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qcl 's research -- Detection of Entity Properties in Content Stream

Preprocess for KBA - filter target attributes/properties rapidly.

Motivation

Linguistic knowledge --> world knowledge

  • World knowledge varies with time
  • How to acquire knowledge from heterogeneous resources to reflect the changes of real wrold is very important (KBA)

Problem

Given a target entity to be tracked, find its (new) related information from heterogeneous resources effectively because large volume of data are created.

  • Target entity
    • Different patterns related to the entity type
    • e.g. 歐巴馬 - person type
    • extract all patterns related to person
    • e.g. MS - org type
    • extract pattern related to org.
  • Patterns 分類
    • Entites (entities types)
    • Dynamic v.s Static
    • Related information
      • related to some pattern
    • Exact
      • disambiguation
  • Evaluation matrics
    • Speed
      • wiki
      • dbpedia
    • acc, prec, recall
    • TREC KBA -> to read KBA for more information.
    • Testing Dataset

Issues

Target

Target entities have different types, different types have different patterns, different patterns related to differnet types.

Note that

  • Type
  • Pattern
  • Feature
  • Information
  1. Information related to human
    • How many features are related to human ?
    • What kinds of features are related to human ?
    • What kinds of patterns can be used to find the features ?
  2. Tell out Dynamic and Static information
    • What kinds of features are dynamic, and what kinds of features are static?
    • In other words, what knowledge is unchanged?
  3. Tell out the position of the information
  4. Tell out if the information is related to the interesting targets

Efficiency

  • Data sturcture and algorithms

  • Filtering: (1) and (2)

  • Extract: (3) and (4)

  • Efficient Filtering, e.g.

    • birth (n patterns related to birth)
    • occupation change (m patterns related to this move)

  • If topic model is suitable:

    • birth is a topic, occupation change is a topic,...

  • 23.21 mins 24 core 33w

  • Spend 1393.446886 seconds

  • 1 core spend 24 mins to deal 13750 docs

  • 1 core 592docs/mins

  • 16 core

  • 1432.828743 seconds

  • about 3 mins

  • 177.767400 s 8 core 6.6w

  • 1 core spend 3 min to deal 8250 docs

  • 1 core 2750/mins

Effectiveness

  • Patterns
    1. entity types
    2. dynamic (new) v.s. static
    3. related information, related to some patterns
    4. exact information, target entites, mention disambiguation

Others (暫時沒想到分法)

  • Pattern coverage
  • Pattern use

Statistics

  • Statistics of samples

  • Distribution of features

  • Number of features/wiki

  • How to sample data from wiki for testing?

  • Testing effectiveness and efficient

    • Efficiency: enough documents, number of documents created by human per second, ...

References

Tools

  • MongoDB
  • Stanford parser
  • Python
    • RDFLib
    • TextBlob
      • Aptagger
    • NLTK
    • SimpleJson

Dataset Used

  • DBpedia
    • Download
      • DBpedia Ontology
      • Raw Infobox Property
      • Raw Infobox Property Definitions
      • Persondata
  • Wikipedia dump
    • 20130304
      • enwiki-20130304-pages-articles
  • Wiki API,1961414
    • 01-05,58115
    • 06-10,126706
    • 11-15,195114
    • 15-18,262784
    • 19-20,192237
    • 21-22,206117
    • 23,129647
    • 24,142679
    • 25,132130
    • 26,125104
    • 27,390781
  • PATTY
  • YAGO
    • YAGO Facts