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AgreementMaker: Efficient Matching for Large Real-World Schemas and Ontologies
, 2009
"... We present the AgreementMaker system for matching realworld schemas and ontologies, which may consist of hundreds or even thousands of concepts. The end users of the system are sophisticated domain experts whose needs have driven the design and implementation of the system: they require a responsive ..."
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Cited by 43 (8 self)
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We present the AgreementMaker system for matching realworld schemas and ontologies, which may consist of hundreds or even thousands of concepts. The end users of the system are sophisticated domain experts whose needs have driven the design and implementation of the system: they require a responsive, powerful, and extensible framework to perform, evaluate, and compare matching methods. The system comprises a wide range of matching methods addressing different levels of granularity of the components being matched (conceptual vs. structural), the amount of user intervention that they require (manual vs. automatic), their usage (stand-alone vs. composed), and the types of components to consider (schema only or schema and instances). Performance measurements (recall, precision, and runtime) are supported by the system, along with the weighted combination of the results provided by those methods. The AgreementMaker has been used and tested in practical applications and in the Ontology Alignment Evaluation Initiative (OAEI) competition. We report here on some of its most advanced features, including its extensible architecture that facilitates the integration and performance tuning of a variety of matching methods, its capability to evaluate, compare, and combine matching results, and its user interface with a control panel that drives all the matching methods and evaluation strategies.
Using the AgreementMaker to Align Ontologies for the OAEI Campaign 2007 ⋆
"... Abstract. In this paper, we present the AgreementMaker, an ontology alignment tool that incorporates the Descendants Similarity Inheritance (DSI) method. This method uses the structure of the ontology graphs for contextual information, thus providing the matching process with more semantics. We have ..."
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Cited by 26 (6 self)
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Abstract. In this paper, we present the AgreementMaker, an ontology alignment tool that incorporates the Descendants Similarity Inheritance (DSI) method. This method uses the structure of the ontology graphs for contextual information, thus providing the matching process with more semantics. We have tested our method on the ontologies included in the anatomy track of the OAEI 2007 campaign. 1 Presentation of the System In distributed database applications with heterogeneous classification schemes that describe related domains, an ontology-driven approach to data sharing and interoperability relies on the alignment of concepts across different ontologies. Once the alignment is established, agreements that encode a variety of mappings between the concepts of the aligned ontologies are derived. In this way, users can potentially query the concepts of a given ontology in terms of other ontologies. To enable scalability both in the size and the number of the ontologies involved, the alignment method should be automatic. In order to achieve this, we have been working on a framework that supports the alignment of two ontologies. In our framework, we introduce an alignment approach that uses
Observation-driven geo-ontology engineering
- Transaction in GIS
, 2012
"... Big Data, Linked Data, Smart Dust, Digital Earth, and e-Science are just some of the names for research trends that surfaced over the last years. While all of them address different visions and needs, they share a common theme: How do we manage massive amounts of heterogeneous data, derive knowledge ..."
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Cited by 19 (7 self)
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Big Data, Linked Data, Smart Dust, Digital Earth, and e-Science are just some of the names for research trends that surfaced over the last years. While all of them address different visions and needs, they share a common theme: How do we manage massive amounts of heterogeneous data, derive knowledge out of them instead of drowning in information, and how do we make our findings reproducible and reusable by others? In a network of knowledge, topics span across scientific disciplines and the idea of domain ontologies as common agreements seems like an illusion. In this work, we argue that these trends require a radical paradigm shift in ontology engineering away from a small number of authoritative, global ontologies developed top-down, to a high number of local ontologies that are driven by application needs and developed bottom-up out of observation data. Similarly as the early Web was replaced by a social Web in which volunteers produce data instead of purely consuming it, the next generation of knowledge infrastructures has to enable users to become knowledge engineers themselves. Surprisingly, existing ontology engineering frameworks are not well suited for this new perspective. Hence, we propose an observation-driven ontology engineering framework, show how its layers can be realized using specific methodologies, and relate the framework to existing work on geo-ontologies. 1
SIM-DLA: A Novel Semantic Similarity Measure for Description Logics Reducing Inter-Concept to Inter-Instance Similarity
"... Abstract. While semantic similarity plays a crucial role for human categorization and reasoning, computational similarity measures have also been applied to fields such as semantics-based information retrieval or ontology engineering. Several measures have been developed to compare concepts specifie ..."
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Cited by 18 (5 self)
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Abstract. While semantic similarity plays a crucial role for human categorization and reasoning, computational similarity measures have also been applied to fields such as semantics-based information retrieval or ontology engineering. Several measures have been developed to compare concepts specified in various description logics. In most cases, these measures are either structural or require a populated ontology. Structural measures fail with an increasing expressivity of the used description logic, while several ontologies, e.g., geographic feature type ontologies, are not populated at all. In this paper, we present an approach to reduce interconcept to inter-instance similarity and thereby avoid the canonization problem of structural measures. The novel approach, called SIM-DLA, reuses existing similarity functions such as co-occurrence or network measures from our previous SIM-DL measure. The required instances for comparison are derived from the completion tree of a slightly modified DL-tableau algorithm as used for satisfiability checking. Instead of trying to find one (clash-free) model, the new algorithm generates a set of proxy individuals used for comparison. The paper presents the algorithm, alignment matrix, and similarity functions as well as a detailed example. 1
Efficient Selection of Mappings and Automatic Quality-driven Combination of Matching Methods ⋆
"... Abstract. The AgreementMaker system for ontology matching includes an extensible architecture that facilitates the integration and performance tuning of a variety of matching methods, an evaluation mechanism, which can make use of a reference matching or rely solely on “inherent ” quality measures, ..."
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Cited by 14 (4 self)
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Abstract. The AgreementMaker system for ontology matching includes an extensible architecture that facilitates the integration and performance tuning of a variety of matching methods, an evaluation mechanism, which can make use of a reference matching or rely solely on “inherent ” quality measures, and a multi-purpose user interface, which drives both the matching methods and the evaluation strategies. In this paper, we focus on two main features of AgreementMaker. The former is an optimized method that performs the selection of mappings given the similarities between entities computed by any matching algorithm, a threshold value, and the desired cardinalities of the mappings. Experiments show that our method is more efficient than the typically adopted combinatorial method. The latter is the evaluation framework, which includes three “inherent ” quality measures that can be used both to evaluate matching methods when a reference matching is not available and to combine multiple matching results by defining the weighting scheme of a fully automatic combination method. 1
A transparent semantic enablement layer for the geospatial web
- IN: TERRA COGNITA 2009 WORKSHOP IN CONJUNCTION WITH THE 8TH INTERNATIONAL SEMANTIC WEB CONFERENCE (ISWC
, 2009
"... Building on abstract reference models, the Open Geospatial Consortium (OGC) has established standards for storing, discovering, and processing geographical information. These standards act as basis for the implementation of specific services and Spatial Data Infrastructures (SDI). Research on geo-se ..."
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Cited by 9 (2 self)
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Building on abstract reference models, the Open Geospatial Consortium (OGC) has established standards for storing, discovering, and processing geographical information. These standards act as basis for the implementation of specific services and Spatial Data Infrastructures (SDI). Research on geo-semantics plays an increasing role to support complex queries and retrieval across heterogeneous information sources, as well as for service orchestration, semantic translation, and on-the-fly integration. So far, this research targets individual solutions or focuses on the Semantic Web, leaving the integration into SDI aside. What is missing is a shared and transparent semantic enablement layer for Spatial Data Infrastructures which also integrates reasoning services known from the Semantic Web. Focusing on Sensor Web Enablement (SWE), we outline how Spatial Data Infrastructures in general can benefit from such a semantic enablement layer. Instead of developing new semantically enabled services from scratch, we propose to create profiles of existing services that implement a transparent mapping between the OGC and the Semantic Web world.
M.: Interactive User Feedback in Ontology Matching Using Signature Vectors
, 2012
"... Abstract — When compared to a gold standard, the set of mappings that are generated by an automatic ontology matching process is neither complete nor are the individual mappings always correct. However, given the explosion in the number, size, and complexity of available ontologies, domain experts n ..."
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Cited by 8 (3 self)
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Abstract — When compared to a gold standard, the set of mappings that are generated by an automatic ontology matching process is neither complete nor are the individual mappings always correct. However, given the explosion in the number, size, and complexity of available ontologies, domain experts no longer have the capability to create ontology mappings without considerable effort. We present a solution to this problem that consists of making the ontology matching process interactive so as to incorporate user feedback in the loop. Our approach clusters mappings to identify where user feedback will be most beneficial in reducing the number of user interactions and system iterations. This feedback process has been implemented in the AgreementMaker system and is supported by visual analytic techniques that help users to better understand the matching process. Experimental results using the OAEI benchmarks show the effectiveness of our approach. We will demonstrate how users can interact with the ontology matching process through the AgreementMaker user interface to match real-world ontologies. I.
Cluster-based similarity aggregation for ontology matching
- In Proc. 6th ISWC workshop on ontology matching (OM), Bonn (DE
, 2011
"... Abstract. Cluster-based similarity aggregation (CSA) is an automatic similarity aggregating system for ontology matching. The system have two main part. The first is calculation and combination of different similarity measures. The second is extracting alignment. The system first calculates five dif ..."
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Cited by 7 (0 self)
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Abstract. Cluster-based similarity aggregation (CSA) is an automatic similarity aggregating system for ontology matching. The system have two main part. The first is calculation and combination of different similarity measures. The second is extracting alignment. The system first calculates five different basic measures to create five similarity matrixes, i.e, string-based similarity measure, WordNet-based similarity measure... Furthermore, it exploits the advantage of each mea-sure through a weight estimation process. These similarity matrixes are combined into a final similarity matrix. After that, the pre-alignment is extracted from this matrix. Finally, to increase the accuracy of the system, the pruning process is applied. 1 Presentation of the system In the Internet, ontologies are widely used to provide semantic to data. Since they are created by different users for different purposes, we need to develop a method to match multiple ontologies for integrating data from different resources [2]. 1.1 State, purpose, general statement CSA (Cluster-based Similarity Aggregation) is the automatic weight aggregating sys-tem for ontology alignment. The system is designed to search for semantic correspon-dence between heterogeneous data sources from different ontologies. The current im-plementation only support one-to-one alignment between concepts and properties (in-cluding object properties and data properties). The core of CSA is utilizing the advan-tage of each basic strategy for the alignment process. For example, the string-based similarity measure works well when the two entities are similar linguistically while the structure-based similarity measure is effective when the two entities are similar in their local structure. The system automatically combines many similarity measurements based on the analysis of their similarity matrix. Details of the system are described in the following parts.
