Semantic network analysis of ontologies

A key argument for modeling knowledge in ontologies is the easy re-use and re-engineering of the knowledge. However, beside consistency checking, current ontology engineering tools provide only basic functionalities for analyzing ontologies. Since ontologies can be considered as (labeled, directed) graphs, graph analysis techniques are a suitable answer for this need. Graph analysis has been performed by sociologists for over 60 years, and resulted in the vivid research area of Social Network Analysis (SNA). While social network structures in general currently receive high attention in the Semantic Web community, there are only very few SNA applications up to now, and virtually none for analyzing the structure of ontologies. We illustrate in this paper the benefits of applying SNA to ontologies and the Semantic Web, and discuss which research topics arise on the edge between the two areas. In particular, we discuss how different notions of centrality describe the core content and structure of an ontology. From the rather simple notion of degree centrality over betweenness centrality to the more complex eigenvector centrality based on Hermitian matrices, we illustrate the insights these measures provide on two ontologies, which are different in purpose, scope, and size.

Digital image analysis as a tool to estimate legume contributions in legume-grass swards

Summary: Productivity and forage quality of legume-grass swards are important factors for successful arable farming in both organic and conventional farming systems. For these objectives the botanical composition of the swards is of particular importance, especially, the content of legumes due to their ability to fix airborne nitrogen. As it can vary considerably within a field, a non-destructive detection method while doing other tasks would facilitate a more targeted sward management and could predict the nitrogen supply of the soil for the subsequent crop. This study was undertaken to explore the potential of digital image analysis (DIA) for a non destructive prediction of legume dry matter (DM) contribution of legume-grass mixtures. For this purpose an experiment was conducted in a greenhouse, comprising a sample size of 64 experimental swards such as pure swards of red clover (Trifolium pratense L.), white clover (Trifolium repens L.) and lucerne (Medicago sativa L.) as well as binary mixtures of each legume with perennial ryegrass (Lolium perenne L.). Growth stages ranged from tillering to heading and the proportion of legumes from 0 to 80 %. Based on digital sward images three steps were considered in order to estimate the legume contribution (% of DM): i) The development of a digital image analysis (DIA) procedure in order to estimate legume coverage (% of area). ii) The description of the relationship between legume coverage (% area) and legume contribution (% of DM) derived from digital analysis of legume coverage related to the green area in a digital image. iii) The estimation of the legume DM contribution with the findings of i) and ii). i) In order to evaluate the most suitable approach for the estimation of legume coverage by means of DIA different tools were tested. Morphological operators such as erode and dilate support the differentiation of objects of different shape by shrinking and dilating objects (Soille, 1999). When applied to digital images of legume-grass mixtures thin grass leaves were removed whereas rounder clover leaves were left. After this process legume leaves were identified by threshold segmentation. The segmentation of greyscale images turned out to be not applicable since the segmentation between legumes and bare soil failed. The advanced procedure comprising morphological operators and HSL colour information could determine bare soil areas in young and open swards very accurately. Also legume specific HSL thresholds allowed for precise estimations of legume coverage across a wide range from 11.8 - 72.4 %. Based on this legume specific DIA procedure estimated legume coverage showed good correlations with the measured values across the whole range of sward ages (R2 0.96, SE 4.7 %). A wide range of form parameters (i.e. size, breadth, rectangularity, and circularity of areas) was tested across all sward types, but none did improve prediction accuracy of legume coverage significantly. ii) Using measured reference data of legume coverage and contribution, in a first approach a common relationship based on all three legumes and sward ages of 35, 49 and 63 days was found with R2 0.90. This relationship was improved by a legume-specific approach of only 49- and 63-d old swards (R2 0.94, 0.96 and 0.97 for red clover, white clover, and lucerne, respectively) since differing structural attributes of the legume species influence the relationship between these two parameters. In a second approach biomass was included in the model in order to allow for different structures of swards of different ages. Hence, a model was developed, providing a close look on the relationship between legume coverage in binary legume-ryegrass communities and the legume contribution: At the same level of legume coverage, legume contribution decreased with increased total biomass. This phenomenon may be caused by more non-leguminous biomass covered by legume leaves at high levels of total biomass. Additionally, values of legume contribution and coverage were transformed to the logit-scale in order to avoid problems with heteroscedasticity and negative predictions. The resulting relationships between the measured legume contribution and the calculated legume contribution indicated a high model accuracy for all legume species (R2 0.93, 0.97, 0.98 with SE 4.81, 3.22, 3.07 % of DM for red clover, white clover, and lucerne swards, respectively). The validation of the model by using digital images collected over field grown swards with biomass ranges considering the scope of the model shows, that the model is able to predict legume contribution for most common legume-grass swards (Frame, 1992; Ledgard and Steele, 1992; Loges, 1998). iii) An advanced procedure for the determination of legume DM contribution by DIA is suggested, which comprises the inclusion of morphological operators and HSL colour information in the analysis of images and which applies an advanced function to predict legume DM contribution from legume coverage by considering total sward biomass. Low residuals between measured and calculated values of legume dry matter contribution were found for the separate legume species (R2 0.90, 0.94, 0.93 with SE 5.89, 4.31, 5.52 % of DM for red clover, white clover, and lucerne swards, respectively). The introduced DIA procedure provides a rapid and precise estimation of legume DM contribution for different legume species across a wide range of sward ages. Further research is needed in order to adapt the procedure to field scale, dealing with differing light effects and potentially higher swards. The integration of total biomass into the model for determining legume contribution does not necessarily reduce its applicability in practice as a combined estimation of total biomass and legume coverage by field spectroscopy (Biewer et al. 2009) and DIA, respectively, may allow for an accurate prediction of the legume contribution in legume-grass mixtures.

The Architect's Collaborator: Toward Intelligent Tools for Conceptual Design

In early stages of architectural design, as in other design domains, the language used is often very abstract. In architectural design, for example, architects and their clients use experiential terms such as "private" or "open" to describe spaces. If we are to build programs that can help designers during this early-stage design, we must give those programs the capability to deal with concepts on the level of such abstractions. The work reported in this thesis sought to do that, focusing on two key questions: How are abstract terms such as "private" and "open" translated into physical form? How might one build a tool to assist designers with this process? The Architect's Collaborator (TAC) was built to explore these issues. It is a design assistant that supports iterative design refinement, and that represents and reasons about how experiential qualities are manifested in physical form. Given a starting design and a set of design goals, TAC explores the space of possible designs in search of solutions that satisfy the goals. It employs a strategy we've called dependency-directed redesign: it evaluates a design with respect to a set of goals, then uses an explanation of the evaluation to guide proposal and refinement of repair suggestions; it then carries out the repair suggestions to create new designs. A series of experiments was run to study TAC's behavior. Issues of control structure, goal set size, goal order, and modification operator capabilities were explored. In addition, TAC's use as a design assistant was studied in an experiment using a house in the process of being redesigned. TAC's use as an analysis tool was studied in an experiment using Frank Lloyd Wright's Prairie houses.

Value Stream Analysis and Mapping for Product Development

This paper explores the concept of Value Stream Analysis and Mapping (VSA/M) as applied to Product Development (PD) efforts. Value Stream Analysis and Mapping is a method of business process improvement. The application of VSA/M began in the manufacturing community. PD efforts provide a different setting for the use of VSA/M. Site visits were made to nine major U.S. aerospace organizations. Interviews, discussions, and participatory events were used to gather data on (1) the sophistication of the tools used in PD process improvement efforts, (2) the lean context of the use of the tools, and (3) success of the efforts. It was found that all three factors were strongly correlated, suggesting success depends on both good tools and lean context. Finally, a general VSA/M method for PD activities is proposed. The method uses modified process mapping tools to analyze and improve process.

Value Stream Analysis and Mapping for Product Development

This paper explores the concept of Value Stream Analysis and Mapping (VSA/M) as applied to Product Development (PD) efforts. Value Stream Analysis and Mapping is a method of business process improvement. The application of VSA/M began in the manufacturing community. PD efforts provide a different setting for the use of VSA/M. Site visits were made to nine major U.S. aerospace organizations. Interviews, discussions, and participatory events were used to gather data on (1) the sophistication of the tools used in PD process improvement efforts, (2) the lean context of the use of the tools, and (3) success of the efforts. It was found that all three factors were strongly correlated, suggesting success depends on both good tools and lean context. Finally, a general VSA/M method for PD activities is proposed. The method uses modified process mapping tools to analyze and improve process.

A compositional data analysis package for R providing multiple approaches

”compositions” is a new R-package for the analysis of compositional and positive data. It contains four classes corresponding to the four different types of compositional and positive geometry (including the Aitchison geometry). It provides means for computation, plotting and high-level multivariate statistical analysis in all four geometries. These geometries are treated in an fully analogous way, based on the principle of working in coordinates, and the object-oriented programming paradigm of R. In this way, called functions automatically select the most appropriate type of analysis as a function of the geometry. The graphical capabilities include ternary diagrams and tetrahedrons, various compositional plots (boxplots, barplots, piecharts) and extensive graphical tools for principal components. Afterwards, ortion and proportion lines, straight lines and ellipses in all geometries can be added to plots. The package is accompanied by a hands-on-introduction, documentation for every function, demos of the graphical capabilities and plenty of usage examples. It allows direct and parallel computation in all four vector spaces and provides the beginner with a copy-and-paste style of data analysis, while letting advanced users keep the functionality and customizability they demand of R, as well as all necessary tools to add own analysis routines. A complete example is included in the appendix

Exploration of geological variability and possible processes through the use of compositional data analysis: an example using scottish metamorphosed

Developments in the statistical analysis of compositional data over the last two decades have made possible a much deeper exploration of the nature of variability, and the possible processes associated with compositional data sets from many disciplines. In this paper we concentrate on geochemical data sets. First we explain how hypotheses of compositional variability may be formulated within the natural sample space, the unit simplex, including useful hypotheses of subcompositional discrimination and specific perturbational change. Then we develop through standard methodology, such as generalised likelihood ratio tests, statistical tools to allow the systematic investigation of a complete lattice of such hypotheses. Some of these tests are simple adaptations of existing multivariate tests but others require special construction. We comment on the use of graphical methods in compositional data analysis and on the ordination of specimens. The recent development of the concept of compositional processes is then explained together with the necessary tools for a staying- in-the-simplex approach, namely compositional singular value decompositions. All these statistical techniques are illustrated for a substantial compositional data set, consisting of 209 major-oxide and rare-element compositions of metamorphosed limestones from the Northeast and Central Highlands of Scotland. Finally we point out a number of unresolved problems in the statistical analysis of compositional processes

The allometry of non-employment. What can compositional data analysis tell us about labour market performance across the UK's regions?

The low levels of unemployment recorded in the UK in recent years are widely cited as evidence of the country’s improved economic performance, and the apparent convergence of unemployment rates across the country’s regions used to suggest that the longstanding divide in living standards between the relatively prosperous ‘south’ and the more depressed ‘north’ has been substantially narrowed. Dissenters from these conclusions have drawn attention to the greatly increased extent of non-employment (around a quarter of the UK’s working age population are not in employment) and the marked regional dimension in its distribution across the country. Amongst these dissenters it is generally agreed that non-employment is concentrated amongst older males previously employed in the now very much smaller ‘heavy’ industries (e.g. coal, steel, shipbuilding). This paper uses the tools of compositiona l data analysis to provide a much richer picture of non-employment and one which challenges the conventional analysis wisdom about UK labour market performance as well as the dissenters view of the nature of the problem. It is shown that, associated with the striking ‘north/south’ divide in nonemployment rates, there is a statistically significant relationship between the size of the non-employment rate and the composition of non-employment. Specifically, it is shown that the share of unemployment in non-employment is negatively correlated with the overall non-employment rate: in regions where the non-employment rate is high the share of unemployment is relatively low. So the unemployment rate is not a very reliable indicator of regional disparities in labour market performance. Even more importantly from a policy viewpoint, a significant positive relationship is found between the size of the non-employment rate and the share of those not employed through reason of sickness or disability and it seems (contrary to the dissenters) that this connection is just as strong for women as it is for men

Compositional ideas in the bayesian analysis of categorical data with application to dose finding clinical trials

Compositional random vectors are fundamental tools in the Bayesian analysis of categorical data. Many of the issues that are discussed with reference to the statistical analysis of compositional data have a natural counterpart in the construction of a Bayesian statistical model for categorical data. This note builds on the idea of cross-fertilization of the two areas recommended by Aitchison (1986) in his seminal book on compositional data. Particular emphasis is put on the problem of what parameterization to use

A compositional statistical analysis of capital stock

Most of economic literature has presented its analysis under the assumption of homogeneous capital stock. However, capital composition differs across countries. What has been the pattern of capital composition associated with World economies? We make an exploratory statistical analysis based on compositional data transformed by Aitchinson logratio transformations and we use tools for visualizing and measuring statistical estimators of association among the components. The goal is to detect distinctive patterns in the composition. As initial findings could be cited that: 1. Sectorial components behaved in a correlated way, building industries on one side and , in a less clear view, equipment industries on the other. 2. Full sample estimation shows a negative correlation between durable goods component and other buildings component and between transportation and building industries components. 3. Countries with zeros in some components are mainly low income countries at the bottom of the income category and behaved in a extreme way distorting main results observed in the full sample. 4. After removing these extreme cases, conclusions seem not very sensitive to the presence of another isolated cases

Bayesian tools for zero counts in compositional data

The log-ratio methodology makes available powerful tools for analyzing compositional data. Nevertheless, the use of this methodology is only possible for those data sets without null values. Consequently, in those data sets where the zeros are present, a previous treatment becomes necessary. Last advances in the treatment of compositional zeros have been centered especially in the zeros of structural nature and in the rounded zeros. These tools do not contemplate the particular case of count compositional data sets with null values. In this work we deal with \count zeros" and we introduce a treatment based on a mixed Bayesian-multiplicative estimation. We use the Dirichlet probability distribution as a prior and we estimate the posterior probabilities. Then we apply a multiplicative modi¯cation for the non-zero values. We present a case study where this new methodology is applied. Key words: count data, multiplicative replacement, composition, log-ratio analysis

Application of compositional data analysis to geochemical data of marine sediments

In an earlier investigation (Burger et al., 2000) five sediment cores near the Rodrigues Triple Junction in the Indian Ocean were studied applying classical statistical methods (fuzzy c-means clustering, linear mixing model, principal component analysis) for the extraction of endmembers and evaluating the spatial and temporal variation of geochemical signals. Three main factors of sedimentation were expected by the marine geologists: a volcano-genetic, a hydro-hydrothermal and an ultra-basic factor. The display of fuzzy membership values and/or factor scores versus depth provided consistent results for two factors only; the ultra-basic component could not be identified. The reason for this may be that only traditional statistical methods were applied, i.e. the untransformed components were used and the cosine-theta coefficient as similarity measure. During the last decade considerable progress in compositional data analysis was made and many case studies were published using new tools for exploratory analysis of these data. Therefore it makes sense to check if the application of suitable data transformations, reduction of the D-part simplex to two or three factors and visual interpretation of the factor scores would lead to a revision of earlier results and to answers to open questions . In this paper we follow the lines of a paper of R. Tolosana- Delgado et al. (2005) starting with a problem-oriented interpretation of the biplot scattergram, extracting compositional factors, ilr-transformation of the components and visualization of the factor scores in a spatial context: The compositional factors will be plotted versus depth (time) of the core samples in order to facilitate the identification of the expected sources of the sedimentary process. Kew words: compositional data analysis, biplot, deep sea sediments

Integration of knowledge-based, qualitative and numeric tools for real time dynamic systems supervision

The proposal presented in this thesis is to provide designers of knowledge based supervisory systems of dynamic systems with a framework to facilitate their tasks avoiding interface problems among tools, data flow and management. The approach is thought to be useful to both control and process engineers in assisting their tasks. The use of AI technologies to diagnose and perform control loops and, of course, assist process supervisory tasks such as fault detection and diagnose, are in the scope of this work. Special effort has been put in integration of tools for assisting expert supervisory systems design. With this aim the experience of Computer Aided Control Systems Design (CACSD) frameworks have been analysed and used to design a Computer Aided Supervisory Systems (CASSD) framework. In this sense, some basic facilities are required to be available in this proposed framework: ·

Sensitivity analysis of a catchment-scale nitrogen model

There are now considerable expectations that semi-distributed models are useful tools for supporting catchment water quality management. However, insufficient attention has been given to evaluating the uncertainties inherent to this type of model, especially those associated with the spatial disaggregation of the catchment. The Integrated Nitrogen in Catchments model (INCA) is subjected to an extensive regionalised sensitivity analysis in application to the River Kennet, part of the groundwater-dominated upper Thames catchment, UK The main results are: (1) model output was generally insensitive to land-phase parameters, very sensitive to groundwater parameters, including initial conditions, and significantly sensitive to in-river parameters; (2) INCA was able to produce good fits simultaneously to the available flow, nitrate and ammonium in-river data sets; (3) representing parameters as heterogeneous over the catchment (206 calibrated parameters) rather than homogeneous (24 calibrated parameters) produced a significant improvement in fit to nitrate but no significant improvement to flow and caused a deterioration in ammonium performance; (4) the analysis indicated that calibrating the flow-related parameters first, then calibrating the remaining parameters (as opposed to calibrating all parameters together) was not a sensible strategy in this case; (5) even the parameters to which the model output was most sensitive suffered from high uncertainty due to spatial inconsistencies in the estimated optimum values, parameter equifinality and the sampling error associated with the calibration method; (6) soil and groundwater nutrient and flow data are needed to reduce. uncertainty in initial conditions, residence times and nitrogen transformation parameters, and long-term historic data are needed so that key responses to changes in land-use management can be assimilated. The results indicate the general, difficulty of reconciling the questions which catchment nutrient models are expected to answer with typically limited data sets and limited knowledge about suitable model structures. The results demonstrate the importance of analysing semi-distributed model uncertainties prior to model application, and illustrate the value and limitations of using Monte Carlo-based methods for doing so. (c) 2005 Elsevier B.V. All rights reserved.

Making 'dirty' nations look clean? The nation state and the problem of selecting and weighting indices as tools for measuring progress towards sustainability

Pressing global environmental problems highlight the need to develop tools to measure progress towards "sustainability." However, some argue that any such attempt inevitably reflects the views of those creating such tools and only produce highly contested notions of "reality." To explore this tension, we critically assesses the Environmental Sustainability Index (ESI), a well-publicized product of the World Economic Forum that is designed to measure 'sustainability' by ranking nations on league tables based on extensive databases of environmental indicators. By recreating this index, and then using statistical tools (principal components analysis) to test relations between various components of the index, we challenge ways in which countries are ranked in the ESI. Based on this analysis, we suggest (1) that the approach taken to aggregate, interpret and present the ESI creates a misleading impression that Western countries are more sustainable than the developing world; (2) that unaccounted methodological biases allowed the authors of the ESI to over-generalize the relative 'sustainability' of different countries; and, (3) that this has resulted in simplistic conclusions on the relation between economic growth and environmental sustainability. This criticism should not be interpreted as a call for the abandonment of efforts to create standardized comparable data. Instead, this paper proposes that indicator selection and data collection should draw on a range of voices, including local stakeholders as well as international experts. We also propose that aggregating data into final league ranking tables is too prone to error and creates the illusion of absolute and categorical interpretations. (c) 2004 Elsevier Ltd. All rights reserved.