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A Probabilistic-Logical Framework for Ontology Matching
"... Ontology matching is the problem of determining correspondences between concepts, properties, and individuals of different heterogeneous ontologies. With this paper we present a novel probabilistic-logical framework for ontology matching based on Markov logic. We define the syntax and semantics and ..."
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Ontology matching is the problem of determining correspondences between concepts, properties, and individuals of different heterogeneous ontologies. With this paper we present a novel probabilistic-logical framework for ontology matching based on Markov logic. We define the syntax and semantics and provide a formalization of the ontology matching problem within the framework. The approach has several advantages over existing methods such as ease of experimentation, incoherence mitigation during the alignment process, and the incorporation of a-priori confidence values. We show empirically that the approach is efficient and more accurate than existing matchers on an established ontology alignment benchmark dataset.
CODI: Combinatorial Optimization for Data Integration – Results for OAEI 2011
"... Abstract. In this paper, we describe our probabilistic-logical alignment system CODI (Combinatorial Optimization for Data Integration). The system provides a declarative framework for the alignment of individuals, concepts, and properties of two heterogeneous ontologies. CODI leverages both logical ..."
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Abstract. In this paper, we describe our probabilistic-logical alignment system CODI (Combinatorial Optimization for Data Integration). The system provides a declarative framework for the alignment of individuals, concepts, and properties of two heterogeneous ontologies. CODI leverages both logical schema information and lexical similarity measures with a well-defined semantics for A-Box and T-Box matching. The alignments are computed by solving corresponding combinatorial optimization problems. 1 Presentation of the system 1.1 State, purpose, general statement CODI (Combinatorial Optimization for Data Integration) leverages terminological structure for ontology matching. The current implementation produces mappings between concepts, properties, and individuals. The system combines lexical similarity measures with schema information to completely avoid incoherence and inconsistency during the alignment process. CODI participates in 2011 for the second time in an OAEI campaign. Thus, we put a special focus on differences compared to the previous 2010 version of
CODI: Combinatorial Optimization for Data Integration – Results for OAEI 2010
"... Abstract. The problem of linking entities in heterogeneous and decentralized data repositories is the driving force behind the data and knowledge integration effort. In this paper, we describe our probabilistic-logical alignment system CODI (Combinatorial Optimization for Data Integration). The syst ..."
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Cited by 15 (3 self)
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Abstract. The problem of linking entities in heterogeneous and decentralized data repositories is the driving force behind the data and knowledge integration effort. In this paper, we describe our probabilistic-logical alignment system CODI (Combinatorial Optimization for Data Integration). The system provides a declarative framework for the alignment of individuals, concepts, and properties of two heterogeneous ontologies. CODI leverages both logical schema information and lexical similarity measures with a well-defined semantics for A-Box and T-Box matching. The alignments are computed by solving corresponding combinatorial optimization problems. 1 Presentation of the system 1.1 State, purpose, general statement CODI (Combinatorial Optimization for Data Integration) leverages terminological structure for ontology matching. The current implementation produces mappings between concepts, properties, and individuals including mappings between object and data type properties. The system combines lexical similarity measures with schema information
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.
Aligning the Parasite Experiment Ontology and the Ontology for Biomedical Investigations Using AgreementMaker
"... Abstract. Tremendous amounts of data exist in life sciences along with many bio-ontologies. Though these databases contain important infor-mation about gene, proteins, functions, etc., this information is not well utilized due to the heterogeneous formats of these databases. Therefore, ontology alig ..."
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Abstract. Tremendous amounts of data exist in life sciences along with many bio-ontologies. Though these databases contain important infor-mation about gene, proteins, functions, etc., this information is not well utilized due to the heterogeneous formats of these databases. Therefore, ontology alignment (OA) is now very critical for life science domain. Our work utilizes AgreementMaker for OA and describes results, difficulties faced in the process, and lessons learned. We aligned two real-world on-tologies, the Parasite Experiment Ontology (PEO) and the Ontology for Biomedical Investigations (OBI). The former is more application-oriented and the latter is a reference ontology for any biomedical or clinical investigations. Our study led to several enhancements to Agree-mentMaker: annotation profiling, mapping provenance information, and tailored lexicon building. These enhancements, which are applicable to any OA system, greatly improved the alignment of these real world on-tologies, producing 90 % precision with 60 % recall from the BSMlex+, the Base Similarity Matcher, and 57 % precision with 67 % recall from the PSMlex+, the Parametric String Matcher, both using lexicon lookup for synonyms. The mappings obtained through this study are posted on BioPortal site for public use.
Probabilistic-Logical Web Data Integration
"... Abstract. The integration of both distributed schemas and data repositories is a major challenge in data and knowledge management applications. Instances of this problem range from mapping database schemas to object reconciliation in the linked open data cloud. We present a novel approach to several ..."
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Abstract. The integration of both distributed schemas and data repositories is a major challenge in data and knowledge management applications. Instances of this problem range from mapping database schemas to object reconciliation in the linked open data cloud. We present a novel approach to several important data integration problems that combines logical and probabilistic reasoning. We first provide a brief overview of some of the basic formalisms such as description logics and Markov logic that are used in the framework. We then describe the representation of the different integration problems in the probabilistic-logical framework and discuss efficient inference algorithms. For each of the applications, we conducted extensive experiments on standard data integration and matching benchmarks to evaluate the efficiency and performance of the approach. The positive results of the evaluation are quite promising and the flexibility of the framework makes it easily adaptable to other realworld data integration problems. 1
www.mdpi.com/journal/ijgi A Bottom-Up Approach for Automatically Grouping Sensor Data Layers by their Observed Property
, 2013
"... Abstract: The Sensor Web is a growing phenomenon where an increasing number of sensors are collecting data in the physical world, to be made available over the Internet. To help realize the Sensor Web, the Open Geospatial Consortium (OGC) has developed open standards to standardize the communication ..."
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Abstract: The Sensor Web is a growing phenomenon where an increasing number of sensors are collecting data in the physical world, to be made available over the Internet. To help realize the Sensor Web, the Open Geospatial Consortium (OGC) has developed open standards to standardize the communication protocols for sharing sensor data. Spatial Data Infrastructures (SDIs) are systems that have been developed to access, process, and visualize geospatial data from heterogeneous sources, and SDIs can be designed specifically for the Sensor Web. However, there are problems with interoperability associated with a lack of standardized naming, even with data collected using the same open standard. The objective of this research is to automatically group similar sensor data layers. We propose a methodology to automatically group similar sensor data layers based on the phenomenon they measure. Our methodology is based on a unique bottom-up approach that uses text processing, approximate string matching, and semantic string matching of data layers. We use WordNet as a lexical database to compute word pair similarities and derive a set-based dissimilarity function using those scores. Two approaches are taken to group data layers: mapping is defined between all the data layers, and clustering is performed to group similar data layers. We evaluate the results of our methodology.
A Terminological Search Algorithm for Ontology Matching
"... Most of the ontology alignment tools use terminological techniques as the initial step and then apply the structural techniques to refine the results. Since each terminological similarity measure considers some features of similarity, ontology alignment systems require exploiting different measures. ..."
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Most of the ontology alignment tools use terminological techniques as the initial step and then apply the structural techniques to refine the results. Since each terminological similarity measure considers some features of similarity, ontology alignment systems require exploiting different measures. While a great deal of effort has been devoted to developing various terminological similarity measures and also developing various ontology alignment systems, little attention has been paid to develop similarity search algorithms which exploit different similarity measures in order to gain benefits and avoid limitations. We propose a novel terminological search algorithm which tries to find an entity similar to an input search string in a given ontology. This algorithm extends the search string by creating a matrix from its synonym and hypernyms. The algorithm employs and combines different kind of similarity measures in different situations to achieve a higher performance, accuracy, and stability in comparison with previous methods which either use one measure or combine more measures in a naive ways such as averaging. We evaluated the algorithm using a subset of OAEI Bench mark data set. Results showed the superiority of proposed algorithm and effectiveness of different applied techniques such as word sense disambiguation and semantic filtering mechanism.
