1. ONTOMERGE Ontology translations by merging ontologies Paper: Ontology Translation on the Semantic Web
by Dejing Dou, Drew McDermott and Peishen Qi
2003
presented by Laurentiu Vasiliu

2. Introduction Problem: ontology translation is required when:
- translating datasets
- generating ontology extensions
- querying through different ontologies
Approach: ontology translation by ontology merging and automated reasoning.
OntoMerge: online system that implements the previous approach

3. Introduction
OntoMerge: implement ontology translation with inputs and outputs in DAML+OIL or other web languages.
Approach focus: formal inference from facts expressed in one ontology to facts expressed in another ontology.
The merge of 2 ontologies: taking the union of the terms and the axioms that define them.
XML namespaces are used to avoid name clashes.
Bridging axioms are added, to relate the concepts between the 2 ontologies.
Bridging axioms = description of mappings between ontologies.

4. Introduction
Devising and maintaining a merged ontology must involve the contribution of human experts.
Inference mechanism: a theorem prover optimised for ontology translation task.
Inference used for:
- dataset translation
- ontology extension generation
- query handling through ontologies

5. Ontology translation problems
Ontologies differences: syntactic & semantic
Syntactic & semantic translation needed.
Problem 1:agents: web agents try to exchange datasets but they use different ontologies to describe them.
Ontology translation for datasets: translation of a dataset from one ontology to another.
Dataset: set of facts expressed in a particular ontology.

6. Ontology translation problems
Problem 2:ontologies generation: translation required when generating ontologies extensions (sub-ontologies)
Generating ontologies: given O1 and O2 – two related ontologies and an extension O1s of O1construct the corresponding extension of O2s.
Ontology experts are developing sub-ontologies manually
Tedious work: tools needed

7. Ontology translation problems
Problem 3:Query: knowledge to be used to answer query may be in multiple knowledge bases
These knowledge bases may use different ontologies than the querying agent.
Without ontology translation, querying is very difficult

8. Ontology mapping vs. translation ontology
They are different.
Ontology mapping: find correspondence (mapping) between concepts of 2 ontologies.
Mapping are expressed by mapping rules – how the concepts correspond.
Ontology translation: needs to know the mapping first; then can use the mapping rules.
Mappings can be generated by experts or generated automated.
In this paper’ implementation the mapping are performed manual, by experts.
Automation is an active area of research.

9. Previous work on ontology translations for datasets
Two strategies used:
1: Translate a dataset in any source ontology in one big centralised ontology; from it can be translated into a dataset in any target ontology – [Ontolingua]
It can’t really work unless a global ontology can cover all the existing ontologies + agreement by all ontology experts to write translators between their own ontology and the global ontology
Daily maintenance of all ontologies consistent with the One Truth Theory is very difficult

10. Previous work on ontology translations for datasets
2: perform ontology translation directly from a dataset in a source ontology to a dataset in another target ontology. [Ontomorph]
For practical purposes is very useful
It relies on special properties of the dataset to be translated.
Does not address the question of producing a general-purpose translator.
Previous work on ontology translation for query handling is closely related to database mediators.

11. New approach: separate syntactic and semantic translation
Break ontology translation into 3 parts:
- syntactic translation from the web language to internal representation
- semantic translation by inference using the internal notation
- syntactic translation from the internal representation to the target web language

12. New approach: separate syntactic and semantic translation
Syntactic issues are managed in the 1st and 3 with a (extendable) translator: PDDAML for translating between the internal representation and DAML+OIL
Internal representation language: Web-PDDL
Web-PDDL – first order language with LISP like syntax
It extends Planning Domain Definition Language (PDDL) with XML namespaces and more flexible notations for axioms.

13. New approach: Ontology merging and automated reasoning
Translating datasets: given a set of facts in one vocabulary (source), infer the largest possible set of consequences in another (the target); this can be broke in 2 phases:
Step 1: Inference: working in a merged ontology that combines all the symbols and axioms from both the source and target, draw inferences from source facts.
Merged ontologies: contain symbols, facts + bridging axioms

14. New approach: Ontology merging and automated reasoning
Step2: Projection: retain conclusions that are expressed only in the target vocabulary.
For the near future the merged ontologies has to be constructed by human experts.
For very large ontologies automated tools may give human suggestions to human experts
The merged ontology is an ontology itself that can be further merged with other ontologies.
Skolem terms are used: when translation requires talking about an object that cannot be identified with an existing object.

15. New approach: Ontology merging and automated reasoning
Term generation functions are introduced
Allow finer control over term generation than skolemisation
For translation is used theorem proving
Concern: in general theorem provers can run for a long time and conclude nothing usefull
However, the inference needed to be made are focused on:

16. New approach: Ontology merging and automated reasoning
- Forward chaining from source to target
- Backward chaining from queries in one ontology to datasets in another
- Introduction of skolem terms and term-generating functions.
- Use of equalities to substitute existing constant terms for skolem terms
Here, the theorem prover called OntoEngine is specialised on these sorts of inference

17. New approach: Ontology merging and automated reasoning
OntoEngine uses chaining through implications with specified directions instead of full fledged resolutions
It is not complete in the logical sense
Trade: completeness vs. efficiency
Even a logically complete theorem prover would fail in general to achieve translation completeness because the source ontology and target ontology might not overlap.

18. OntoMerge architecture for translating datasets

19. Conclusions
Ontology translation is one of the most difficult problems.
Ontology translation is required when translating datasets, generating ontologies extensions or querying through different ontologies.
Ontology translation can be thought in terms of ontology merging.
If all ontologies, datasets and queries can be expressed in terms of the same internal representation, semantic translation can be implemented by automatic reasoning.
The required reasoning can be thought as simple typed, first order inference.