Extending the Foundational Model of Anatomy with Automatically Acquired Spatial Relations

Formal ontologies have made significant impact in bioscience over the last ten years. Among them, the Foundational Model of Anatomy Ontology (FMA) is the most comprehensive model for the spatio-structural representation of human anatomy. In the research project MEDICO we use the FMA as our main source of background knowledge about human anatomy. Our ultimate goals are to use spatial knowledge from the FMA (1) to improve automatic parsing algorithms for 3D volume data sets generated by Computed Tomography and Magnetic Resonance Imaging and (2) to generate semantic annotations using the concepts from the FMA to allow semantic search on medical image repositories. We argue that in this context more spatial relation instances are needed than those currently available in the FMA. In this publication we present a technique for the automatic inductive acquisition of spatial relation instances by generalizing from expert-annotated volume datasets

A Boxology of Design Patterns for Hybrid Learning and Reasoning Systems

We propose a set of compositional design patterns to describe a large variety of systems that combine statistical techniques from machine learning with symbolic techniques from knowledge representation. As in other areas of computer science (knowledge engineering, software engineering, ontology engineering, process mining and others), such design patterns help to systematize the literature, clarify which combinations of techniques serve which purposes, and encourage re-use of software components. We have validated our set of compositional design patterns against a large body of recent literature.Comment: 12 pages,55 reference

The Use of Geographical Information Systems in the Urban Communes of Łódź Metropolitan Area

The article presents the results of pilot studies carried out in the cities of Łódź Metropolitan Area. The study concerned the use of geographical information system by the employees of offices of these cities. The interest in the subject of GIS in Polish urban communes results from the assumptions of the EU INSPIRE Directive and the necessity of its implementation in the basic units of territorial division of Poland. The aim of the research was the analysis of the impact of GIS on the sphere of the public participation in the process of local management and the possible use of GIS in the decision making in communes. Research showed what kind of software was used in analyzed communes

Control, Process Facilitation, and Requirements Change in Offshore Requirements Analysis: The Provider Perspective

Process, technology, and project factors have been increasingly driving organizations to offshore early software development phases, such as requirements analysis. This emerging trend necessitates greater control and process facilitation between client and vendor sites. The effectiveness of control and facilitation has, however, not been examined within the context of requirements analysis and change. In this study, we examine the role of control and facilitation in managing changing requirements and on success of requirements gathering in the Indian offshore software development environment. Firms found that control by client-site coordinators had a positive impact on requirements analysis success while vender site-coordinators did not have similar influence. Process facilitation by client site-coordinators affected requirements phase success indirectly through control. The study concludes with recommendations for research and practice

Knowledge Base Population using Semantic Label Propagation

A crucial aspect of a knowledge base population system that extracts new facts from text corpora, is the generation of training data for its relation extractors. In this paper, we present a method that maximizes the effectiveness of newly trained relation extractors at a minimal annotation cost. Manual labeling can be significantly reduced by Distant Supervision, which is a method to construct training data automatically by aligning a large text corpus with an existing knowledge base of known facts. For example, all sentences mentioning both 'Barack Obama' and 'US' may serve as positive training instances for the relation born_in(subject,object). However, distant supervision typically results in a highly noisy training set: many training sentences do not really express the intended relation. We propose to combine distant supervision with minimal manual supervision in a technique called feature labeling, to eliminate noise from the large and noisy initial training set, resulting in a significant increase of precision. We further improve on this approach by introducing the Semantic Label Propagation method, which uses the similarity between low-dimensional representations of candidate training instances, to extend the training set in order to increase recall while maintaining high precision. Our proposed strategy for generating training data is studied and evaluated on an established test collection designed for knowledge base population tasks. The experimental results show that the Semantic Label Propagation strategy leads to substantial performance gains when compared to existing approaches, while requiring an almost negligible manual annotation effort.Comment: Submitted to Knowledge Based Systems, special issue on Knowledge Bases for Natural Language Processin