1. The Harmony of Music and Computing Jantine Trapman Expanding a Domain- Specific Database

2. Overview Components –LT4eL –Cornetto Creation / expansion of Music Ontology –Automatic Creation –Watson –Prompt Mapping –Music Ontology –Cornetto

3. Components LT4eL Cornetto

4. Components: LT4eL Language Technology for eLearning www.lt4el.eu Development of search and management facilities in the LMS: –Keyword Extractor –Glossary Candidate Finder –Semantic Search

5. Semantic Search Based on: –(multilingual) documents (LOs) for eight languages –semantic annotation of LOs –ontology –lexicon for each language involved Corpus and ontology are restricted to Computing domain

6. Computing Ontology (1) Creation: –Manually annotated keywords in eight languages extracted from LOs –Translated into (English) concepts –Definitions collected on the WWW and added to concepts Extension with additional concepts from: –Restrictions on existing concepts –Superconcepts of existing concepts –Missing subconcepts –Annotation of LOs

7. Computing Ontology (2) Domain ontology: –Domain: Computing –Manually created –1406 concepts 50 from DOLCE 250 intermediate concepts from OntoWordNet Use: –Lexicon development for 8 languages –Semantic annotation LOs –LO indexing WordNet Computin g DOLC E GermanPolish Maltese Portugu ese Bulgari an Czech Romani an English Dutch LT4eL lexicons

8. Computing Ontology Part

9. Computing Lexicon Concepts were translated in all languages Each entry contains three types of information: –Concept (and superconcept): CDDrive (is-a Drive) –Definition: a drive that reads a compact disc and that is connected to an audio system –Set of terms in a given language: CD-speler, CD drive

10. Expansion of the LT4eL KB Future: more domains needed Task: –Expansion ontology and lexicons –Preferably semi-automatic Three options: –Top-down –Bottom-up –Both, ingredients: Cornetto, WordNet Music ontology Watson, Prompt

11. Cornetto Combinatorial and Relational Network as Toolkit for Dutch Language Technology Referentie Bestand Nederlands (RBN)  lexical units Dutch part of EuroWordNet: Dutch WordNet (DWN)  synsets SUMO/MILO plus extensions  terms and axioms Core: table of Cornetto Identifiers (CIDs) http://www.let.vu.nl/onderzoek/projectsites/cornetto/index.html SUMO/ MILO Dutch WordNet (DWN) Wordnet Cornetto Database Referentie Bestand Nederlands (RBN)

12. Example Lexical Entry Cornetto (1) [noun] zanger SenseCID Iemand die zingt c_n-42316 Vogel die zingt c_n-42317 (Poëtisch voor) dichter c_n-42318 … …

13. [noun] zanger:1 c_n-42316 Morphology: type:derivation; structure:zingen[*er]; plurforms:zangers Syntax: gender:m/f; article:de Semantics: reference:common; countability:count; type:human; subclass:beroepsnaam/beoefenaar; resume:iemand die zingt Pragmatics: domain:muz

14. Example Lexical Entry Cornetto (2) Combinatorics zanger1: –De redacteur van het woordenboek was ook een zanger –De zanger van de band SUMO: (+,, hasSkill) Synonyms: zanger, zangeres HAS_HYPERONYM musicus, musicienne, muzikant HAS_HYPONYM baszanger, sopraan, blueszanger, charmezanger,... Equivalence relations: EQ_SYNONYM singer, vocalist, vocalizer, vocaliser /ENG20- 09908715-n  link with WordNet 2.0! WordNet Domains: music

15. Goal:

16. Tasks –Extract music related terms from Cornetto –Create a domain ontology for Music –Map between terms from lexicon and concepts in ontology –Map music ontology to OntoWN and DOLCE –Adjust Cornetto data to LT4eL format

17. Questions (1) 1.How can we automatize the process of ontology building and to which extent? 2.How can we profit from existing resources from the Semantic Web to enrich ontologies? 3.To which extent do Watson and PROMPT support the reuse of existing resources?

18. Music Ontology Automatic Creation Expansion with: Watson Prompt

19. Automatic Creation (1) (Basili et al. 2007): automatic ontology extraction from open-domain corpus (BNC) Designed for three tasks: 1.lexical ambiguity resolution within a specific domain 2.restricting a set of terms to a subset relevant for an ontology to be constructed 3.expanding this new ontology with other, novel and relevant concepts, relations and instances.

20. Automatic Creation (2) Preprocessing: –Corpus split in 40 sentence text segments –PoS tagging –Filtering of noun phrases General steps: –Term extraction through Latent Semantic Analysis (Deerwester et al. 1990) –Ontology extraction from WordNet based on Conceptual Density (Agirre and Rigau 1996)

21. Music Ontology Part

22. Music Ontology (Basili et al. ‘07) 46 primitive classes Leaf concepts have a synset ID from WordNet No properties, only super-/subconcept relation So.. a rather small and shallow ontology expansion by exploiting Semantic Web techniques

24. Watson (1) http://watson.kmi.open.ac.uk/WatsonWUI/ Every URI is clickable: all resources are available Information about: –Size –Representation language –Number of classes, properties, individuals etc. –Review rating Interface for SPARQL queries Possibility of (upwards) navigation

25. Watson (2) Also available as Protégé plug-in (under development) API New concepts can be added Manually One by one Much human action required Faster than creation from scratch, but still a tedious exercise

26. Watson (3) Watson provides in –a list of URIs of available semantic databases –a list of candidate concepts What is still lacking: –a (semi-)automatic way to merge or align new concepts or ontologies to an existing one. Possible solution: Prompt

27. PROMPT (1) http://protege.stanford.edu/plugins/prompt/prompt.html Protégé plug-in Functionalities: Comparison Inclusion Merging Alignment Requirement: ontologies for merge etc. must be available offline Prompt goes beyond purely syntactic matching Evaluation shows that experts followed 90% of Prompt’s suggestions

28. Prompt (2) Saves time and effort: –linguistically similar classes are found quickly –inherited properties and subclasses can be added automatically –similar structures are automatically detected –automatic consistency check Resources must have the exact same markup language Merging: –faster but more complex –requires good insight in resources

29. Mapping Music Ontology Cornetto

30. Resources Music Ontology: –Some nodes have WordNet ID (from the automatic process –Many haven’t, especially those added with Watson Cornetto entries: –have synset ID from Dutch WN –have mapping to WordNet entry through equivalence or near-equivalence e.g.

31. Questions (2) 4.To which extent does WordNet support a mapping between: a)The Cornetto lexicon and a newly created ontology partly based on Wordnet; b)The existing ontology and lexicon from LT4eL, and Cornetto + ontology

32. Procedures A concept either has or has not a WN synset ID Mapping via WordNet synset ID: –Lookup synset ID in Cornetto –Establish related DWN synset(s) –Results: until now without problems although near- equivalence relations are expected to give mismatches Mapping without synset ID: –Syntactic matching of conceptname with terms from WordNet synsets –compare definitions and glosses

33. Examples “easy match” zanger:1 d_n-20810 (iemand die zingt)is [EQ_SYNONYM] of: singer, vocalist, vocalizer, vocaliser /ENG20-09908715-n (a person who sings ) strijkkwartet:1 d_n-14287 (ensemble van vier strijkers)and: strijkkwartet:2 d n-19905 (ensemble voor vier strijkers)are [EQ_NEAR_SYNONYM] of: soloist:1/ENG20-09931035 Note: Cornetto contains mismatch between WN and DWN

34. Matching without ID (1) For each owl:Class in Music ontology –try to match with: –target attribute in relation element of Cornetto XML structure, where –Attribute relation_name is (EQ_)NEAR_SYNONYM e.g. –Add synset ID to concept (for mapping to OntoWordNet)

35. Matching without ID (2) Compare definitions and glosses: –many ontology classes have a definition –each WN synset has a gloss –preprocess: stemming and filtering nouns –Consider percentage of nouns in concept definition that match with a certain gloss –Evaluate results Note: some definitions are equal to WN glosses

36. Current work Matching without ID on class name and definitions/glosses Manually check results for precision and recall Problem: MWEs, e.g. class Brass_Instrument: –has no precise WN counterpart, but –Brass does exist, but –it has multiple senses  how can we disambiguate? Question: ID allows easy and reliable match, but can we do the task without?

37. Remaining and Future work Attuning format lexicon to LT4eL format Mapping to OntoWordNet (semi-automatic) Mapping to DOLCE (manual task) Ontology evaluation Experiments with WordNets from different languages Involve additional lexical info to improve LT4eL search engine e.g. use morphological info about plural forms