Ontologies tend to be found everywhere. They are viewed as the silver bullet for many applications, such as database integration, peer-to-peer systems, e-commerce, semantic web services, or social networks. However, in open or evolving systems, such as the semantic web, different parties would, in general, adopt different ontologies. Thus, merely using ontologies, like using XML, does not reduce heterogeneity: it just raises heterogeneity problems to a higher level. Euzenat and Shvaikos book is devoted to ontology matching as a solution to the semantic heterogeneity problem faced by computer systems. Ontology matching aims at finding correspondences between semantically related entities of different ontologies. These correspondences may stand for equivalence as well as other relations, such as consequence, subsumption, or disjointness, between ontology entities. Many different matching solutions have been proposed so far from various viewpoints, e.g., databases, information systems, and artificial intelligence. The second edition of Ontology Matching has been thoroughly revised and updated to reflect the most recent advances in this quickly developing area, which resulted in more than 150 pages of new content. In particular, the book includes a new chapter dedicated to the methodology for performing ontology matching. It also covers emerging topics, such as data interlinking, ontology partitioning and pruning, context-based matching, matcher tuning, alignment debugging, and user involvement in matching, to mention a few. More than 100 state-of-the-art matching systems and frameworks were reviewed. With Ontology Matching, researchers and practitioners will find a reference book that presents currently available work in a uniform framework. In particular, the work and the techniques presented in this book can be equally applied to database schema matching, catalog integration, XML schema matching and other related problems. The objectives of the book include presenting (i) the state of the art and (ii) the latest research results in ontology matching by providing a systematic and detailed account of matching techniques and matching systems from theoretical, practical and application perspectives.

Ontology Matching

: This thesis applies neural network feature selection techniques to multivariate time series data to improve prediction of a target time series. Two approaches to feature selection are used. First, a subset enumeration method is used to determine which financial indicators are most useful for aiding in prediction of the S&P 500 futures daily price. The candidate indicators evaluated include RSI, Stochastics and several moving averages. Results indicate that the Stochastics and RSI indicators result in better prediction results than the moving averages. The second approach to feature selection is calculation of individual saliency metrics. A new decision boundary-based individual saliency metric, and a classifier independent saliency metric are developed and tested. Ruck's saliency metric, the decision boundary based saliency metric, and the classifier independent saliency metric are compared for a data set consisting of the RSI and Stochastics indicators as well as delayed closing price values. The decision based metric and the Ruck metric results are similar, but the classifier independent metric agrees with neither of the other metrics. The nine most salient features, determined by the decision boundary based metric, are used to train a neural network and the results are presented and compared to other published results. (AN)

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Nonlinear Time Series Analysis.

After years of research on ontology matching, it is reasonable to consider several questions: is the field of ontology matching still making progress? Is this progress significant enough to pursue further research? If so, what are the particularly promising directions? To answer these questions, we review the state of the art of ontology matching and analyze the results of recent ontology matching evaluations. These results show a measurable improvement in the field, the speed of which is albeit slowing down. We conjecture that significant improvements can be obtained only by addressing important challenges for ontology matching. We present such challenges with insights on how to approach them, thereby aiming to direct research into the most promising tracks and to facilitate the progress of the field.

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Ontology Matching: State of the Art and Future Challenges

Results of the Ontology Alignment Evaluation Initiative

Ontology matching consists of finding correspondences between semantically related entities of two ontologies. OAEI campaigns aim at comparing ontology matching systems on precisely defined test cases. These test cases can use ontologies of different nature (from simple thesauri to expressive OWL ontologies) and use different modalities, e.g., blind evaluation, open evaluation, consensus. OAEI 2012 offered 7 tracks with 9 test cases followed by 21 participants. Since 2010, the campaign has been using a new evaluation modality which provides more automation to the evaluation. This paper is an overall presentation of the OAEI 2012 campaign.

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Results of the ontology alignment evaluation initiative 2012

Existing ontology matching algorithms use a combination of lexical and structural correspondence between source and target ontologies. We present a realistic case-study where both types of overlap are low: matching two unstructured lists of vocabulary used to describe patients at Intensive Care Units in two different hospitals. We show that indeed existing matchers fail on our data. We then discuss the use of background knowledge in ontology matching problems. In particular, we discuss the case where the source and the target ontology are of poor semantics, such as flat lists, and where the background knowledge is of rich semantics, providing extensive descriptions of the properties of the concepts involved. We evaluate our results against a Gold Standard set of matches that we obtained from human experts.

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Matching unstructured vocabularies using a background ontology

Discovering mappings between concept hierarchies is widely regarded as one of the hardest and most urgent problems facing the Semantic Web. The problem is even harder in domains where concepts are inherently vague and ill-defined, and cannot be given a crisp definition. A notion of approximate concept mapping is required in such domains, but until now, no such notion is vailable. The first contribution of this paper is a definition for approximate mappings between concepts. Roughly, a mapping between two concepts is decomposed into a number of submappings, and a sloppiness value determines the fraction of these submappings that can be ignored when establishing the mapping. A potential problem of such a definition is that with an increasing sloppiness value, it will gradually allow mappings between any two arbitrary concepts. To improve on this trivial behaviour, we need to design a heuristic weighting which minimises the sloppiness required to conclude desirable matches, but at the same time maximises the sloppiness required to conclude undesirable matches. The second contribution of this paper is to show that a Google based similarity measure has exactly these desirable properties. We establish these results by experimental validation in the domain of musical genres. We show that this domain does suffer from ill-defined concepts. We take two real-life genre hierarchies from the Web, we compute approximate mappings between them at varying levels of sloppiness, and we validate our results against a handcrafted Gold Standard. Our method makes use of the huge amount of knowledge that is implicit in the current Web, and exploits this knowledge as a heuristic for establishing approximate mappings between ill-defined concepts.

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Using Google distance to weight approximate ontology matches

We investigate the use of a background knowledge ontology in ontology matching. We conducted experiments on matching two medical ontologies using a third extensive one as background knowledge, and compare the results with directly matching the two ontologies. Our results indicate that using background knowledge, in particular the exploitation of its structure, has enormous benefits on the matching. The structure of the background ontology needs closer examination to determine how to use it in order to obtain maximal benefit.

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Exploiting the structure of background knowledge used in ontology matching

Recently, two methods for quantifying the stability of a dynamical system have been applied to human locomotion: local stability (quantified by finite time maximum Lyapunov exponents, λs and λL) and orbital stability (quantified by maximum Floquet multipliers, MaxFm). In most studies published to date, data from optoelectronic measurement systems were used to calculate these measures. However, using wireless inertial sensors may be more practical as they are easier to use, also in ambulatory applications. While inertial sensors have been employed in some studies, it is unknown whether they lead to similar stability estimates as obtained with optoelectronic measurement systems. In the present study, we compared stability measures of human walking estimated from an optoelectronic measurement system with those calculated from an inertial sensor measurement system. Subjects walked on a treadmill at three different speeds while kinematics were recorded using both measurement systems. From the angular velocities and linear accelerations, λs, λL, and MaxFm were calculated. Both measurement systems showed the same effects of walking speed for all variables. Estimates from both measurement systems correlated high for λs and λL, (R > 0.85) but less strongly for MaxFm (R = 0.66). These results indicate that inertial sensors constitute a valid alternative for an optoelectronic measurement system when assessing dynamic stability in human locomotion, and may thus be used instead, which paves the way to studying gait stability during natural, everyday walking.

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Estimating dynamic gait stability using data from non-aligned inertial sensors.

A transmitter for transmitting at least two main signal components (L,R) includes a first data compression circuit (BRR1) for performing a data compression of a first of the main signal components, a data expansion circuit (DEQ) for performing a data expansion of the compressed signal component so as to obtain a replica of the first main signal component, and a matrixing circuit for combining the second main signal component with the replica of the first main signal component so as to obtain a combined signal (M'). A second data compression circuit (BRR2) performs a data compression of the combined signal (M') in response to a masking control signal. The output signals of the first and second data compression circuits are combined as a composite signal for transmission via a transmission medium, for example a record carrier. The invention also provide a receiver for such a transmitted composite signal. The transmitter may be adapted to provide for transmission of three individual signal components (L,R,C).

Warner ten Kate

Papers

Matching unstructured vocabularies using a background ontology

Using Google distance to weight approximate ontology matches

Exploiting the structure of background knowledge used in ontology matching

Estimating dynamic gait stability using data from non-aligned inertial sensors.

Transmitter, receiver and record carrier for transmitting/receiving at least a first and a second signal component