Detection and resolution of semantic inconsistency and redundancy in an automatic ontology merging system

In recent years, researchers have been developing algorithms for the automatic mapping and merging of ontologies to meet the demands of interoperability between heterogeneous and distributed information systems. But, still state-of-the-art ontology mapping and merging systems is semi-automatic that reduces the burden of manual creation and maintenance of mappings, and need human intervention for their validation. The contribution presented in this paper makes human intervention one step more down by automatically identifying semantic inconsistencies in the early stages of ontology merging. We are detecting semantic heterogeneities that occur due to conflicts among the set of Generalized Concept Inclusions, Property Subsumption Criteria, and Constraint Satisfaction Mechanism in local heterogeneous ontologies, which become obstacles for the generation of semantically consistent global merged ontology. We present several algorithms to detect such semantic inconsistencies based on subsumption analysis of concepts and properties in local ontologies from the list of initial mappings. We provide ontological patterns for resolving these inconsistencies automatically. This results global merged ontology free from ??circulatory error in class/property hierarchy??, ??common class between disjoint classes/properties??, ??redundancy of subclass/subproperty of relations?? and other types of ??semantic inconsistency?? errors. Experiments on the real ontologies show that our algorithms save time and cost of traversing local ontologies, improve system??s performance by producing only consistent accurate mappings, and reduce the users?? dependability for ensuring the satisfiability of merged ontology.     

A visual tool for ontology alignment to enable geospatial interoperability

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. Using our approach, querying can be easily extended to new data sources and, therefore, to new regions. In this paper, we describe the AgreementMaker, a tool that displays the ontologies, supports several mapping layers visually, presents automatically generated mappings, and finally produces the agreements.     

本体集成研究综述

本体集成(ontology integration)的目的是使异质的本体互操作,目前是本体研究的一个热点.本体集成首先发现实体间关系,生成本体映射,然后根据应用目的基于映射进行处理,最终达成本体对齐或者本体合并的目标.本文介绍了本体集成中的概念,给出本体集成的一般工程化方法.对国内外较具代表性的本体集成工具进行比较分析,讨论了现存的问题,指出了未来的研究方向.     

Merging of axiomatic definitions of concepts in the complex OWL ontologies

In the last decade, ontology matching and mapping research has shown a measurable progress. This topic draws substantial attention within the research community, though it is not fully researched so far and new complex and effective solutions are needed. Current works are limited in finding alignments or mappings between concepts of heterogeneous ontologies. But, once ontology mappings are found, then how they (or their class expressions) are to be integrated automatically is left open for the ontology merging research. This paper elaborates the mapping of class expressions of concepts and contributes an algorithm for their merging in an automatic ontology merging process without any human intervention. However, the challenge of mapping axiomatic definitions is the most difficult task for merging concept definitions of the source ontologies, but it reveals significant increase in precision and recall values. In addition, with the study of these algorithms, we conclude that ontology merging facilitates when one wants to get ontology with the better quality as the combined rich axioms are added in the merged ontology. We also discuss the results of our first successful participation in the Conference, OA4QA and Anatomy track of OAEI 2015.     

Minimizing conservativity violations in ontology alignments: algorithms and evaluation

In order to enable interoperability between ontology-based systems, ontology matching techniques have been proposed. However, when the generated mappings lead to undesired logical consequences, their usefulness may be diminished. In this paper, we present an approach to detect and minimize the violations of the so-called conservativity principle where novel subsumption entailments between named concepts in one of the input ontologies are considered as unwanted. The practical applicability of the proposed approach is experimentally demonstrated on the datasets from the Ontology Alignment Evaluation Initiative.     

Context-sensitive dependency pairs

Termination is one of the most interesting problems when dealing with context-sensitive rewrite systems. Although a good number of techniques for proving termination of context-sensitive rewriting (CSR) have been proposed so far, the adaptation to CSR of the dependency pair approach, one of the most powerful techniques for proving termination of rewriting, took some time and was possible only after introducing some new notions like collapsing dependency pairs, which are specific for CSR. In this paper, we develop the notion of context-sensitive dependency pair (CSDP) and show how to use CSDPs in proofs of termination of CSR. The implementation and practical use of the developed techniques yield a novel and powerful framework which improves the current state-of-the-art of methods for automatically proving termination of CSR.     

Evolutionary coincidence-based ontology mapping extraction

Abstract: Ontology matching is a process for selection of a good alignment across entities of two (or more) ontologies. This can be viewed as a two-phase process of (1) applying a similarity measure to find the correspondence of each pair of entities from two ontologies, and (2) extraction of an optimal or near optimal mapping. This paper is focused on the second phase and introduces our evolutionary approach for that. To be able to do so, we need a mechanism to score different possible mappings. Our solution is a weighting mechanism named coincidence-based weighting . A genetic algorithm is then introduced to create better mappings in successive iterations. We will explain how we code a mapping as well as our crossover and mutation functions. Evaluation of the algorithm is shown and discussed.     

本体校正的半自动处理过程

创建本体间的映射是个单调冗烦的工作过程,特别是当各本体非常庞大时。从Ontol-ogy的基本理论出发,基于基本概念、角色和关系,探讨了Ontology开发中的各个要素,然后介绍了一种半自动校正处理过程,该过程是在本体校正框架的基础上实现的。由即将被校正的两个本体组成输入处理过程,而输出处理过程则是本体间实体的通信集合。     

Algebraic properties of complete residuated lattice valued tree automata

This paper investigates tree automata based on complete residuated lattice valued (referred to as L-valued) logic. First, we define the notions of L-valued set of pure subsystems and L-valued set of strong pure subsystems, as well as, their relation is considered. Also, L-valued n-tuple operator consist of n successors is defined, some of its properties are examined and its relation with pure subsystem is analyzed. Furthermore, we investigate some concepts such as L-valued set of (strong) homomorphisms, L-valued set of (strong) isomorphisms, and L-valued set of admissible relations. Moreover, we discuss bifuzzy topological characterization of L-valued tree automata. Finally, the relations of homomorphisms between the L-valued tree automata to continuous mappings and open mappings is examined.     

Building linked ontologies with high precision using subclass mapping discovery

The creation of links between schemas of published datasets is a key part of the Linked Open Data (LOD) paradigm. The ability to discover these links “on the go” requires that ontology matching techniques achieve good precision and recall within acceptable execution times. In this paper, we add similarity-based and mediator-based ontology matching methods to the Agreementmaker ontology matching system, which aim to efficiently discover high precision subclass mappings between LOD ontologies. Similarity-based matching methods discover subclass mappings by extrapolating them from a set of high quality equivalence mappings and from the interpretation of compound concept names. Mediator-based matching methods discover subclass mappings by comparing polysemic lexical annotations of ontology concepts and by considering external web ontologies. Experiments show that when compared with a leading LOD approach, Agreementmaker achieves considerably higher precision and F-measure, at the cost of a slight decrease in recall.     

Ontology evolution without tears

The evolution of ontologies is an undisputed necessity in ontology-based data integration. Yet, few research efforts have focused on addressing the need to reflect the evolution of ontologies used as global schemata onto the underlying data integration systems. In most of these approaches, when ontologies change their relations with the data sources, i.e., the mappings, are recreated manually, a process which is known to be error-prone and time-consuming. In this paper, we provide a solution that allows query answering in data integration systems under evolving ontologies without mapping redefinition. This is achieved by rewriting queries among ontology versions and then forwarding them to the underlying data integration systems to be answered. To this purpose, initially, we automatically detect and describe the changes among ontology versions using a high level language of changes. Those changes are interpreted as sound global-as-view (GAV) mappings, and they are used in order to produce equivalent rewritings among ontology versions. Whenever equivalent rewritings cannot be produced we a) guide query redefinition or b) provide the best “over-approximations”, i.e., the minimally-containing and minimally-generalized rewritings. We prove that our approach imposes only a small overhead over traditional query rewriting algorithms and it is modular and scalable. Finally, we show that it can greatly reduce human effort spent since continuous mapping redefinition is no longer necessary.     

Classification and retrieval of knowledge on a parallelmarker-passing architecture

Frame-based systems or semantic networks have been generally used for knowledge representation. In such a knowledge representation system, concepts in the knowledge base are organized based on the subsumption relation between concepts, and classification is a process of constructing a concept hierarchy according to the subsumption relationships. Since the classification process involves search and subsumption test between concepts, classification on a large knowledge base may become unacceptably slow, especially for real-time applications. In this paper, a massively parallel classification and property retrieval algorithm on a marker passing architecture is presented. The subsumption relation is first defined by using the set relationship, and the parallel classification algorithm is described based on that relationship. In this algorithm, subsumption test between two concepts is done by parallel marker passing and multiple subsumption tests are performed simultaneously. To investigate the performance of the algorithm, time complexities of sequential and parallel classification are compared. Simulation of the parallel classification algorithm was performed using the SNAP (Semantic Network Array Processor) simulator, and the influence of several factors on the execution time is discussed     

本体复合映射研究

已有的本体复合映射算法中,绝大部分都只能发现基于字符串比较的简单类型的复合映射,查准率和查全率不高。提出一种基于结点间包含关系和等价关系发现复合映射的方法。实验证明,该方法可以发现本体中包含的绝大部分复合映射,较之现有的复合映射发现算法在查准率和查全率方面有了很大改进。     

A HYBRID METHOD FOR FUZZY ONTOLOGY INTEGRATION

The main contribution of this work consists of combining a heuristic method for propagation of matchable concepts and using consensus techniques for conflict resolution for fuzzy ontology integration. Two central observations behind this approach are as follows: (1) if two concepts across different source ontologies equivalently match each other, then their neighboring concepts will be often matched as well; and (2) conflicts regarding integration of multiple ontologies can be resolved by creating a consensus among the conflict ontological entities. The key idea of the first observation is to start from an aligned pair of concepts (called medoids) to determine so-called potentially common parts to provide additional suggestions for possible matching concepts. This approach is used to obtain pairs of matchable concepts and to avoid pairs of mismatching concepts. On the other hand, the second observation is used to discover a new merged concept from matched concepts by making a consensus among conflict ontological entities. This idea is to determine the best representative as the merged version of the component ones. A combination of both observations for fuzzy ontology integration is a significant contribution of this work. The results of the experiments imply that the proposed approach is effective with regard to both completeness and accuracy.     

Identifying relevant concept attributes to support mapping maintenance under ontology evolution

The success of distributed and semantic-enabled systems relies on the use of up-to-date ontologies and mappings between them. However, the size, quantity and dynamics of existing ontologies demand a huge maintenance effort pushing towards the development of automatic tools supporting this laborious task. This article proposes a novel method, investigating different types of similarity measures, to identify concepts’ attributes that served to define existing mappings. The obtained experimental results reveal that our proposed method allows to identify the relevant attributes for supporting mapping maintenance, since we found correlations between ontology changes affecting the identified attributes and mapping changes.     

Learning to match ontologies on the Semantic Web

On the Semantic Web, data will inevitably come from many different ontologies, and information processing across ontologies is not possible without knowing the semantic mappings between them. Manually finding such mappings is tedious, error-prone, and clearly not possible on the Web scale. Hence the development of tools to assist in the ontology mapping process is crucial to the success of the Semantic Web. We describe GLUE, a system that employs machine learning techniques to find such mappings. Given two ontologies, for each concept in one ontology GLUE finds the most similar concept in the other ontology. We give well-founded probabilistic definitions to several practical similarity measures and show that GLUE can work with all of them. Another key feature of GLUE is that it uses multiple learning strategies, each of which exploits well a different type of information either in the data instances or in the taxonomic structure of the ontologies. To further improve matching accuracy, we extend GLUE to incorporate commonsense knowledge and domain constraints into the matching process. Our approach is thus distinguished in that it works with a variety of well-defined similarity notions and that it efficiently incorporates multiple types of knowledge. We describe a set of experiments on several real-world domains and show that GLUE proposes highly accurate semantic mappings. Finally, we extend GLUE to find complex mappings between ontologies and describe experiments that show the promise of the approach.Received: 16 December 2002, Accepted: 16 April 2003, Published online: 17 September 2003Edited by: Edited by B.V. Atluri, A. Joshi, and Y. Yesha     

Ontology based text indexing and querying for the semantic web

This publication shows how the gap between the HTML based internet and the RDF based vision of the semantic web might be bridged, by linking words in texts to concepts of ontologies. Most current search engines use indexes that are built at the syntactical level and return hits based on simple string comparisons. However, the indexes do not contain synonyms, cannot differentiate between homonyms (‘mouse’ as a pointing vs. ‘mouse’ as an animal) and users receive different search results when they use different conjugation forms of the same word. In this publication, we present a system that uses ontologies and Natural Language Processing techniques to index texts, and thus supports word sense disambiguation and the retrieval of texts that contain equivalent words, by indexing them to concepts of ontologies.

For this purpose, we developed fully automated methods for mapping equivalent concepts of imported RDF ontologies (for this prototype WordNet, SUMO and OpenCyc). These methods will thus allow the seamless integration of domain specific ontologies for concept based information retrieval in different domains.

To demonstrate the practical workability of this approach, a set of web pages that contain synonyms and homonyms were indexed and can be queried via a search engine like query frontend. However, the ontology based indexing approach can also be used for other data mining applications such text clustering, relation mining and for searching free text fields in biological databases. The ontology alignment methods and some of the text mining principles described in this publication are now incorporated into the ONDEX system http://ondex.sourceforge.net/.