FCA-MERGE: Bottom-Up Merging of Ontologies

Intelligent Decision Technologies, 2010

A person adds new knowledge to his/her mind, taking into account new information, additional details, better precision, synonyms, homonyms, redundancies, apparent contradictions, and inconsistencies between what he/she knows and new knowledge that he/she acquires. This way, he/she incrementally acquires information keeping it at all times consistent. This information can be represented by Ontologies. In contrast to human approach, algorithms of Ontologies fusion lack these features, merely being computer-aided editors where a person solves the details and inconsistencies. This article presents a method for Ontology Merging (OM), its algorithm and implementation to fuse or join two ontologies (obtained from Web documents) in an automatic fashion (without human intervention), producing a third ontology, and taking into account the inconsistencies, contradictions, and redundancies between both ontologies, thus delivering a result close to reality. The repeated use of OM allows acquisition of much information about the same topic. 2 synonyms among others cases. Nowadays, computers could do the same process (joining knowledge which comes from two different ontologies) through an editor [ §1.2] that makes preliminary alignment of concepts, and lets a person finally decide. It is a computer-aided fusion. The problem to solve is how to mechanize that fusion.

2010

A person adds new knowledge to his/her mind, taking into account new information, additional details, better precision, synonyms, homonyms, redundancies, apparent contradictions, and inconsistencies between what he/she knows and new knowledge that he/she acquires. This way, he/she incrementally acquires information keeping it at all times consistent. This information can be represented by Ontologies. In contrast to human approach, algorithms of Ontologies fusion lack these features, merely being computer-aided editors where a person solves the details and inconsistencies. This article presents a method for Ontology Merging (OM), its algorithm and implementation to fuse or join two ontologies (obtained from Web documents) in an automatic fashion (without human intervention), producing a third ontology, and taking into account the inconsistencies, contradictions, and redundancies between both ontologies, thus delivering a result close to reality. The repeated use of OM allows acquisition of much information about the same topic. Keywords. Ontology, Artificial Intelligence, Ontology fusion"

2006

Nowadays, most of the important information resources that the people require are available through the Internet. The use of several sources in the Internet requires merging the information into a knowledge base in a reasonable way. We will use an ontology, an information technology that manages this knowledge in computers. Merging is an important task and many languages and tools have been developed to describe and process Internet content but the current languages (DAML+OIL, RDF, OWL, etc.) lack a complete expressiveness. For this reason, we present two important improvements to facilitate knowledge interchange: 1) The OM (ontology merging) notation that provides substantial improvements to these languages and 2) The OM algorithm, this is totally automatic in comparison with others (Prompt, Chimaera, OntoMerge, FCA-Merge, IF-Map and ISI) where the user manually solves the most important problems found in the merging

15th International Conference on Computing (CIC 06), 2006

Nowadays, most of the important information resources that the people require are available through the Internet. The use of several sources in the Internet requires merging the information into a knowledge base in a reasonable way. We will use an ontology, an information technology that manages this knowledge in computers. Merging is an important task and many languages and tools have been developed to describe and process Internet content but the current languages (DAML+OIL, RDF, OWL, etc.) lack a complete expressivenes. For this reason, we present two important improvements to facilitate knowledge interchange: 1) The OM (Ontology Merging) Notation that provides substantial improvements to these languages and 2) The OM Algorithm, that is totally automatic in comparison with others (Prompt, Chimaera, OntoMerge, FCA-Merge, IF-Map and ISI) where the user manually solves the most important problems found in the merging.""

2020

Ontology merging is important, but not always effective. The main reason, why ontology merging is not effective, is that ontology merging is performed without considering goals. Goals define the way, in which ontologies to be merged more effectively. The paper illustrates ontology merging by means of rules, which are generated from these ontologies. This is necessary for further use in expert systems.

International Journal of Knowledge Management

The merging procedures of two ontologies are mostly related to the enrichment of one of the input ontologies, i.e. the knowledge of the aligned concepts from one ontology are copied into the other ontology. As a consequence, the resulting new ontology extends the original knowledge of the base ontology, but the unaligned concepts of the other ontology are not considered in the new extended ontology. On the other hand, there are experts-aided semi-automatic approaches to accomplish the task of including the knowledge that is left out from the resulting merged ontology and debugging the possible concept redundancy. With the aim of facing the posed necessity of including all the knowledge of the ontologies to be merged without redundancy, this article proposes an automatic approach for merging ontologies, which is based on semantic similarity measures and exhaustive searching along of the closest concepts. The authors' approach was compared to other merging algorithms, and good res...

2006

No long ago ontology merging was a necessary activity, however, the current methods used in ontology merging present neither detailed cases nor an accurate formalization. For validating these methods, it is convenient to have a case list as complete as possible. In this paper we present the OEGMerge model, developed from the OEG (Ontological Engineering Group at UPM) experience, which describes precisely the merging casuistic and the actions to carry out in each case. In this first approach, the model covers only the taxonomy of concepts, attributes and relations.

Elsevier Knowledge-Based Systems, 2014

With the development of the Semantic Web (SW), the creation of ontologies to formally conceptualize our understanding of various domains has widely increased in number. However, the conceptual and terminological differences (a.k.a semantic heterogeneity problem) between ontologies form a major limiting factor towards their use/reuse and full adoption in practical settings. A key solution to addressing this problem can be through identifying semantic correspondences between the entities (including concepts, relations, and instances) of heterogeneous ontologies, and consequently achieving interoperability between them. This process is also known as ontology alignment. The output of this process can be further exploited to merge ontologies into a single coherent ontology. Indeed, this is widely regarded as a crucial, yet difficult task, specifically when dealing with heavyweight ontologies that consist of hundreds of thousands of concepts. To address this issue, various ontology merging approaches have been proposed. These approaches can be classified into three categories: single-strategy-based approaches, multiple-strategy based approaches, and approaches based on exploiting external semantic resources. In this paper, we first discuss the strengths and limitations of each of these approaches, and then present our framework for addressing the semantic heterogeneity problem through merging domain-specific ontologies based on multiple external semantic resources. The novelty of the proposed approach is mainly based on employing knowledge represented by multiple external resources (knowledge bases in our work) to make aggregated decisions on the semantic correspondences between the entities of heterogeneous ontologies. Other important issues that we attempt to tackle in the proposed framework are: (i) Identifying and handling inconsistency of semantic relations between the ontology concepts and, (ii) Handling the issue of missing background knowledge (such as concepts and instances) in the exploited knowledge bases by utilizing an integrated statistical and semantic technique. Additionally, the proposed solution soundly enriches the knowledge bases with missing background knowledge, and thus enables the reuse of the newly obtained knowledge in future ontology merging tasks. To validate our proposal, we tested the framework using the OAEI 2009 benchmark and compared the produced results with state-of-the-art syntactic and semantic based systems. In addition, we utilized the proposed techniques to merge three heavyweight ontologies from the environmental domain.

Ontologies have been developed for a number of knowledge domains as diverse as clinical terminology, photo camera parts and micro-array gene expression data. However, an innate characteristic of the development of ontologies is that they are often created by independent groups of expertise, which generated the necessity of merging and aligning ontologies covering overlapping domains. Many algorithms and tools have been proposed for merging of ontologies, but most of them disregard the structural properties of the source ontologies, focusing mostly on syntactic analysis. This article focuses on an alignment method for ontologies based on Formal Concept Analysis, a data analysis technique founded on lattice theory.

2002

Researchers in the ontology-design field have developed the content for ontologies in many domain areas. Recently, ontologies have become increasingly common on the World-Wide Web where they provide semantics for annotations in Web pages. 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. The processes of ontology alignment and merging are usually handled manually and often constitute a large and tedious portion of the sharing process. We have developed and implemented PROMPT, an algorithm that provides a semi-automatic approach to ontology merging and alignment. PROMPT performs some tasks automatically and guides the user in performing other tasks for which his intervention is required. PROMPT also determines possible inconsistencies in the state of the ontology, which result from the user's actions, and suggests ways to remedy these inconsistencies. PROMPT is based on an extremely general knowledge model and therefore can be applied across various platforms. Our formative evaluation showed that a human expert followed 90% of the suggestions that PROMPT generated and that 74% of the total knowledge-base operations invoked by the user were suggested by PROMPT.

2010

Ontology mapping and merging systems play a vital role that aim at promoting automatic interoperability among different heterogeneous systems, agents, web services or groups in open environments such as Semantic Web. These systems help ontologists to resolve different types of conflicts among local ontologies to produce global merged ontology. This paper provides three contributions to the study and design of ontology merging systems that provides complete, consistent and coherent merged global ontology. First, we analyze that one of the important merge requirements is ignored yet by state-of-the-art ontology mapping and merging systems, i.e., Disjoint-knowledge Preservation between concepts. Second, we introduce another type of semantic conflict, which needs attention for consistent and coherent merged ontology, i.e., Alignment Conflict among disjoint relations. Third, we present an overview of our semantic-based ontology merger, DKP-OM, as a solution for the generation of global merged ontology that is consistent, coherent and complete with respect to local ontologies. We conclude that disjoint knowledge analysis for ontology merging is very much helpful for the detection of inconsistent initial mappings that originate from concept name or instance matching strategies, reduce search space for concept matching, and promote consistent computation by exploiting reliable logical inference on facts by axiomatization.

The Semantic Web: Research and Applications, 2004

Existing efforts on ontology mapping, alignment and merging vary from methodological and theoretical frameworks, to methods and tools that support the semi-automatic coordination of ontologies. However, only latest research efforts "touch" on the mapping /merging of ontologies using the whole breadth of available knowledge. This paper aims to thoroughly describe the HCONE approach on ontology merging. The approach described is based on (a) capturing the intended informal interpretations of concepts by mapping them to WordNet senses using lexical semantic indexing, and (b) exploiting the formal semantics of concepts' definitions by means of description logics' reasoning services.

The fact that many people simultaneously construct the pages of the Web in an independent way, generates a great obstacle for the machines that track the information in it. Therefore, the concept of Semantic Network has been introduced. It provides a standardization of the information through markup languages (SGML, XML, etc.) where the user generates his own annotations, almost all of them as labels or syntactic rules. Relatively few of the languages have try to represent and to manipulate the knowledge with methods of Artificial Intelligence. This paper proposes a structure (an ontology) more suitable to represent the knowledge, with interesting contributions with respect to current languages (AML+OIL[5], RDF[8], OWL[12]). Also, this paper presents an automatic algorithm to match and merge two or more ontologies. This merging is important when it is desired to increase the knowledge in an ontology. In that way it is possible to accumulate the knowledge in an automatic way. The process of merging begins by obtaining the value of the similarity between each elements of the ontologies (through COM[1] Algorithm); later, the optimal matching is sought. Finally, the result defines the new ontology. This process is performed totally by the computer. That is to say, the user does not take part in this process, as it happens in current merging algorithms (OntoMerge[6], FCA-Merge[9], Chimaera[11], Prompt[13], If-Map[14]). In the merging, the OM Algorithm solves problems of contradiction and reorganization of the final ontology. The efficiency of the algorithm of fusion is demonstrated through several examples.

Research in Computer Science, Special issue on Data Mining and Information Systems, 2006

The fact that many people simultaneously construct the pages of the Web in an independent way, generates a great obstacle for the machines that track the information in it. Therefore, the concept of Semantic Network has been introduced. It provides a standardization of the information through markup languages (SGML, XML, etc.) where the user generates his own annotations, almost all of them as labels or syntactic rules. Relatively few of the languages have try to represent and to manipulate the knowledge with methods of Artificial Intelligence. This paper proposes a structure (an ontology) more suit-able to represent the knowledge, with interesting contributions with respect to current languages (AML+OIL[5], RDF[8], OWL[12]). Also, this paper presents an automatic algorithm to match and merge two or more ontologies. This merg-ing is important when it is desired to increase the knowledge in an ontology. In that way it is possible to accumulate the knowledge in an automatic way. The process of merging begins by obtaining the value of the similarity between each elements of the ontologies (through COM[1] Algorithm); later, the optimal matching is sought. Finally, the result defines the new ontology. This process is performed totally by the computer. That is to say, the user does not take part in this process, as it happens in current merging algorithms (OntoMerge[6], FCA-Merge[9], Chimaera[11], Prompt[13], If-Map[14]). In the merging, the OM Algorithm solves problems of contradiction and reorganization of the final ontology. The efficiency of the algorithm of fusion is demonstrated through several examples.

2013

Abstract. In recent years, researchers have focused on merging knowledge bases in both pragmatic and theoretical points of view. In this paper, we enumerate a few attempts to deal with inconsistencies while merging knowledge bases. We focus on ontology merging and show that pragmatic and theoretical approaches are not integrated and that both could benefit from a closer relationship. We extended an existing theoretical algorithm for Description Logics and applied it for the ontology merging problem. We describe here an implementation of this algorithm as an open source Protégé plugin. 1.