GEO Label – quality information interrogation tool for geospatial datasets:towards effective visualization of quality metadata

Although the importance of dataset fitness-for-use evaluation and intercomparison is widely recognised within the GIS community, no practical tools have yet been developed to support such interrogation. GeoViQua aims to develop a GEO label which will visually summarise and allow interrogation of key informational aspects of geospatial datasets upon which users rely when selecting datasets for use. The proposed GEO label will be integrated in the Global Earth Observation System of Systems (GEOSS) and will be used as a value and trust indicator for datasets accessible through the GEO Portal. As envisioned, the GEO label will act as a decision support mechanism for dataset selection and thereby hopefully improve user recognition of the quality of datasets. To date we have conducted 3 user studies to (1) identify the informational aspects of geospatial datasets upon which users rely when assessing dataset quality and trustworthiness, (2) elicit initial user views on a GEO label and its potential role and (3), evaluate prototype label visualisations. Our first study revealed that, when evaluating quality of data, users consider 8 facets: dataset producer information; producer comments on dataset quality; dataset compliance with international standards; community advice; dataset ratings; links to dataset citations; expert value judgements; and quantitative quality information. Our second study confirmed the relevance of these facets in terms of the community-perceived function that a GEO label should fulfil: users and producers of geospatial data supported the concept of a GEO label that provides a drill-down interrogation facility covering all 8 informational aspects. Consequently, we developed three prototype label visualisations and evaluated their comparative effectiveness and user preference via a third user study to arrive at a final graphical GEO label representation. When integrated in the GEOSS, an individual GEO label will be provided for each dataset in the GEOSS clearinghouse (or other data portals and clearinghouses) based on its available quality information. Producer and feedback metadata documents are being used to dynamically assess information availability and generate the GEO labels. The producer metadata document can either be a standard ISO compliant metadata record supplied with the dataset, or an extended version of a GeoViQua-derived metadata record, and is used to assess the availability of a producer profile, producer comments, compliance with standards, citations and quantitative quality information. GeoViQua is also currently developing a feedback server to collect and encode (as metadata records) user and producer feedback on datasets; these metadata records will be used to assess the availability of user comments, ratings, expert reviews and user-supplied citations for a dataset. The GEO label will provide drill-down functionality which will allow a user to navigate to a GEO label page offering detailed quality information for its associated dataset. At this stage, we are developing the GEO label service that will be used to provide GEO labels on demand based on supplied metadata records. In this presentation, we will provide a comprehensive overview of the GEO label development process, with specific emphasis on the GEO label implementation and integration into the GEOSS.

Ontologies for crisis management:a review of state of the art in ontology design and usability

The growing use of a variety of information systems in crisis management both by non-governmental organizations (NGOs) and emergency management agencies makes the challenges of information sharing and interoperability increasingly important. The use of semantic web technologies is a growing area and is a technology stack specifically suited to these challenges. This paper presents a review of ontologies, vocabularies and taxonomies that are useful in crisis management systems. We identify the different subject areas relevant to crisis management based on a review of the literature. The different ontologies and vocabularies available are analysed in terms of their coverage, design and usability. We also consider the use cases for which they were designed and the degree to which they follow a variety of standards. While providing comprehensive ontologies for the crisis domain is not feasible or desirable there is considerable scope to develop ontologies for the subject areas not currently covered and for the purposes of interoperability.

Issues in learning an ontology from text

Ontology construction for any domain is a labour intensive and complex process. Any methodology that can reduce the cost and increase efficiency has the potential to make a major impact in the life sciences. This paper describes an experiment in ontology construction from text for the animal behaviour domain. Our objective was to see how much could be done in a simple and relatively rapid manner using a corpus of journal papers. We used a sequence of pre-existing text processing steps, and here describe the different choices made to clean the input, to derive a set of terms and to structure those terms in a number of hierarchies. We describe some of the challenges, especially that of focusing the ontology appropriately given a starting point of a heterogeneous corpus. Results - Using mainly automated techniques, we were able to construct an 18055 term ontology-like structure with 73% recall of animal behaviour terms, but a precision of only 26%. We were able to clean unwanted terms from the nascent ontology using lexico-syntactic patterns that tested the validity of term inclusion within the ontology. We used the same technique to test for subsumption relationships between the remaining terms to add structure to the initially broad and shallow structure we generated. All outputs are available at http://thirlmere.aston.ac.uk/~kiffer/animalbehaviour/ webcite. Conclusion - We present a systematic method for the initial steps of ontology or structured vocabulary construction for scientific domains that requires limited human effort and can make a contribution both to ontology learning and maintenance. The method is useful both for the exploration of a scientific domain and as a stepping stone towards formally rigourous ontologies. The filtering of recognised terms from a heterogeneous corpus to focus upon those that are the topic of the ontology is identified to be one of the main challenges for research in ontology learning.

Issues in learning an ontology from text

Ontology construction for any domain is a labour intensive and complex process. Any methodology that can reduce the cost and increase efficiency has the potential to make a major impact in the life sciences. This paper describes an experiment in ontology construction from text for the Animal Behaviour domain. Our objective was to see how much could be done in a simple and rapid manner using a corpus of journal papers. We used a sequence of text processing steps, and describe the different choices made to clean the input, to derive a set of terms and to structure those terms in a hierarchy. We were able in a very short space of time to construct a 17000 term ontology with a high percentage of suitable terms. We describe some of the challenges, especially that of focusing the ontology appropriately given a starting point of a heterogeneous corpus.

Defining an OWL ontology that could be used to integrate mental-health risk information within a decision support system

In the field of mental health risk assessment, there is no standardisation between the data used in different systems. As a first step towards the possible interchange of data between assessment tools, an ontology has been constructed for a particular one, GRiST (Galatean Risk Screening Tool). We briefly introduce GRiST and its data structures, then describe the ontology and the benefits that have already been realised from the construction process. For example, the ontology has been used to check the consistency of the various trees used in the model. We then consider potential uses in integration of data from other sources. © 2009 IEEE.

Semisupervised learning of hierarchical latent trait models for data visualization

Recently, we have developed the hierarchical Generative Topographic Mapping (HGTM), an interactive method for visualization of large high-dimensional real-valued data sets. In this paper, we propose a more general visualization system by extending HGTM in three ways, which allows the user to visualize a wider range of data sets and better support the model development process. 1) We integrate HGTM with noise models from the exponential family of distributions. The basic building block is the Latent Trait Model (LTM). This enables us to visualize data of inherently discrete nature, e.g., collections of documents, in a hierarchical manner. 2) We give the user a choice of initializing the child plots of the current plot in either interactive, or automatic mode. In the interactive mode, the user selects "regions of interest," whereas in the automatic mode, an unsupervised minimum message length (MML)-inspired construction of a mixture of LTMs is employed. The unsupervised construction is particularly useful when high-level plots are covered with dense clusters of highly overlapping data projections, making it difficult to use the interactive mode. Such a situation often arises when visualizing large data sets. 3) We derive general formulas for magnification factors in latent trait models. Magnification factors are a useful tool to improve our understanding of the visualization plots, since they can highlight the boundaries between data clusters. We illustrate our approach on a toy example and evaluate it on three more complex real data sets. © 2005 IEEE.

Theorizing accounting and other [calculative] practices in private equity:Experimenting with Schatzki’s ‘site’ ontology

This thesis contributes to social studies of finance and accounting (Vollmer, Mennicken, & Preda, 2009) and the practice theory literatures (Feldman & Orlikowski, 2011) by experimenting (Baxter & Chua, 2008) with concepts developed by Theodore Schatzki and demonstrating their relevance and usefulness in theorizing and explaining accounting and other organizational phenomena. Influenced by Schatzki, I have undertaken a sociological investigation of the practices, arrangements, and nexuses forming (part of) the social ‘site’ of private equity (PE). I have examined and explained the organization of practices within the PE industry. More specifically, I have sought to throw light on the practice organizations animating various PE practices. I have problematized a particular aspect of Schatzki’s practice organization framework: ‘general understanding’, which has so far been poorly understood and taken for granted in the accounting literature. I have tried to further explore the concept to clarify important definitional issues surrounding its empirical application. In investigating the forms of accounting and control practices in PE firms and how they link with other practices forming part of the ‘site’, I have sought to explain how the ‘situated functionality’ of accounting is ‘prefigured’ by its ‘dispersed’ nature. In doing so, this thesis addresses the recent calls for research on accounting and control practices within financial services firms. This thesis contributes to the social studies of finance and accounting literature also by opening the blackbox of investment [e]valuation practices prevalent in the PE industry. I theorize the due diligence of PE funds as a complex of linked calculative practices and bring to fore the important aspects of ‘practical intelligibility’ of the investment professionals undertaking investment evaluation. I also identify and differentiate the ‘causal’ and ‘prefigurational’ relations between investment evaluation practices and the material entities ‘constituting’ those practices. Moreover, I demonstrate the role of practice memory in those practices. Finally, the thesis also contributes to the practice theory literature by identifying and attempting to clarify and/or improve the poorly defined and/or underdeveloped concepts of Schatzki’s ‘site’ ontology framework.

Sensing presence (PreSense) ontology:user modelling in the semantic sensor web

Increasingly, people's digital identities are attached to, and expressed through, their mobile devices. At the same time digital sensors pervade smart environments in which people are immersed. This paper explores different perspectives in which users' modelling features can be expressed through the information obtained by their attached personal sensors. We introduce the PreSense Ontology, which is designed to assign meaning to sensors' observations in terms of user modelling features. We believe that the Sensing Presence ( PreSense ) Ontology is a first step toward the integration of user modelling and "smart environments". In order to motivate our work we present a scenario and demonstrate how the ontology could be applied in order to enable context-sensitive services. © 2012 Springer-Verlag.

Ontology-based pathways generation for biomass to bioenergy conversion

Bio energy is a renewable energy and a solution to the depleting fossil fuels. Bio energy such as heat, power and bio fuel is generated by conversion technologies using biomass for example domestic waste, root crops, forest residue and animal slurry. Pyrolysis, anaerobic digestion and combined heat and power engine are some examples of the technologies. Depending on the nature of a biomass, it can be treated with various technologies giving out some products, which can be further treated with other technologies and eventually converted into the final products as bio energy. The pathway followed by the biomass, technologies, intermediate products and bio energy in the conversion process is referred to as bio energy pathway. Identification of appropriate pathways optimizes the conversion process. Although there are various approaches to create or generate the pathways, there is still a need for a semantic approach to generate the pathways, which allow checking the consistency of the knowledge, and to share and extend the knowledge efficiently. This paper presents an ontology-based approach to automatic generation of the pathways for biomass to bio energy conversion, which exploits the definition and hierarchical structure of the biomass and technologies, their relationship and associated properties, and infers appropriate pathways. A case study has been carried out in a real-life scenario, the bio energy project for the North West of Europe (Bioen NW), which showed promising results.

Representing human expertise by the OWL web ontology language to support knowledge engineering in decision support systems

Clinical decision support systems (CDSSs) often base their knowledge and advice on human expertise. Knowledge representation needs to be in a format that can be easily understood by human users as well as supporting ongoing knowledge engineering, including evolution and consistency of knowledge. This paper reports on the development of an ontology specification for managing knowledge engineering in a CDSS for assessing and managing risks associated with mental-health problems. The Galatean Risk and Safety Tool, GRiST, represents mental-health expertise in the form of a psychological model of classification. The hierarchical structure was directly represented in the machine using an XML document. Functionality of the model and knowledge management were controlled using attributes in the XML nodes, with an accompanying paper manual for specifying how end-user tools should behave when interfacing with the XML. This paper explains the advantages of using the web-ontology language, OWL, as the specification, details some of the issues and problems encountered in translating the psychological model to OWL, and shows how OWL benefits knowledge engineering. The conclusions are that OWL can have an important role in managing complex knowledge domains for systems based on human expertise without impeding the end-users' understanding of the knowledge base. The generic classification model underpinning GRiST makes it applicable to many decision domains and the accompanying OWL specification facilitates its implementation.

Ontology for performance measurement indicators’ comparison

Indicators are widely used by organizations as a way of evaluating, measuring and classifying organizational performance. As part of performance evaluation systems, indicators are often shared or compared across internal sectors or with other organizations. However, indicators can be vague and imprecise, and also can lack semantics, making comparisons with other indicators difficult. Thus, this paper presents a knowledge model based on an ontology that may be used to represent indicators semantically and generically, dealing with the imprecision and vagueness, and thus facilitating better comparison. Semantic technologies are shown to be suitable for this solution, so that it could be able to represent complex data involved in indicators comparison.

Design software architecture models using ontology

Software architecture plays an essential role in the high level description of a system design, where the structure and communication are emphasized. Despite its importance in the software engineering process, the lack of formal description and automated verification hinders the development of good software architecture models. In this paper, we present an approach to support the rigorous design and verification of software architecture models using the semantic web technology. We view software architecture models as ontology representations, where their structures and communication constraints are captured by the Web Ontology Language (OWL) and the Semantic Web Rule Language (SWRL). Specific configurations on the design are represented as concrete instances of the ontology, to which their structures and dynamic behaviors must conform. Furthermore, ontology reasoning tools can be applied to perform various automated verification on the design to ensure correctness, such as consistency checking, style recognition, and behavioral inference.

Ontology forecasting in scientific literature:semantic concepts prediction based on innovation-adoption priors

The ontology engineering research community has focused for many years on supporting the creation, development and evolution of ontologies. Ontology forecasting, which aims at predicting semantic changes in an ontology, represents instead a new challenge. In this paper, we want to give a contribution to this novel endeavour by focusing on the task of forecasting semantic concepts in the research domain. Indeed, ontologies representing scientific disciplines contain only research topics that are already popular enough to be selected by human experts or automatic algorithms. They are thus unfit to support tasks which require the ability of describing and exploring the forefront of research, such as trend detection and horizon scanning. We address this issue by introducing the Semantic Innovation Forecast (SIF) model, which predicts new concepts of an ontology at time t + 1, using only data available at time t. Our approach relies on lexical innovation and adoption information extracted from historical data. We evaluated the SIF model on a very large dataset consisting of over one million scientific papers belonging to the Computer Science domain: the outcomes show that the proposed approach offers a competitive boost in mean average precision-at-ten compared to the baselines when forecasting over 5 years.

Jointly event extraction and visualization on Twitter via probabilistic modelling

Event extraction from texts aims to detect structured information such as what has happened, to whom, where and when. Event extraction and visualization are typically considered as two different tasks. In this paper, we propose a novel approach based on probabilistic modelling to jointly extract and visualize events from tweets where both tasks benefit from each other. We model each event as a joint distribution over named entities, a date, a location and event-related keywords. Moreover, both tweets and event instances are associated with coordinates in the visualization space. The manifold assumption that the intrinsic geometry of tweets is a low-rank, non-linear manifold within the high-dimensional space is incorporated into the learning framework using a regularization. Experimental results show that the proposed approach can effectively deal with both event extraction and visualization and performs remarkably better than both the state-of-the-art event extraction method and a pipeline approach for event extraction and visualization.