Ontology Alignment

X-SOM: A Flexible Ontology Mapper

Authors: 
Curino, Carlo A.; Orsi, Giorgio; Tanca, Letizia
Year: 
2008
Venue: 
DEXA Workshop

System interoperability is a well known issue, especially for heterogeneous information systems, where ontology-based representations may support automatic and user-transparent integration. In this paper we present X-SOM: an ontology mapping and integration tool. The contribution of our tool is a modular and extensible architecture that automatically combines several matching techniques by means of a neural network, performing also ontology debugging to avoid inconsistencies.

Information Systems Integration and Evolution: Ontologies at Rescue

Authors: 
Curino, Carlo A.; Tanca, Letizia; Zaniolo, Carlo
Year: 
2008
Venue: 
STSM

The life of a modern Information System is often characterized by (i) a push toward integration with other systems, and (ii) the evolution of its data management core in response to continuously changing application requirements. Most of the current proposals dealing with these issues from a database perspective rely on the formal notions of mapping and query rewriting.

Automatic Ontology Matching using Application semantics

Authors: 
Gal, A.; Modica, G.; Jamil, H.; Eyal, A.
Year: 
2005
Venue: 
AI Magazine

We propose the use of application semantics to enhance the process of semantic reconciliation. Application semantics involve those elements of the business reasoning that affect the way concepts are presented to users, their layout, etc. In particular, we pursue in this paper the notion of precedence, in which temporal constraints determine the ordering of concepts when presented to the user.

A visual tool for ontology alignment to enable geospatial interoperability

Authors: 
Cruz, IF; Sunna, W; Makar, N; Bathala, S
Year: 
2007
Venue: 
Journal of Visual Languages and Computing

In distributed geospatial applications with heterogeneous databases, an ontology-driven approach to data integration relies on the alignment of the concepts of a global ontology that describe the domain, with the concepts of the ontologies that describe the data in the distributed databases. Once the alignment between the global ontology and each distributed ontology is established, agreements that encode a variety of mappings between concepts are derived. In this way, users can potentially query hundreds of geospatial databases using a single query.

A Framework for Aligning Ontologies

Authors: 
Lambrix, P; Tan, H
Year: 
2005
Venue: 
Proc. 3rd Int Workshop on Principles and Practice of Semantic Web Reasoning. Springer LNCS 3703

Ontologies are an important technology for the Semantic Web. In different areas ontologies have already been developed and many of these ontologies contain overlapping information. Often we would therefore want to be able to use multiple ontologies and thus the ontologies need to be aligned. Currently, there exist a number of systems that support users in aligning ontologies, but not many comparative evaluations have been performed.

The PROMPT suite: interactive tools for ontology merging and mapping

Authors: 
Noy, N.F.; Musen, M.A.
Year: 
2003
Venue: 
International Journal of Human-Computer Studies

Researchers in the ontology-design field have developed the content for ontologies in many domain areas. This distributed nature of ontology development has led to a large number of ontologies covering overlapping domains. In order for these ontologies to be reused, they first need to be merged or aligned to one another. We developed a suite of tools for managing multiple ontologies. These suite provides users with a uniform framework for comparing, aligning, and merging ontologies, maintaining versions, translating between different formalisms.

PromptDiff: A fixed-point algorithm for comparing ontology versions

Authors: 
Noy, N.F.; Musen, M.A.
Year: 
2002
Venue: 
18th National Conference on Artificial Intelligence (AAAI-

As ontology development becomes a more ubiquitous and collaborative process, the developers face the problem of maintaining versions of ontologies akin to maintaining versions of software code in large software projects. Versioning systems for software code provide mechanisms for tracking versions, checking out versions for editing, comparing different versions, and so on. We can directly reuse many of these mechanisms for ontology versioning. However, version comparison for code is based on comparing text files—an approach that does not work for comparing ontologies.

Combining and relating ontologies: an analysis of problems and solutions

Authors: 
Klein, Michel
Year: 
2001
Venue: 
IJCAI'01 Workshop on Ontologies and Information Sharing, 2001

With the grown availability of large and specialized online ontologies, the questions about the combined use of independently developed ontologies have become even more important. Although there is already a lot of research done in this area, there are still many open questions. In this paper we try to classify the problems that may arise into a common framework. We then use that framework to examine several projects that aim at some ontology combination task, thus sketching the state of the art.

Ontology Matching: A Machine Learning Approach

Authors: 
Doan, AnHai; Madhavan, Jayant; Domingos, Pedro; Halevy, Alon
Year: 
2004
Venue: 
Handbook on Ontologies in Information Systems

This chapter studies ontology matching: the problem of finding the seman-
tic mappings between two given ontologies. This problem lies at the heart of
numerous information processing applications. Virtually any application that
involves multiple ontologies must establish semantic mappings among them,
to ensure interoperability. Examples of such applications arise in myriad do-
mains, including e-commerce, knowledge management, e-learning, information
extraction, bio-informatics, web services, and tourism (see Part D of this book
on ontology applications).

Learning to Map between Ontologies on the Semantic Web

Authors: 
Doan, AnHai; Madhavan, Jayant; Domingos, Pedro; Halevy, Alon
Year: 
2002
Venue: 
WWW2002, 2002

Ontologies play a prominent role on the Semantic Web. They make possible the widespread publication of machine understandable data, opening myriad opportunities for automated information processing. However, because of the Semantic Web's distributed nature, data on it will inevitably come from many different ontologies. Information processing across ontologies is not possible without knowing the semantic mappings between their elements. Manually finding such mappings is tedious, error-prone, and clearly not possible at the Web scale.

Semantic matching

Authors: 
Giunchiglia, F.; Shvaiko, P.
Year: 
2003
Venue: 
The Knowledge Engineering Review Journal, 2003

QOM - Quick Ontology Mapping

Authors: 
Ehrig, M.; Staab, S.
Year: 
2004
Venue: 
ISWC, 2004

(Semi-)automatic mapping — also called (semi-)automatic alignment
— of ontologies is a core task to achieve interoperability when two agents or
services use different ontologies. In the existing literature, the focus has so far
been on improving the quality of mapping results. We here consider QOM, Quick
Ontology Mapping, as a way to trade off between effectiveness (i.e. quality)
and efficiency of the mapping generation algorithms. We show that QOM has
lower run-time complexity than existing prominent approaches. Then, we show

Bootstrapping Ontology Alignment Methods with APFEL

Authors: 
Ehrig, M.; Staab, S.; Sure, Y.
Year: 
2005
Venue: 
Proc. ISWC, 2005, LNCS 3729

Ontology alignment is a prerequisite in order to allow for interoperation between different ontologies and many alignment strategies have been proposed to facilitate the alignment task by (semi-)automatic means. Due to the complexity of the alignment task, manually defined methods for (semi-)automatic alignment rarely constitute an optimal configuration of substrategies from which they have been built. In fact, scrutinizing current ontology alignment methods, one may recognize that most are not optimized for given ontologies.

Ontology Versioning in an Ontology Management Framework

Authors: 
Noy, Natalya Fridman; Musen, Mark A.
Year: 
2004
Venue: 
IEEE Intelligent Systems 19(4): 6-13 (2004)
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