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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 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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slide1

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

Presented By

Mayank Singh

overview
Overview :
  • The importance of the experiment.
  • Introduction to SPIRIT and GATE.
  • Techniques employed – Geo Parsing and Geo Coding.
  • Pros
  • Cons
  • What it leads to.
the importance of the experiment
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.
how does it work summary
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.

introduction to spirit
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
slide7
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.

annie
ANNIE
  • Tokenizer
  • Gazetter
  • Sentence splitter
  • Part-of-speech tagger
  • Named-Entity transducer
spatial markup
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)
geo parsing
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)
hurdles faced
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.
geo coding
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.
experimental setup
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
geo parsing results
Geo-parsing Results

SPIRIT + SABE + OS:

  • Correct – 1340
  • Missing – 479
  • False Hits – 596
  • Precision – 0.6966
  • Recall – 0.7820
  • F1 – 0.7184
geo coding results
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.
conclusions
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.
slide17
Pros
  • Novel system and high success rate.
  • Towards a geospatial search engine.
  • Spatial markup resources in abundance.
slide18
Cons
  • Ambiguity (geographical)
  • Matching correct geographical sense.
  • Large overhead required to build such systems.
  • Inherent NLP problems.
what it all leads to
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)
references
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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