Extracting Metadata for Spatially-Aware Information Retrieval on the Internet Clough, Paul University of Sheffield, UK - PowerPoint PPT Presentation

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Extracting Metadata for Spatially-Aware Information Retrieval on the Internet Clough, Paul University of Sheffield, UK. Presented By Mayank Singh. Overview :. The importance of the experiment. Introduction to SPIRIT and GATE. Techniques employed – Geo Parsing and Geo Coding. Pros Cons

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Extracting Metadata for Spatially-Aware Information Retrieval on the Internet Clough, Paul University of Sheffield, UK

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Extracting Metadata for Spatially-Aware Information Retrieval on the InternetClough, PaulUniversity of Sheffield, UK

Presented By

Mayank Singh


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Overview :

  • The importance of the experiment.

  • Introduction to SPIRIT and GATE.

  • Techniques employed – Geo Parsing and Geo Coding.

  • Pros

  • Cons

  • What it leads to.


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The importance of the experiment:

  • A novel system.

  • Geospatial information extraction from the Web documents.

  • Annotating the retrieved documents with the spatial data.

  • Using the annotated documents to power a working GIR system.


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How does it work (summary)

Extracting geospatial references from document involves:

  • Identifying geographic references

  • Assigning them spatial co-ordinates

  • Factors influencing the above:

    speed, reliability, flexibility and multilingualism.


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Introduction to SPIRIT

  • Spatial Information Retrieval on the Internet

  • The main aim of the project is to create tools and

    techniques to help people find information that

    relates to specified geographical locations.


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1TB crawl of about 9million web documents focused

on UK, Germany, France and Switzerland. Support

of Ontology of places.

Relevance ranking of web documents catering to

needs of:

  • Documents referring some place of interest

  • Digital geospatial resources


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GATE

It’s a java suite for tasks related to Natural Language

Processing and particularly useful and widely used in

the area of Information Extraction. ANNIE (A

nearly-new Information Extraction system) is the

highlight of this experiment which is employed by

SPIRIT.


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ANNIE

  • Tokenizer

  • Gazetter

  • Sentence splitter

  • Part-of-speech tagger

  • Named-Entity transducer


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Spatial Markup

Sources of Spatial markup:

  • OS – Ordnance Survey (UK, point)

  • TGN – Getty Thesaurus of Geographical names (Global, point)

  • SABE – Seamless administrative boundaries of Europe (Europe, polygon)


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Geo-Parsing

  • Named-Entity Recognition – lists + rules

  • List lookup inefficient

  • First gazetter lookup then use of contextual evidence to realize this.

  • JAPE (Java Patterns Annotation Engine) – rules defined w.r.t terms of entities identified within GATE.

  • Rules are language independent (using Systran system)


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Hurdles faced

  • Filtering out commonly used words – specially which are used in a non-geographical sense.

  • Using person-name list to filter out ambiguity between places and names.


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Geo-Coding

  • Gazetter lookup to assign co-ordinates

  • Removing ambiguity in place names: by feature hierarchy and feature type provided by OS.

  • Actual grounding done by SABE and OS.

  • TGN used to resolve global ambiguity.


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Experimental Setup

  • Total annotated collection of about 8.8million pages

  • 22 out of top 50 domains from Europe

  • About 1.6 million doc containing 5-10 unique footprints selected. Further 10% chosen from this and then those only from UK (130)

  • All geographic names (1864) manually identified and stored as benchmark


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Geo-parsing Results

SPIRIT + SABE + OS:

  • Correct – 1340

  • Missing – 479

  • False Hits – 596

  • Precision – 0.6966

  • Recall – 0.7820

  • F1 – 0.7184


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Geo-Coding Results

  • TGN ineffective due to global scope – 1021 found, 68% ambiguous.

  • UK SABE good – 942 found, 11% ambiguous.

  • 1137 places assigned a UID correctly. That is not only correct geo sense but resource order too.


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Conclusions

  • Promising as success rate of 89% is there.

  • Geo-parsing can be improved by enhancing gazetter matching methods and filtering of non-geographic entries

  • Geo-coding can be improved by finding better methods for combining geog. resources.


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Pros

  • Novel system and high success rate.

  • Towards a geospatial search engine.

  • Spatial markup resources in abundance.


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Cons

  • Ambiguity (geographical)

  • Matching correct geographical sense.

  • Large overhead required to build such systems.

  • Inherent NLP problems.


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What it all leads to

  • Creating geographical ontology to assist in GIR (Challenges and Resources for Evaluating Geographical IR - Bruno Martins, Mário J. Silva and Marcirio Silveira Chaves Faculdade de Ciências da Universidade de Lisboa 1749-016 Lisboa, Portugal)

  • More focused Local and topical search (Urban Web Crawling - Dirk Ahlers OFFIS Institute for Information Technology Oldenburg, Germany; Susanne Boll University of Oldenburg Germany)


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References

  • Extracting Metadata for Spatially-Aware Information Retrieval on the Internet - Clough, Paul

  • GATE - http://gate.ac.uk/overview.html

  • SPIRIT - http://www.geo-spirit.org/project_full.html

  • Challenges and Resources for Evaluating Geographical IR - Bruno Martins, Mário J. Silva and Marcirio Silveira Chaves Faculdade de Ciências da Universidade de Lisboa 1749-016 Lisboa, Portugal

  • Urban Web Crawling - Dirk Ahlers OFFIS Institute for Information Technology Oldenburg, Germany; Susanne Boll University of Oldenburg Germany


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