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

https://www.alaakhamis.org/teaching/MCT200/reading/Information%20Fusion.pdf

Multisensor data fusion: A review of the state-of-the-art

1. Place animal in induction chamber and anesthetize the mouse and ensure sedation. 2. Once the animal is sedated, move it to a nose cone for hair removal using cream. Only apply cream to the area of the chest that will be utilized for imaging. Once the hair is removed, wipe area with wet gauze to ensure all hair is removed. 3. Move the animal to the imaging platform and tape its paws to the ECG lead plates and insert rectal probe. Body temperature should be maintained at 36-37°C. During imaging, reduce anesthesia to maintain proper heart rate. If the animal shows signs of being awake, use a higher concentration of anesthetic.

人唾液天疱疮抗原的纯化

Subspace clustering is an extension of traditional clustering that seeks to find clusters in different subspaces within a dataset. Often in high dimensional data, many dimensions are irrelevant and can mask existing clusters in noisy data. Feature selection removes irrelevant and redundant dimensions by analyzing the entire dataset. Subspace clustering algorithms localize the search for relevant dimensions allowing them to find clusters that exist in multiple, possibly overlapping subspaces. There are two major branches of subspace clustering based on their search strategy. Top-down algorithms find an initial clustering in the full set of dimensions and evaluate the subspaces of each cluster, iteratively improving the results. Bottom-up approaches find dense regions in low dimensional spaces and combine them to form clusters. This paper presents a survey of the various subspace clustering algorithms along with a hierarchy organizing the algorithms by their defining characteristics. We then compare the two main approaches to subspace clustering using empirical scalability and accuracy tests and discuss some potential applications where subspace clustering could be particularly useful.

/pdf/subspace-clustering-for-high-dimensional-data-a-review-56727kn4va.pdf

Subspace clustering for high dimensional data: a review

Schema and ontology matching is a critical problem in many application domains, such as semantic web, schema/ontology integration, data warehouses, e-commerce, etc. Many different matching solutions have been proposed so far. In this paper we present a new classification of schema-based matching techniques that builds on the top of state of the art in both schema and ontology matching. Some innovations are in introducing new criteria which are based on (i) general properties of matching techniques, (ii) interpretation of input information, and (iii) the kind of input information. In particular, we distinguish between approximate and exact techniques at schema-level; and syntactic, semantic, and external techniques at element- and structure-level. Based on the classification proposed we overview some of the recent schema/ontology matching systems pointing which part of the solution space they cover. The proposed classification provides a common conceptual basis, and, hence, can be used for comparing different existing schema/ontology matching techniques and systems as well as for designing new ones, taking advantages of state of the art solutions.

/pdf/a-survey-of-schema-based-matching-approaches-1iseu9xjqp.pdf

A survey of schema-based matching approaches

We review the use on ontologies for the integration of heterogeneous information sources. Based on an in-depth evaluation of existing approaches to this problem we discuss how ontologies are used to support the integration task. We evaluate and compare the languages used to represent the ontologies and the use of mappings between ontologies as well as to connect ontologies with information sources. We also ask for ontology engineering methods and tools used to develop ontologies for information integration. Based on the results of our analysis we summarize the state of the art in ontology-based information integration and name areas of further research activities.

/pdf/ontology-based-integration-of-information-a-survey-of-4d6okm0dof.pdf

Ontology-Based Integration of Information — A Survey of Existing Approaches

Ontologies for geographic information processing

Intelligent and efficient information retrieval becomes increasingly important. Analogous to thesauri in the realm of spatial concepts, gazetteers offer a controlled vocabulary that can be used for spatial queries. Gazetteers use geographic footprints to link place names to geographic locations. Which geographic footprint representation is chosen has a strong impact on the quality of spatial queries. However, the footprint representations currently used in standard gazetteers such as points, lines, grid cell representations, and bounding boxes do not offer enough topological information to support refined spatial queries. We propose a new type of spatial footprint that can be described as a qualitative representation of the spatial decomposition of geographic entities. It holds enough topological and ordinal information enable refined spatial queries without being subject to the constraints of exact polygon representations. The proposed spatial representation was developed to be combined with terminological reasoning techniques used in systems for intelligent information integration.

Qualitative Spatial Representation for Information Retrieval by Gazetteers

Our main objective is to develop user-centric services to support better governance, democratic processes, and a sustainable and balanced development of rural and urban areas around the North Sea. A bike trip example offers a detailed look at the GeoShare Network's technologies, including ontologies for describing vocabularies and catalogs. We also discuss about the spatial data infrastructure consisting of a spatial reasoner, a Web catalog service, distributed Web map services, and a generic Web map client and about the ontology-based search for interactive digital maps.

Ontology-based search for interactive digital maps

Environmental information systems have gained more importance both in the public administration and industry since the beginning of 1990. For example, in the public administration every state in the Federal Republic of Germany has developed a type of environmental information system. National and European legislation demanding far reaching transparency in the state of the environment encouraged this development. In industry on the other hand, environmental information systems are used for costand product-specific recording of waste flows. These are used to point out weak points within the companies’ processes.

Thomas Vögele

Papers

Ontology-Based Integration of Information — A Survey of Existing Approaches

Ontologies for geographic information processing

Qualitative Spatial Representation for Information Retrieval by Gazetteers

Ontology-based search for interactive digital maps

Using Environmental Information Efficiently: Sharing Data and Knowledge from Heterogeneous Sources