Semantic Enrichment of Ontology Mappings: A Linguistic-based Approach Patrick Arnold, Erhard Rahm University of Leipzig, Germany 17th East-European Conference on Advances in Databases and Information Systems

2 10/14/2015Semantic Enrichment of Ontology Mappings 1. Introduction Ontology Matching: Detecting corresponding concepts between two Ontologies O and O' Most matching tools do not consider the relation type that holds between corresponding concepts

3 10/14/2015Semantic Enrichment of Ontology Mappings 1. Introduction Importance of Relation Types Ontology Merging and Ontology Evolutions  More precise results  Effectively preventing false conclusions Related fields  Text Mining, Entity Resolution, Linked Data  Key Question: Given two items, words etc.: What is the logical relation between them?

4 10/14/2015Semantic Enrichment of Ontology Mappings 1. Introduction - Example

5 10/14/2015Semantic Enrichment of Ontology Mappings 1. Introduction Some existing tools regarding relation types S-Match: Ineffective in our evaluations  Returned about 20,000 correspondences where around 400 were expected Further tools: LogMap, TaxoMap, etc.

6 10/14/2015Semantic Enrichment of Ontology Mappings Our Contributions 1.Introduction 2.Semantic Enrichment Architecture 3.Implemented Strategies 4.Evaluation 5.Outlook

7 10/14/2015Semantic Enrichment of Ontology Mappings 2. Semantic Enrichtment Architecture We provide a 2-step architecutre Step 1: Classic Ontology Matching Step 2: Enrichment

8 10/14/2015Semantic Enrichment of Ontology Mappings 2. Semantic Enrichtment Architecture Approach consists of 4 strategies Each strategy returns one of the following relation types: equal is-a / inverse is-a part-of / has-a related undecided Take the relation which was returned most In case of draw: User feedback required If all strategies return undecided: Decide on equal by default

9 10/14/2015Semantic Enrichment of Ontology Mappings 3. Implemented Strategies 3.1 Compound Strategy Compound: Two words A, B form a new word AB. Examples: high-school, blackbird, database conference A is called the modifier, B is called the head Compounds often express is-a relations (endocentric compounds) High-school is a school Blackbird is a bird...

10 10/14/2015Semantic Enrichment of Ontology Mappings 3. Implemented Strategies 3.1 Compound Strategy If there is a correspondence (AB, B) or (B, AB), we derive the is-a or inv. is-a relation Example: (main memory, memory) Problem: Exocentric compounds butterfly, redhead, computer mouse Exocentric matches extremely rare in mappings If AB is an exocentric compound, there usually is no head B in the opposite ontology Example: sawtooth – tooth

11 10/14/2015Semantic Enrichment of Ontology Mappings 3. Implemented Strategies 3.1 Compound Strategy Possibilities to reduce false conclusions Check modifier length: Must be at least 3  inroad – road Use dictionary to check the modifier  marriage – age  nausea – sea  holiday – day? No solutions for “Pseudo-Compounds“ question – ion justice – ice

12 10/14/2015Semantic Enrichment of Ontology Mappings 3. Implemented Strategies 3.2 Background Knowledge WordNet for English-language scenarioes Reliable, extensive thesaurus Excellent precision, good recall Limited in domain-specific areas Problem: Compounds Example: Vintage Car Repair Shop Very simple word, but not contained by WordNet

13 10/14/2015Semantic Enrichment of Ontology Mappings 3. Implemented Strategies 3.2 Background Knowledge Gradual Modifier Removal Remove modifiers gradually from the left After each removal: Check whether word is contained by WordNet Example: Vintage Car Repair Shop ↔ Company WordNet: Repair Shop is a Company  Vintage Car Repair Shop is a Company StepWordIn WordNet? 1Vintage Car Repair Shop  2Car Repair Shop  3Repair Shop

14 10/14/2015Semantic Enrichment of Ontology Mappings 3. Implemented Strategies 3.3 Itemization Itemization: List of items (words or phrases) Most frequently in product taxonomies Examples:  Laptops and Computers  Bikes, Scooters and Motorbikes More complex: Need special treatment Itemization Strategy: Triggers if at least one concept is an itemization Exploits previous strategies Approach: Remove items from item sets  Goal: Empty set

15 10/14/2015Semantic Enrichment of Ontology Mappings 3. Implemented Strategies 3.3 Itemization Example Correspondence: books, e-books, movies, films, cds novels and compact discs Step 1: Build item sets { books, e-books, movies, films, cds } { novels, compact discs } Step 2: Intra-Synonym Removal { books, e-books, movies, films, cds }In each item set, remove synonyms (A,B) by crossing off either A or B. { novels, compact discs } Step 2: Intra-Synonym Removal { books, e-books, movies, films, cds }In each item set, remove synonyms (A,B) by crossing off either A or B. { novels, compact discs } Step 3: Intra-Hyponym Removal { books, e-books, movies, cds }In each item set, remove existing hyponyms. { novels, compact discs } Step 3: Intra-Hyponym Removal { books, e-books, movies, cds }In each item set, remove existing hyponyms. { novels, compact discs } Step 4: Inter-Synonym Removal { books, movies, cds }Remove each synonym pair between the two item sets. { novels, compact discs } Step 4: Inter-Synonym Removal { books, movies, cds }Remove each synonym pair between the two item sets. { novels, compact discs } Step 5: Intra-Hyponym Removal { books, movies }Remove each word H to which a hypernym H’ in the opposite item set exists. { novels } Step 5: Intra-Hyponym Removal { books, movies }Remove each word H to which a hypernym H’ in the opposite item set exists. { novels } Step 6: Determine the Relation Type { books, movies }Second item set more specific than first one: Inverse is-a { }

16 10/14/2015Semantic Enrichment of Ontology Mappings 3. Implemented Strategies 3.4 Structure Strategy Focus: Structured schemas (hierarchies) Issue: A relation between two matching concepts X, Y cannot be derived Check the relation between X' and Y resp. X and Y' Prime (') denotes father element

17 10/14/2015Semantic Enrichment of Ontology Mappings 3. Implemented Strategies 3.4 Structure Strategy Example:

18 3. Implemented Strategies 3.5 Subset Verification In some cases, is-a relations only appear to be correct

19 10/14/2015Semantic Enrichment of Ontology Mappings 4. Evaluation 3 Benchmark Scenarios Input: Perfect mapping without relation types Evaluation: How many non-trivial relations were detected? (recall) How many of them were correct? (precision) ScenarioDomain / TraitsCorresp.Non-trivial corresp. 1Web DirectoriesGerman language, product catalog DiseasesHealth, medical domain Text Mining Taxon.TM Taxonomy (Everyday Language)762692

20 10/14/2015Semantic Enrichment of Ontology Mappings 4. Evaluation Evaluation (as of April 2013) Evaluation against S-Match No reasonable evaluation feasible Scenario 1: Returned only 4 correspondences, all wrong Scenario 2: Returned 19,600 correspondences  3 % recall, precision close to 0 % RecallPrecisionF-Measure Web Directories46.7 %69.0 %57.8 % Health58.5 %80.0 %69.2 % TM Taxonomies65.4 %97.7 %81.1 %

21 10/14/2015Semantic Enrichment of Ontology Mappings 4. Evaluation Evaluating the Strategies RecallPrecision Compound18.9 %82.2 % Background Knowledge19.6 %94.0 % Itemization17.1 %88.8 % Structure1.0 %50.0 %

22 10/14/2015Semantic Enrichment of Ontology Mappings 5. Outlook Relation types needed for different mapping tasks Two general approaches: Linguistic or background knowledge Linguistic Strategies  More generic and more error-prone Background Knowledge  Less generic and more precise Improvements Exploit more background knowledge  Example: Yago Taxonomy, DBPedia, UMLS  Combine it with linguistic / NLP technologies Exploit further linguistic techniques

23 Thank You