs and what other concepts it should be related to, which may themselves need to be added to the taxonomy.



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ogy 3. A conceptual retrieval system

Uses the conceptual taxonomy to make connections between requests and indexed items of information.

4.A conceptual navigator Allows a user to browse the conceptual taxonomy and to move back and forth freely between concepts in the taxonomy and occurrences of those concepts in the indexed material



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ogy CONCEPTUAL INDEXING---FOR STOCK VIDEO CLIPS

Given the task of retrieving stock video clips for use in larger video productions, there are six types of indexes that should be assigned to individual clips

1. The Content Of The Scene

2. The Points Illustrated By The Clip

3. The Composition And Camerawork Of The Clip

4. The Likely functions of the clip in a larger narrative

5. Information About The Source Of The Clip

6. The Relationships To Other Clips In The Library



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1. The Content Of The Scene

scene content indexes include information about where the clip is located, the activities that are occurring in the clip, the types of people, and the salient visible objects.

2. The Points Illustrated By The Clip

video, like all media needs to communicate abstract ideas and relationships.In many cases it may be appropriate to index clips by the points that they make or support



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ogy 3.The Composition And Camerawork Of The Clip

The composition and camera work may also serve as a valuable index for clip retrieval, especially when functioning as a filter to pick out some small number of usable clips from some larger set with potentially relevant content or point

4.The Likely functions of the clip in a larger narrative.

One way to index a clip is in regard to the roles it might play in some larger context, most typically in some narrative sequence in a larger production. Narrative function indexes may include specific types of transition, interludes, prologues, background or hooks among others.



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5. Information About The Source Of The Clip

The details surrounding the creation of a video clip are likely to be relevant to individuals looking for stock footage.these details may include the date and time that the footage was shot, the

history of how it found its way into the stock video database.

6. The Relationships To Other Clips In The Library

In larger collection of stock video clips, there will be many sets of clip that are related in important ways.



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ogy Retrieval system for stock video

• In stock video retrieval system only scene content indexes are supported. Scene content indexes can be effectively organized by using using a hierarchy of places as the backbone of a larger conceptual network that includes indexes for activities, types of people and objects. This organization is used directly to support both the zoom and browse mechanisms in the system.

• During the zooming, the user identifies a particular place index which is related to the sort of clips they are looking for.



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ogy During the browsing process users are presented with the

conceptual neighborhood around the place index and permitted to incrementally select indexes that correspond to clips in the case library.



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ogy CONCLUSION

The need for intelligent information retrieval system presents a new opportunity to apply research in artificial intelligence to large-scale real world problems. By viewing the multimedia information as cases in a case library, we can attempt to apply the indexing and retrieval solutions developed in existing case based reasoning systems to the problems facing users of multimedia databases

Rich multimedia databases are useless without the ability to access the right piece of information at the right time.

Technology

Conceptual indexing