Background Knowledge for Ontology Construction

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Background Knowledge for Ontology Construction

• Blaž Fortuna,
• Marko Grobelnik,
• Dunja Mladenic,
• Institute Jožef Stefan, Slovenia

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Bag-of-words

• Documents are encoded as vectors
• Each element of vector corresponds to frequency
  of one word
• Each word can also be weighted corresponding to
  the importance of the word

• There exist various ways of selecting word
  weights. In our paper we propose a method to
  learn them!

computer 0.9
mathematics 0.8
are 0.01
and 0.01
science 0.9

Important
Word Weigts
Noise
computer 2
mathematics 2
are 1
and 4
science 3

computer 1.8
mathematics 1.6
are 0.01
and 0.04
science 2.7

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SVM Feature selection

• Input
• Set documents
• Set of categories
• Each document is assigned a subset of categories
• Output
• Ranking of words according to importance
• Intuition
• Word is important if it discriminates documents
  according to categories.

• Basic algorithm
• Learn linear SVM classifier for each of the
  categories.
• Word is important if it is important for
  classification into any of the categories.
• Reference
• Brank J., Grobelnik M., Milic-Frayling N.
  Mladenic D. Feature selection using support
  vector machines.

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Word weight learning

• Algorithm
• Calculate linear SVM classifier for each category
• Calculate word weights for each category from SVM
  normal vectors. Weight for i-th word and j-th
  category is
• Final word weights are calculated separately for
  each document

• The word weight learning method is based on SVM
  feature selection.
• Besides ranking the words it also assigns them
  weights based on SVM classifier.
• Notation
• N number of documents
• x1, , xN documents
• C(xi) set of categories for document xi
• n number of words
• w1, , wn word weights
• nj1, , njn SVM normal vector for j-th
  category

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OntoGen system

• System for semi-automatic ontology construction
• Why semi-automatic?The system only gives
  suggestions to the user, the user always makes
  the final decision.
• The system is data-driven and can scale to large
  collections of documents.
• Current version focused on construction of Topic
  Ontologies, next version will be able to deal
  with more general ontologies.
• Can import/export RDF.

• There is a big divide between unsupervised and
  fully supervised construction tools.
• Both approaches have weak points
• it is difficult to obtain desired results using
  unsupervised methods, e.g. limited background
  knowledge
• manual tools (e.g. Protégé, OntoStudio) are time
  consuming, user needs to know the entire domain.
• We combined these two approaches in order to
  eliminate these weaknesses
• the user guides the construction process,
• the system helps the user with suggestions based
  on the document collection.

http//kt.ijs.si/blazf/examples/ontogen.html
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How does OnteGen help?

• By identifying the topics and
• relations between them
• using k-means clustering
• cluster of documents gt topic
• documents are assigned to clusters gt
  subject-of relation
• We can repeat clustering on a subset of documents
  assigned to a specific topic gt identifies
  subtopics and subtopic-of relation

• By naming the topics
• using centroid vector
• A centroid vector of a given topic is the average
  document from this topic (normalised sum of
  topics documents)
• Most descriptive keywords for a given topic are
  the words with the highest weights in the
  centroid vector.
• using linear SVM classifier
• SVM classifier is trained to seperate documents
  of the given topic from the other document in
  the context
• Words that are found most mportant for the
  classification are selected as keywords for the
  topic

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Topic ontology of Yahoo! Finances
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Background knowledge in OntoGen

• All of the methods in OntoGen are based on
  bag-of-words representation.
• By using a different word weights we can tune
  these methods according to the users needs.
• The user needs to group the documents into
  categories. This can be done efficiently using
  active learning.

http//kt.ijs.si/blazf/examples/ontogen.html
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Influence of background knowledge

• Data Reuters news articles
• Each news is assigned two different sets of tags
• Topics
• Countries
• Each set of tags offers a different view on the
  data

Topics view
Countries view
Documents
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Links

• OntoGen
• http//ontogen.ijs.si/
• Text Garden
• http//www.textmining.net/