Using Termmatching Algorithms for the Annotation of Geoservices

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Using Term-matching Algorithms for the Annotation
of Geo-services
Semantic Web Service Interoperability
forGeospatial Decision Making (FP6-026514)

• Miha Grcar1, Eva Klien2
• 1Joef Stefan Institute, Slovenia
• 2Institute for Geoinformatics, Germany

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Introduction and motivation

• Geo-data
• Provided by geo-services
• Information about geographical features such as
  rivers, lakes, roads, quarries, geological
  structure
• Geo-services
• Web-based services
• Defined by Open GIS Consortium (OGC)
• Web Feature Services (WFS)
• Spatial filtering
• Common interface (syntactically)
• HTTP/XML-based
• Semantic incompatibility (interoperability issue)
• Synonymy (e.g. Aegirite and Acmite is the
  same mineral)
• Data structured differently
• Multiliguality (e.g. river and fleuve is the
  same thing)
• European project SWING Semantic Web Service
  Interoperability for Geospatial Decision Making
• STREP in the 6th Framework Programme
• http//www.swing-project.org/

This is what weare trying to solve
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Outline of the talk

• Geo-service annotation
• Automating the annotation
• Text mining
• Web as the source of documents
• Evaluation
• Preliminary evaluation
• Larger-scale evaluation
• Conclusions and future work

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Geo-service annotation
Facilitates discovery and composition
How to establish this bridge?
Real world
entities
Spatial
information
objects
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Geo-service annotation
Domain ontology
WFS
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Automating the annotation

• Term matching is the main building block
• Using text mining techniques for term matching
• Bag-of-words representation of documents,
  document similarity
• Clustering and classification
• Visualization techniques
• Using the Web as the source of documents for text
  mining
• Search engines
• On-line encyclopedias
• Dictionaries, thesauruses

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Automating the annotation
Geo-service
Domain ontology
Schema
open-pit mine
DQuarry
DLegislation
Similarity?
Classifier
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One possible source of the documents
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Preliminary evaluation

• Dataset 150 mineral names together with their
  synonyms
• Train a classifier to distinguish between mineral
  names

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Preliminary evaluation

• Dataset 150 mineral names together with their
  synonyms
• Train a classifier to distinguish between mineral
  names

Aegirite
Alalite
Allanite
Classifier
Synonym
Diopside
Diopside


Zincblende
Zinc-spinel
Zinc vitriol
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Preliminary evaluation

• Dataset 150 mineral names together with their
  synonyms
• Train a classifier to distinguish between mineral
  names

Aegirite
Alalite
Allanite
Sort andrecommendto the user
Classifier


Zincblende
Zinc-spinel
Zinc vitriol
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Preliminary evaluation
Sort order
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Larger-scale evaluation

• Datasets
• STINET Thesaurus (STINET Scientific and
  Technical Information Network)
• 16,000 terms interlinked with broader-than,
  narrower-than, used-in-combination-for,
  used-alone-for (2 more)
• We took 1,000 term-pairs for each of the
  narrower-than and used-alone-for relations
• GEMET (General Multilingual Environmental
  Thesaurus)
• 6,000 terms interlinked with broader-than and
  related-to
• We took 1,000 term-pairs for each of the two
  relations
• Tourism ontology
• 710 concepts interlinked with is-a
• A set of instances (mostly named entities)
  belonging to the concepts
• We took 1,000 named entities and their
  corresponding concepts, and the entire structure
  defined by the is-a relation
• WordNet (lexical database for the English
  language)
• 115,000 synsets (i.e. sets of synonymous words)
  interlinked with hypernymy, meronymy, entailment,
  cause for verbs (6 more)
• We took 1,000 word-pairs for each of 9 selected
  relations
• We also considered the inverted relations for 3
  selected relations (e.g. consists-of is inverse
  of part-of)

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Larger-scale evaluation

• Examples
• GEMET
• traffic infrastructure broader-than road network
• mineral resource related-to mineral deposit
• STINET
• numerical methods and procedures used-alone-for
  gauss-seidel method
• potassium narrower-than alkali metals
• Tourism ontology
• gliding field is-a sports institution
• Warsaw instance-of city
• WordNet
• do drugs causes trip out
• snore entails sleep
• modify hypernym-of Europeanize
• Cretaceous period instance-of geological period
• shuffling meronym-of card game
• rum meronym-of rum cocktail
• housewife synonym-for homemaker

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Larger-scale evaluation

• Experimental setting
• Classification algorithm
• k-NN
• Centroid classifier
• Quotes
• Yes exact occurrence
• No co-occurrence
• We ran experiments on 18 datasets, 4 different
  settings on each dataset this means roughly 4 x
  18,000 term-pairs altogether
• We measured accuracy on top 1, 3, 5, 10, 20, 40
  recommended items

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Larger-scale evaluation
Synonymy
Meronymy
Verbs
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Larger-scale evaluation
Hyper-/Hyponymy
Class membership (instance-of)...
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Conclusions

• Terms lexical category (e.g. verb vs. noun) has
  the largest impact on the accuracy
• The dataset has much larger impact on the
  accuracy than the choice of the classifier
• General vs. specific vocabulary (works better for
  specific vocabulary or named entities)
• Semantics of the relation (works best for
  synonymy)
• The centroid classifier faster and slightly
  more accurate
• Quotes useful on datasets that contain
  technical expressions (e.g. STINET)
• Inverting the relation has no major impact on the
  results

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Future work

• Try SVM
• Cleanup the document sets
• Active learning
• Clustering, removing irrelevant clusters
• Both techniques require interaction with the user
• Visualize the term space
• Latent Semantic Analysis (LSA), Multi-Dimensional
  Scaling (MDS)
• Force-directed layout
• Use WordNet to infer relations between arbitrary
  words
• Input two words
• Process detect the corresponding synsets and
  explore inter-relations
• Output most probable relations (according to
  WordNet)
• Deal with the multilinguality issue
• Kernel Canonical Correlation Analysis (KCCA)
• Machine translation

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Thank you...

• ...for your attention
• Any questions?