logic based semantically enriched integration of multi feature mir
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Logic-based Semantically Enriched Integration of Multi-Feature MIR. Dominik Lübbers Computer Science Department V (Information Systems) Prof. Matthias Jarke RWTH Aachen, Germany. Some Personal Information. Name: Dominik Lübbers

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logic based semantically enriched integration of multi feature mir

Logic-based Semantically Enriched Integration of Multi-Feature MIR

Dominik Lübbers

Computer Science Department V (Information Systems) Prof. Matthias Jarke

RWTH Aachen, Germany

some personal information
Some Personal Information
  • Name: Dominik Lübbers
  • Ph.D. student at CS Department V(Information Systems, Prof. Jarke), RWTH Aachen, Germany
  • main CS research interests:
    • Multimedia Databases
    • Information Retrieval
    • Formal Logics, esp. Description Logics and its applications
    • Data Mining
    • Data Quality
  • Musical interests
    • Playing piano & organ
    • part-time church musician
    • Singing in (mainly chamber) choirs
  • One reason for difficulty of MIR:
    • User cannot express his information demand easily
    • Multiple features as „hints“
      • Standard metadata (author, title, …)
      • Melody Similarity (e.g. Query by Humming)
      • Sound Similarity (e.g. Query By Example)
      • Lyric fragments („classical“ information retrieval)
    • Queries cannot be understood without respecting (user‘s) background knowledge
  • Central goal: Formalism and Retrieval Mechanisms to
    • Integrate similarity-based queries with
    • Ontology-based Information Retrieval

=> Intelligent Music Information Retrieval Systems

overall approach
Overall Approach
  • Based on previous work done by Umberto Straccia
    • Relevance description logics
    • Fuzzy description logics
    • Ph.D thesis „Foundations of a logic based approach to Multimedia Document Retrieval“ (1999)
  • Information retrieval as logical inference (van Rijsbergen):
    • Relevance of for
overall approach1
Overall Approach







class hierarchy






Track 1



Track 2



MIDI file



Track 10

Measure 10-15




media dependant information

OO model

media independant information

Non-standard DL model

  • Object oriented modelling of media dependant information (form part)
  • Stepwise development of suitable logic
    • Standard Description Logics
    • Inconsistencies and Relevance Logics
    • Imprecision and Fuzzy Logics
  • Reasoning about form in
  • Reasoning about form and semantics
  • (Some of the many) open questions
classic description logics i
Classic Description Logics I
  • Main purpose: Ontology formalization
    • Represent relationships between terms in a domain of interest
  • Many application areas:
    • Conceptual Data Modelling
    • Semantic Query Optimization
    • Software Engineering
    • Configuration Management
    • Representation of the meaning of Web resources: Semantic Web (OWL-DL is a Description Logic), …
  • Good compromise between expressivity and computational complexity
  • Thoroughly investigated family of logics with many variantsWe concentrate on basic DL
classic description logics ii
Classic Description Logics II

: TBox (terminological knowledge)

s : ABox (Assertions, statements about objects in the KB)

relevance and inconsistency
Relevance and Inconsistency
  • Material implication allows for valid sentences, although is not related to
    • (Inconsistent knowledge base)
      • Since this piece is by Hensel and by Mendelssohn, Beethoven wrote 11 symphonies
    • (tautologies independent of premise)
      • Since Beethoven wrote 11 symphonies,this piece is by Mendelssohn or not.
  • Relevance Logic: Reject logical inferences where the premise is not relevant to the conclusion
relevance and inconsistency ii
Relevance and Inconsistency II
  • Avoid „fallacies of relevance“ by four-valued logic
  • Denotational semantics:
    • Explicitly and independently interpret falsehood

Be aware:

imprecision and fuzzy logic
Imprecision and Fuzzy Logic
  • Semantic model of application domain is imperfect and can contain vague concepts (think of genre…)
  • Approach:
    • Replace crisp interpretation of conceptsby fuzzy interpretations:
    • Fuzzy assertions:
    • Terminological constraints:
  • Allows for integration of form-based similarity measures as fuzzy predicates
Horn rules allow basic reasoning with fuzzy n-ary predicates
  • Specify combination of evidence by nondecreasing function of membership degrees
  • So far: no recursive rules possible
  • Queries:
reasoning about form
Reasoning about form
  • OO model of media-dependant data as concrete domain in with fixed interpretation
  • Represent similarity as fuzzy binary predicate, e.g.
  • Combine similarity queries, e.g.
reasoning about form semantics
Reasoning about form + semantics
  • Link form and semantics by (fuzzy) predicate Int_As
some example queries
Some example queries

Find all MIDI files that have melody A, contain lyrics B and are transcribed organ pieces

Find all MIDI files that additionally sound similar to C (somewhere)

some open questions
(Some) Open Questions
  • What parts of the model language do we need for music information? What must be extended?
  • What are suitable models for the form perspective of music data? What are meaningful ontologies?
  • How to integrate similarity measures? What interfaces are meaningful?
  • How to combine membership degrees?
  • Complexity issues, „query plan evaluation“
  • User interfaces for query formulation, …
modelling the form dimension
Modelling the form dimension
  • Modelling of media-dependent information according to OO-principles
  • Classify documents by assigning a class with defined attributes (+ types)
  • Basic class: MDO (media data object) represents linear stream of bytes
  • Organize classes in a specialization hierarchy
  • Describe parts of MDOs as CSMO (complex single media objects) by Region-functions
  • Aggregate CSMO to more complex CSMO
  • Model similarity measurement as methods in classes
modelling the form dimension example
Modelling the form dimension - Example





MidiTrack provides method

classic description logics
Classic Description Logics
  • Syntax
    • Primitive concepts: (~ unary predicates)
    • Roles: (~ binary predicates)
    • Concept term operators
  • Semantics
    • Interpretation
projects and competences at i5
Projects and Competences at i5

ConceptBase 


ProLearn 

CRC 427: Media&Cultural Communication 

SFB 476 Improve 




  • Deductive Databases
  • Conceptual Modelling
  • Formal Logics, esp. Description Logics
  • Multimedia Data Management, esp. MPEG-7
  • Service-oriented Information Systems
  • Ontology Engineering
  • Semantic Web
  • Data Quality
  • Data Mining
  • E-Commerce, esp. Electronic Marketplaces