Evaluating semantic similarity using GML in Geographic Information Systems

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Evaluating semantic similarity using GML in
Geographic Information Systems

• Fernando Ferri 1, Anna Formica 2, Patrizia
  Grifoni 1, and Maurizio Rafanelli 2
• 1 IRPPS-CNR, via Nizza 128, 00198 Roma, Italy
• fernando.ferri_at_irpps.cnr.it, patrizia.grifoni_at_irpp
  s.cnr.it
• 2 IASI-CNR, viale Manzoni 30, 00185 Roma, Italy
• formica_at_iasi.cnr.it, rafanelli_at_iasi.cnr.

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Summary

• Motivation
• Related works
• Coding a Part-of Hierarchy using GML
• Similarity evaluation
• Conclusion

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Motivation (1)

• In Geographic Information Systems (GISs) semantic
  similarity plays an important role, as it
  supports the identification of objects that are
  conceptually close, but not identical.
• GML (Geography Markup Language) is emerging as
  the dominant standard for exchanging geographic
  data across the Internet.
• A semantic similarity model facilitates
  comparison of entities and allows information
  retrieval and integration to handle semantically
  similar concepts . The goal of a similarity model
  is to obtain flexible and better matches between
  user-expected and system-retrieved information.

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Motivation (2)

• Given the relevance of the Is-in relationship in
  the geographic context, we focus on GML elements
  organized according to Part-of (meronymic)
  hierarchies.
• The semantics essentially concerns parts which
  are similar to and inseparable from the whole.

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Related works (1)

• Similarity of hierarchically related concepts has
  been widely investigated in the literature
  Resnik Rodriguez, Egenhofer.
• From the various proposals, we followed the
  probabilistic approach of Lin, which is based on
  the notion of information content and overcomes
  the drawbacks of the traditional edge-counting
  approach.

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Related Works (2)

• Resnik proposes algorithms that take advantage of
  taxonomic similarity in resolving syntactic and
  semantic ambiguities.
• Lin starts from the Resnik work and addresses
  also the information content of the comparing
  concepts.

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Coding a Part-of Hierarchy with GML (1)

• The real world in the geographic domain can be
  represented as a set of features, and
  AbstractFeatureType codifies a geographic feature
  in GML.
• Its geometry type is an important property, it is
  given in the reference coordinate system and
  describes the extent, position or relative
  location of the represented concept.

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Coding a Part-of Hierarchy with GML (2)

• The geometric types defined in GML provide the
  framework for modelling all the geographical
  concepts.
• By means of this framework it is possible to
  model, for example, the concepts composing a
  communication ways network, such as roads,
  rivers, canals and other communication
  infrastructures.

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Coding a Part-of Hierarchy with GML (3)

• This figure shows an example of a type hierarchy
  that introduces concepts concerning communication
  infrastructures starting from the GML geometric
  types.

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Coding a Part-of Hierarchy with GML (4)

• As mentioned in the motivation, due to the
  relevance of the Is-in relationship in the
  geographic context, the paper focuses on GML
  elements organized according to Part-of
  (meronymic) hierarchies.
• For instance, in our example a Part-of
  relationship exists among communication ways
  (ComWay) and roads, rivers and canals.

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Coding a Part-of Hierarchy with GML (5)

• Usually, in the literature, Part-of hierarchies
  are modelled in XML using sequences of
  elements, and a similar approach could be
  followed in GML

• However, this approach does not permit to
  distinguish between elements of the Part-of
  hierarchy and other elements eventually defined
  out of the Part-of hierarchy, such as Kind and
  Country

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Coding a Part-of Hierarchy with GML (6)

• In order to put in evidence meronymic
  relationships within the GML element hierarchy, a
  Part-of hierarchy could be modelled by
  introducing some special geographic types such as
  PartOfWayType, PartOfRivType, PartOfCanType

• Each special type is introduced for modelling a
  Part-of relationship between a geographic concept
  and their component concepts

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Coding a Part-of Hierarchy with GML (7)

• ltelement name"ComWay" type"ComWayType"/gt
• ltelement name"Road" type"RoadType"/gt
• ltelement name"River" type"RiverType"/gt
• ltelement name"Canal" type"CanalType"/gt
• ltelement name"NavRiver" type"NavSegmentType"/
  gt
• ltelement name"NNavRiver type"NNavSegmentType"
  /gt
• ltelement name"NavCanal type"NavSegmentType"/
  gt
• ltelement name"NNavCanal type"NNavSegmentType"
  /gt
•  
• ltcomplexType name"ComWayType"gt
• ltsequencegt
• ltelement name "kind" type"string"/gt
• ltelement name "country" type"string"/gt
• ltelement name "PartOfWay"
  type"PartOfWayType"/gt
• lt/sequencegt
• ltattribute name"label" type"string" /gt
• ltattribute name"label" type"string" /gt
• ltattribute name"length" type"integer" /gt
• lt/complexTypegt

• This GML code shows how to put in evidence a
  meronymic relationship within the GML element
  hierarchy introducing a special geographic type
  such as PartOfWayType

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Evaluating similarity (1)

• For evaluating concept similarity this paper
  combines and revisits
• the information content approach Lin98,
• a proposal inspired by the maximum weighted
  matching problem in bipartite graphs FM02.

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Evaluating similarity (2)

• The starting assumption is that the association
  of probabilities with the Part-of taxonomy allows
  us the notion of a weighted element hierarchy to
  be introduced. In particular, in our example the
  probabilities have been estimated in line with
  WordNet 2.0.
• For instance, below the concepts Road and River
  have been defined, with the related frequencies
  (the numbers in parenthesis).
  
  (95) Road an open way (generally public)
  for travel and transportation
  
  (55) River a large natural stream of water
  (larger than a creek)

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Evaluating similarity (3)

• The probability of a concept
• The probability of a concept c is defined as
• p(c) freq(c)/N
• where freq(c) is the frequency of the concept c
  in the taxonomy, and N is the total number of
  concepts.
• In the example probabilities have been assigned
  according to WordNet.

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Evaluating similarity (4)

• Example Weighted Concept Hierarchy

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Evaluating similarity (5)

• Following the standard approach of information
  theory Ross76, the information content of a
  concept c can be quantified as
• log p(c)
• that is, as the probability increases, the
  informativeness decreases.

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Evaluating similarity (6)

• The information content similarity (ics) of
  two concepts such as River and Canal is defined
  as
• ics(River, Canal) 2 log p(ComWay)/(log
  p(River)log p(Canal)) 0,72
• where ComWay is the concept representing the
  maximum information content shared by River and
  Canal. According to the Lins approach the more
  information two concepts share, the more similar
  they are.

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Evaluating similarity (7)

• Structural similarity (asim)
• Inspired by the maximum weighted matching
  problem in bipartite graphs, we have to identify
  the
• set of pairs of typed attributes
• such that is maximal the sum of the products
  of the information content similarity of the
  attributes and the related types.

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Evaluating similarity (8)

• Example

RiverType
CanalType
labelstring lengthinteger flowinteger deepne
ssinteger
labelstring profundityinteger capacityinteger
lengthinteger
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Evaluating similarity (9)

• In the previous example the set of pairs of
  attributes that maximizes the sum of the related
  information content similarity is the following
• (label,label), (length,length),
  (flow,capacity), (deepness,profundity)

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Evaluating similarity (10)

• In fact, by assuming that deepness and
  profundity are synonyms, we have
• ics(label,label)ics(length,length)
  ics(deepness,profundity) 1
• and ics(flow,capacity) 0.  

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Evaluating similarity (11)

• The similarity of the sets of attributes of
  complexTypes (asim) is therefore defined by the
  above maximum sum divided by the greatest of the
  cardinalities of the sets of attributes of the
  types compared.
• In the case of RiverType and CanalType we
  have
• asim(RiverType,CanalType) ¾ 0.75

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Evaluating similarity (12)

• Concept Similarity (Gsim)
• The Similarity (Gsim) of the concepts River and
  Canal is defined as
• Gsim(River , Canal) (ics(River , Canal)w
  asim(River, Canal)(1-w)) Bt(RiverType,CanalTyp
  e)
• where
• ics(River , Canal) is the information content
  similarity
• asim(River , Canal) is the structural similarity
• w is a weight, s.t. 0 lt w lt 1.
• Bt is a Boolean function that, given two
  complexTypes, returns 0 if their least upper
  bound in the type hierarchy is AbstractFeatureType
  , otherwise it returns 1.

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Evaluating similarity (13)

• In particular, if we assume w0.5
• Gsim(River , Canal) (ics(River , Canal)w
  asim(River, Canal)(1-w)) Bt(RiverType,CanalTyp
  e)
• Gsim(River , Canal) 0.5 (0.720.75)1 0.74

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Conclusion

• Thank you