HY-566 Semantic Web1 Ontology Merging Kyriakos Kritikos (ΥΔ) Miltos Stratakis (MET)

HY-566 Semantic Web 1

Ontology Merging

Kyriakos Kritikos (ΥΔ)Miltos Stratakis (MET)


Representation Matching Problem of creating semantic mappings

between two data representations Mapping examples

Element location of one representation maps to element address of the other

Contact-phone maps to agent-phone Listed-price maps to price * (1 + tax-rate)

Fundamental step in numerous data management applications But, manual effort in semantic mapping has

become intensive, due to the expansive development of the above applications


Applications of Representation Matching (I) Schema integration (early 1980s)

Need to merge a set of given schemas into a single global schema

Data warehousing - Data mining (early 1990s) Need to translate data between multiple databases Data coming from multiple sources must be

transformed to data conforming to a single target schema

Knowledge Base construction (late 1980s, all 1990s)

Used in AI KBs store complex types of entities and relationships,

using “extended database schemas” (ontologies) Requirement of semantic mapping between the

involved ontologies (ontology matching problem)


Applications of Representation Matching (II) Data integration systems (recent years)

Provide an uniform query interface to a big number of data sources, by enabling users to pose queries against a mediated schema

Need to use a set of semantic mappings between the mediated schema and the local schemas of the data sources

Peer data management systems (recent years) Allow peers to query and retrieve data

directly from each other Need of creation of semantic mappings

among the peers


Using Ontologies as Representations Ontology: “Explicit specification of

a conceptualization” Can be used

In an integration task to Describe the semantics of the information

sources Make the content explicit

For the identification and association of semantically corresponding information concepts


Content Explication

The way the ontologies are employed for content explication can be different

We can identify three different directions Single ontology approaches Multiple ontology approaches Hybrid ontology approaches


Single Ontology Approaches Use one global

ontology providing a shared vocabulary for the specification of the semantics

Can be applied to integration problems where all information sources to be integrated provide nearly the same view on a domain

Not effective if one information source has a different view on a domain

globalontology

informationsource

informationsource

informationsource


Multiple Ontology Approaches Each information source

is described by its own ontology

Each source ontology can be developed without respect to other sources or their ontologies

Can simplify the integration task

Supports the change of sources

Not effective in comparing different source ontologies, due to the lack of a common vocabulary

localontology

informationsource

informationsource

informationsource

localontology

localontology


Hybrid Ontology Approaches Semantics of each source

is described by its own ontology, but these ontologies are built from a global shared vocabulary to make them comparable

The shared vocabulary contains basic terms of a domain which are combined in the local ontologies in order to describe more complex semantics

New sources can easily be added without the need of modification

But, existing ontologies can not easily be reused

localontology

informationsource

informationsource

informationsource

localontology

localontology

shared vocabulary


The need for Ontology Matching (Integration)Semantic Web evolution: Requirement for formal descriptions of parts of

our human environment (i.e. descriptions of parts of the real world)

These descriptions, in various degrees of formalness and specificity, are the ontologies

To form a real web of semantics, ontologies from different sources should be linked and related to each other

Problem: The reuse of existing ontologies is often not possible without considerable effort

Ontologies need to Be integrated (i.e. merged into a new ontology) Be aligned (i.e. they have to be brought into mutual

agreement)


Ontology Integration Process

Consists of three steps:1. Find the places in the ontologies

where they overlap2. Relate concepts that are

semantically close via equivalence and subsumption relations (aligning)

3. Check the consistency, coherency and non-redundancy of the result


Technical Problems with Ontology Combination The technical problems that

underlie the difficulties in ontology merging and aligning are: The mismatches that may exist

between separate ontologies (Mismatches between Ontologies)

The synchronization of the changes made to an ontology with the revisions to the applications and data sources that use them (Ontology Versioning)


Mismatches between Ontologies Key type of problems that hinder the

combined use of independently developed ontologies

We distinguish two levels at which these mismatches may appear:

Language or meta-model level Level of the language primitives that are used

to specify an ontology Mismatches at this level are between the

mechanisms to define classes, relations etc. Ontology or model level

Level of the actual ontology of a domain A mismatch at this level is a difference in the

way the domain is modelled


Language level Mismatches

Occur in combinations of ontologies written in different ontology languages

We distinguish four types of this level mismatches Syntax

Different ontology languages often use different syntaxes Constitutes probably the simplest kind of language level

mismatch Logical representation

Existence of different representations of logical notions Focused in which language constructs should be used to

express something, not in whether something can be expressed

Semantics of primitives Sometimes, although the same name is used for a language

construct in two languages, the semantics may differ (e.g. when there are several interpretations of A equalTo B )

Language expressivity Implies that some languages are able to express things that

are not expressible in other languages (e.g. some languages have constructs to express negation and others have not)


Ontology level Mismatches Happen in combination of two or more ontologies that

describe (partly) overlapping domains We can distinguish the mismatches of this level in four

classifications: Conceptualization mismatch

A difference in the way a domain is interpreted, which results in different ontological concepts or different relations between those concepts

Explication mismatch A difference in the way the conceptualization is specified

Terminological mismatch A difference in the way the terms are described

Encoding mismatch Values in the ontologies may be encoded in different formats

(e.g. a date may be represented as “dd/mm/yyyy” or as “mm-dd-yy”)

Terminological and encoding mismatches can be considered as specialized explication mismatches


Conceptualization Mismatches We distinguish two types of these

mismatches: Scope

When two classes seem to represent the same concept, but do not have exactly the same instances (e.g. several administrations use slightly different concepts of employee)

Model coverage and granularity The mismatches of this level are in the part of the

domain that is covered by the ontology or in the level of detail to which that domain is modelled

For example, one ontology might model cars but not trucks, another might represent trucks but only classify them into a few categories, while a third one might make very specified distinctions between types of trucks based on their general physical structure, weight etc.


Explication Mismatches We distinguish two types of these mismatches

focused on the style of modeling: Paradigm

Different paradigms can be used to represent concepts such as time, action, plans etc.

For example, the use of different “top-level” ontology is a mismatch of this type

Concept description Several choices can be made for the modeling of

concepts in the ontology For example, we can consider the place where the

distinction between scientific and non-scientific publications is made

A dissertation can be modelled as dissertation < book < scientific publication < publication, or as dissertation < scientific book < book < publication


Terminological Mismatches We distinguish two term types in which

there can be these mismatches: Synonym terms

Concepts could be represented by different names For example, an ontology may use the term “car”

and another ontology may use the term “automobile”

Homonym terms The meaning of a term could be different in an

other context For example, the term “conductor” has a different

meaning in a music domain than in an electric engineering domain


Ontology Versioning In an open domain, the changes in the

ontologies used are unavoidable, so it becomes very important to keep track of these changes

Although the problem is introduced by subsequent changes to one specific ontology, the most important problems are caused by the dependencies on that ontology

A versioning scheme should pay attention of the following aspects

The relation between succeeding revisions of one ontology

The relation between the ontology and its dependencies: Instance data that conforms to the ontology Other ontologies that are built from or import the ontology Applications that use the ontology


Versioning Scheme Requirements Identification

For every use of a concept or a relation, a versioning framework should provide an distinct reference to the intended definition

Change tracking A versioning framework should make the

relation of one version of a concept or relation to other versions of that construct explicit

Transparent translating A versioning framework should as far as

possible automatically perform conversions from one version to another, to enable transparent access


Practical Problems with Ontology Combination Finding alignments

It is difficult to find the terms that need to be aligned Diagnosis

The consequences of a specific mapping (unforeseen implications) are difficult to see

Repeatability of merges The sources that are used for the merging continue to

evolve The alignments that are created for the merging

should be as much reusable as possible for the merging of the revised ontologies

Very important in the context of ontology maintenance


Problems Overview


Super-imposed Metamodel Transforms information between representations. Approach:

Represent info from diff models in a uniform way Provide a mapping formalism.

Technique: Ontology langs are represented in a meta-model through

RDF triples. Mapping specified by production rules over RDF triples.

+: Mapping rules provide integration at schema and

instance level. -:

Handles only language mismatches but not expressivity. Mappings are specified manually.


OKBC A generic interface to KRS. A KR lang is mapped to OKBC Knowledge

Model (KM). +:

Interoperability achieved at the level of OKBC KM.

Solves language mismatches but not expressivity.

-: Notions requiring higher level of expressivity are

lost. Does not express terminological axioms like

covering, disjointness, partition , exclusion.


OntoMorph (I) Transformation system for symbolic knowledge. Facilitates:

Ontology merging. Rapid generation of KB translators.

Provides 2 mechanisms: Syntactic rewriting via pattern-directed rewrite rules. Semantic rewriting that modulates:

syntactic rewriting via semantic models. logical inference via an integrated KR system.

OntoMorph architecture facilitates incremental development and scripted replay of transforms.


OntoMorph (II) Focuses on aligning ontologies through 3 steps:

Design transforms to bring sources to mutual agreement.

Editing sources to carry out the transforms. Taking the union of the morphed sources.

Steps: 2 is facilitated by transforming ontos in common format. 1 is less automatable and involves human negotiation.

+: Language mismatches but not expressivity. Ontology level mismatches but not coverage of model Repeatability

-: Transforms are expressed manually. Merging is not dealt at all.


Scalable Knowledge Composition Developed algebra for onto composition that:

Operates on directed label graphs like ontos. Each operator has input a graph of semi-structured data

and transforms it to a graph.(composable) Operations are knowledge driven by using articulation

rules that are : Logical rules (semantic implication between terms) Functional rules (conversion between terms across ontos)

Intersection op produces articulation onto that contains terms that are related and their relations.

+: Solves conceptual and terminological mismatches. Rules are expressed by engineer and lexical knowledge. Repeatability.

-: Most rules specified manually. No support for merging.


Chimaera (I) Chimaera is onto merging and diagnosis tool. Supports ontology browsing and editing. It is targeted at lightweight ontologies. Supports 2 merging tasks:

Joins two similar terms under the same name. Identifies terms that should be related by

subsumption, disjointness or instance relations and provides support for the introduction of these relations.

Chimaera also generates by heuristics: Name resolution lists for related terms. Taxonomy resolution lists where it suggests

taxonomy areas for reorganization.


Chimaera (II) Has diagnostic support for :

Verifying Validating Critiquing ontologies.

+: Solves mismatches at terminological and scope of

concept level. Helps alignment by providing possible edit points. Diagnosis of the merging process

-: Not automatic – everything requires user interaction.

No repeatability. Use of local context for edit points.


Prompt Prompt is interactive ontology-merging tool. Guides the user by:

Making suggestions based on linguistic-similarity matches and syntactic clues.

By detecting conflicts of one realization of a suggestion. By proposing conflict resolution strategies.

For every op it populates 3 sets: Changes performed automatically. New suggestions for the user. Conflicts introduced like: name conflicts, dangling

references, redundancy in class-hierarchy and inconsistencies.

Prompt points to places requiring change and for every place it proposes new actions.

Adv – disadv same as Chimaera but supports repeatability.


FCA-Merge (I) FCA-Merge:

A bottom-up approach for ontology-merging Offers a global structural desc of the merge process Its mechanism based on instances of 2 ontos.

The merge process contains 3 steps: Instance extraction by natural language techniques

and computation of 2 formal contexts based on extracted instances.

Derivation of a common context and computation of pruned concept lattice by math techniques of FCA.

Generation of merged-ontology based on concept lattice with the help of engineer and OntoEdit


FCA-Merge (II) Restrictions:

Input documents should be domain-dependent. Each doc should cover all concepts from source

ontos. Each doc must separate the concepts well enough –

> if concepts not separated rightly by the method, the engineer should provide more and better docs.

+’s and –’s: Terminological and scope of concepts mismatches. Finding alignments with the help of the lattice. Diagnosis of results by using OntoEdit. Repeatability by storing the pruned concept lattice.


GLUE (I) Applies machine learning techniques for alignment. 3 main points:

Computation of joint probability distribution of every concepts involved. In this way:

Any similarity measure can be computed with JBD. Approach applicable to broad range of ontology-matching

problems. Multi-strategy learning for computing JBD. In this way:

Many types of info can be used to maximize the matching accuracy.

System extensible to new learners. Exploits domain restrictions and general heuristics for

maximizing matching accuracy by using relaxation labeling. Process compose of 3 main steps performed by the

automatable components: Distribution Estimator, Similarity Estimator and Relaxation Labeler.


GLUE (II) Restrictions:

Only 1-1 mapping of concepts. Nodes not matched cause insufficient training data. Implementation of base learners resulted in single

general-purpose text classification. Nodes not matched cause they are ambiguous. User

interaction is needed in this way. Some pair of nodes should not be examined at all.

+’s and –’s: Local scope of concepts and proper classification. Finding alignments and repeatability automatic. Different encoding is solved by adding appropriate

learner.


Anchor-Prompt (I) Has input a pair of similar pairs provided by

user or by heuristics. Its algorithm analyzes the paths in the onto

sub-graph and determined which classes frequently appear in similar positions.

Extends the approaches used in Prompt. It is implemented upon OKBC protocol. It finds only 1-1 mappings between

concepts


Anchor-Prompt (II) Limitations:

Very long paths don’t produce accurate results. Path-length=0 (Chimaera), Path-length=1

(Prompt). Incidental matches can be produced (simil

limit). When comparing a deep ontology with many

slots and a shallow ontology that has slot relating top classes, then results are same with Prompt.

+’s: Concept scope mismatches are dealt with. Finding alignments and repeatability are

automatic tasks.


SHOE An HTML-based ontology language. Provides a rule mechanism for alignment:

Common items are mapped by inference rules. Terminological diffs are mapped by if-and-only-if

rules. Scope diffs require mapping of categories where the

one subsumes the other. Encoding diffs handled by mapping individual values.

Provides version numbers to ontologies and facilitates both identification of the revisions and explicit specification of its relation to other revisions (change-tracking).


Conclusions (I) Discovered 4 different approaches that handle

interoperability at the language level: Aligning the meta-model. Layered interoperability. Transformation rules. Mapping onto a common knowledge model.

We found tools that suggest alignments and mappings with the use of heuristics. There are two types of heuristics:

Linguistic based-matches (FCA-Merge). Structural and model similarity (Chimaera and

Prompt).


Conclusions (II) We found tools that semi-automate or

fully-automate the merging process but having only 1-1 mappings of concept using different techniques: Computation of pruned concept lattice (FCA-

Merge). Linguistic and FCA techniques. Machine learning techniques (GLUE). Using global instead of local context

(Anchor-Prompt). Interoperability at the model can be

achieved by a common top level ontology. Conform to a common standard.


Conclusions (III) Different approaches for diagnosing or

checking the results of assignments: Domain independent verification and validation

checks: name conflicts, dangling references etc. Validation that requires reasoning: redundancy at

the class hierarchy, value restrictions violated etc. Several tools support an executable

specification of mappings and transforms (SKC,OntoMorph,Prompt,FCA-Merge,GLUE,Anchor-Prompt).

Most techniques and tools don’t deal versioning.

HY-566 Semantic Web1 Ontology Merging Kyriakos Kritikos (ΥΔ) Miltos Stratakis (MET)

Documents

Transcript of HY-566 Semantic Web1 Ontology Merging Kyriakos Kritikos (ΥΔ) Miltos Stratakis (MET)