The Harmony of Music and Computing Expanding a

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

Overview • Components – LT 4 e. L – Cornetto • Creation / expansion of Music Ontology – Automatic Creation – Watson – Prompt • Mapping – Music Ontology – Cornetto

Components • LT 4 e. L • Cornetto

Components: LT 4 e. L Language Technology for e. Learning www. lt 4 el. eu • Development of search and management facilities in the LMS: – Keyword Extractor – Glossary Candidate Finder – Semantic Search

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

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

Computing Ontology (2) DOLC E • Domain ontology: – Domain: Computing – Manually created – 1406 concepts Word. Net • 50 from DOLCE • 250 intermediate concepts from Onto. Word. Net Computin g • Use: – Lexicon development for 8 languages – Semantic annotation LOs – LO indexing LT 4 e. L lexicons German Polish Romani an Maltese Portugu ese English Bulgari an Czech Dutch

Computing Ontology Part

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

Expansion of the LT 4 e. L KB • Future: more domains needed • Task: – Expansion ontology and lexicons – Preferably semi-automatic • Three options: – Top-down – Bottom-up – Both, ingredients: • Cornetto, Word. Net • Music ontology • Watson, Prompt

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

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

[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

Example Lexical Entry Cornetto (2) • Combinatorics zanger 1: – De redacteur van het woordenboek was ook een zanger – De zanger van de band • SUMO: (+, , has. Skill) • Synonyms: zanger, zangeres HAS_HYPERONYM musicus, musicienne, muzikant HAS_HYPONYM baszanger, sopraan, blueszanger, charmezanger, . . . • Equivalence relations: EQ_SYNONYM singer, vocalist, vocalizer, vocaliser /ENG 2009908715 -n link with Word. Net 2. 0! • Word. Net Domains: music

Goal:

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 Onto. WN and DOLCE – Adjust Cornetto data to LT 4 e. L format

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?

Music Ontology • Automatic Creation • Expansion with: • Watson • Prompt

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.

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

Music Ontology Part

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

Watson (1) http: //watson. kmi. open. ac. uk/Watson. WUI/ • 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

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

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

PROMPT (1) http: //protege. stanford. edu/plugins/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

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

Mapping • Music Ontology • Cornetto

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

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

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

Examples “easy match” • zanger: 1 d_n-20810 (iemand die zingt) is [EQ_SYNONYM] of: singer, vocalist, vocalizer, vocaliser /ENG 20 -09908715 -n (a person who sings ) • strijkkwartet: 1 d_n-14287 (ensemble van vier strijkers) strijkkwartet: 2 d n-19905 (ensemble voor vier strijkers) [EQ_NEAR_SYNONYM] of: soloist: 1/ENG 20 -09931035 and: are • Note: Cornetto contains mismatch between WN and DWN

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 Onto. Word. Net) <owl: Class rdf: about=“http: ///my. Ontos/music. owl#orchestra"/> <relation_name="EQ_NEAR_SYNONYM" target 20 previewtext="symphony orchestra: 1, symphony: 2" version="pwn_1_6" target 20="ENG 20 -07750308 -n" target="ENG 16 -06123240 -n">

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

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?

Remaining and Future work • • • Attuning format lexicon to LT 4 e. L format Mapping to Onto. Word. Net (semi-automatic) Mapping to DOLCE (manual task) Ontology evaluation Experiments with Word. Nets from different languages • Involve additional lexical info to improve LT 4 e. L search engine e. g. use morphological info about plural forms