Ubbo Visser

Ontologies for geographic information processing

The development of geographical information systems (GIS) and the interoperability between these systems demands new requirements for the description of the underlying data. The exchange of data between GIS systems is problematic and often fails due to confusion in the meaning of concepts. The term semantic translator, a translator between GIS systems and/or catalogue systems which gives the user the option to map data between the systems is a topic of current research. This paper provides an overview of formal ontologies and how they can be used for geographical information processing. A description of an intelligent broker architecture for semantic-based information retrieval is introduced, and shows how this approach can be used for general purposes. In conclusion we attempt to provide a roadmap for the use of ontologies for geographic information processing.

Intelligent information integration for the semantic web

and Related Work.- Related Work.- The Buster Approach for Terminological, Spatial, and Temporal Representation and Reasoning.- General Approach of Buster.- Terminological Representation and Reasoning, Semantic Translation.- Spatial Representation and Reasoning.- Temporal Representation and Reasoning.- Implementation, Conclusion, and Future Work.- Implementation Issues and System Demonstration.- Conclusion and Future Work.- References.

BioAssay Ontology (BAO): a semantic description of bioassays and high-throughput screening results

BackgroundHigh-throughput screening (HTS) is one of the main strategies to identify novel entry points for the development of small molecule chemical probes and drugs and is now commonly accessible to public sector research. Large amounts of data generated in HTS campaigns are submitted to public repositories such as PubChem, which is growing at an exponential rate. The diversity and quantity of available HTS assays and screening results pose enormous challenges to organizing, standardizing, integrating, and analyzing the datasets and thus to maximize the scientific and ultimately the public health impact of the huge investments made to implement public sector HTS capabilities. Novel approaches to organize, standardize and access HTS data are required to address these challenges.ResultsWe developed the first ontology to describe HTS experiments and screening results using expressive description logic. The BioAssay Ontology (BAO) serves as a foundation for the standardization of HTS assays and data and as a semantic knowledge model. In this paper we show important examples of formalizing HTS domain knowledge and we point out the advantages of this approach. The ontology is available online at the NCBO bioportal http://bioportal.bioontology.org/ontologies/44531.ConclusionsAfter a large manual curation effort, we loaded BAO-mapped data triples into a RDF database store and used a reasoner in several case studies to demonstrate the benefits of formalized domain knowledge representation in BAO. The examples illustrate semantic querying capabilities where BAO enables the retrieval of inferred search results that are relevant to a given query, but are not explicitly defined. BAO thus opens new functionality for annotating, querying, and analyzing HTS datasets and the potential for discovering new knowledge by means of inference.

Recognition and Prediction of Motion Situations Based on a Qualitative Motion Description

High-level online methods become more and more attractive with the increasing abilities of players and teams in the simulation league. As in real soccer, the recognition and prediction of strategies (e.g. opponent’s formation), tactics (e.g. wing play, offside traps), and situations (e.g. passing behavior) is important. In 2001, we proposed an approach where spatio-temporal relations between objects are described and interpreted in order to detect some of the above mentioned situations. In this paper we propose an extension of this approach that enables us to both interpret and predict complex situations. It is based on a qualitative description of motion scenes and additional background knowledge. The method is applicable to a variety of situations. Our experiment consists of numerous offside situations in simulation league games. We discuss the results in detail and conclude that this approach is valuable for future use because it is (a) possible to use the method in real-time, (b) we can predict situations giving us the option to refine agents actions in a game, and (c) it is domain independent in general.

Qualitative Spatial Representation for Information Retrieval by Gazetteers

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.

Evolving BioAssay Ontology (BAO): modularization, integration and applications

The lack of established standards to describe and annotate biological assays and screening outcomes in the domain of drug and chemical probe discovery is a severe limitation to utilize public and proprietary drug screening data to their maximum potential. We have created the BioAssay Ontology (BAO) project (http://bioassayontology.org) to develop common reference metadata terms and definitions required for describing relevant information of low-and high-throughput drug and probe screening assays and results. The main objectives of BAO are to enable effective integration, aggregation, retrieval, and analyses of drug screening data. Since we first released BAO on the BioPortal in 2010 we have considerably expanded and enhanced BAO and we have applied the ontology in several internal and external collaborative projects, for example the BioAssay Research Database (BARD). We describe the evolution of BAO with a design that enables modeling complex assays including profile and panel assays such as those in the Library of Integrated Network-based Cellular Signatures (LINCS). One of the critical questions in evolving BAO is the following: how can we provide a way to efficiently reuse and share among various research projects specific parts of our ontologies without violating the integrity of the ontology and without creating redundancies. This paper provides a comprehensive answer to this question with a description of a methodology for ontology modularization using a layered architecture. Our modularization approach defines several distinct BAO components and separates internal from external modules and domain-level from structural components. This approach facilitates the generation/extraction of derived ontologies (or perspectives) that can suit particular use cases or software applications. We describe the evolution of BAO related to its formal structures, engineering approaches, and content to enable modeling of complex assays and integration with other ontologies and datasets.

Formalization, Annotation and Analysis of Diverse Drug and Probe Screening Assay Datasets Using the BioAssay Ontology (BAO)

Huge amounts of high-throughput screening (HTS) data for probe and drug development projects are being generated in the pharmaceutical industry and more recently in the public sector. The resulting experimental datasets are increasingly being disseminated via publically accessible repositories. However, existing repositories lack sufficient metadata to describe the experiments and are often difficult to navigate by non-experts. The lack of standardized descriptions and semantics of biological assays and screening results hinder targeted data retrieval, integration, aggregation, and analyses across different HTS datasets, for example to infer mechanisms of action of small molecule perturbagens. To address these limitations, we created the BioAssay Ontology (BAO). BAO has been developed with a focus on data integration and analysis enabling the classification of assays and screening results by concepts that relate to format, assay design, technology, target, and endpoint. Previously, we reported on the higher-level design of BAO and on the semantic querying capabilities offered by the ontology-indexed triple store of HTS data. Here, we report on our detailed design, annotation pipeline, substantially enlarged annotation knowledgebase, and analysis results. We used BAO to annotate assays from the largest public HTS data repository, PubChem, and demonstrate its utility to categorize and analyze diverse HTS results from numerous experiments. BAO is publically available from the NCBO BioPortal at http://bioportal.bioontology.org/ontologies/1533. BAO provides controlled terminology and uniform scope to report probe and drug discovery screening assays and results. BAO leverages description logic to formalize the domain knowledge and facilitate the semantic integration with diverse other resources. As a consequence, BAO offers the potential to infer new knowledge from a corpus of assay results, for example molecular mechanisms of action of perturbagens.

Ontology-based search for interactive digital maps

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 Networks 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.

Towards a league-independent qualitative soccer theory for robocup

The paper discusses a top-down approach to model soccer knowledge, as it can be found in soccer theory books. The goal is to model soccer strategies and tactics in a way that they are usable for multiple RoboCup soccer leagues, i.e. for different hardware platforms. We investigate if and how soccer theory can be formalized such that specification and execution is possible. The advantage is clear: theory abstracts from hardware and from specific situations in leagues. We introduce basic primitives compliant with the terminology known in soccer theory, discuss an example on an abstract level and formalize it. We then consider aspects of different RoboCup leagues in a case study and examine how examples can be instantiated in three different leagues.

Recognizing Formations in Opponent Teams

The online coach within the simulation league has become more powerful over the last few years. Therefore, new options with regard to the recognition of the opponents strategy are possible. For example, the online coach is the only player who gets the information of all the objects on the field. This leads to the idea determine the opponents play system by the online coach and then choose an effective counter-strategy. This has been done with the help of an artificial neural network and will be discussed in this paper. All soccer-clients are initialized with a specific behavior and can change their behavior to an appropriate mode depending on the coachs commands. The result is a flexible and effective game played by the eleven soccer-clients.