Ontology ranking based on the analysis of concept structures

In view of the need to provide tools to facilitate the re-use of existing knowledge structures such as ontologies, we present in this paper a system, AKTiveRank, for the ranking of ontologies. AKTiveRank uses as input the search terms provided by a knowledge engineer and, using the output of an ontology search engine, ranks the ontologies. We apply a number of metrics in an attempt to investigate their appropriateness for ranking ontologies, and compare the results with a questionnaire-based human study. Our results show that AKTiveRank will have great utility although there is potential for improvement.

Data driven ontology evaluation

The evaluation of ontologies is vital for the growth of the Semantic Web. We consider a number of problems in evaluating a knowledge artifact like an ontology. We propose in this paper that one approach to ontology evaluation should be corpus or data driven. A corpus is the most accessible form of knowledge and its use allows a measure to be derived of the ‘fit’ between an ontology and a domain of knowledge. We consider a number of methods for measuring this ‘fit’ and propose a measure to evaluate structural fit, and a probabilistic approach to identifying the best ontology.

Background and foreground knowledge in dynamic ontology construction

Ontologies have become a key component in the Semantic Web and Knowledge management. One accepted goal is to construct ontologies from a domain specific set of texts. An ontology reflects the background knowledge used in writing and reading a text. However, a text is an act of knowledge maintenance, in that it re-enforces the background assumptions, alters links and associations in the ontology, and adds new concepts. This means that background knowledge is rarely expressed in a machine interpretable manner. When it is, it is usually in the conceptual boundaries of the domain, e.g. in textbooks or when ideas are borrowed into other domains. We argue that a partial solution to this lies in searching external resources such as specialized glossaries and the internet. We show that a random selection of concept pairs from the Gene Ontology do not occur in a relevant corpus of texts from the journal Nature. In contrast, a significant proportion can be found on the internet. Thus, we conclude that sources external to the domain corpus are necessary for the automatic construction of ontologies.

User-centred ontology learning for knowledge management

Automatic ontology building is a vital issue in many fields where they are currently built manually. This paper presents a user-centred methodology for ontology construction based on the use of Machine Learning and Natural Language Processing. In our approach, the user selects a corpus of texts and sketches a preliminary ontology (or selects an existing one) for a domain with a preliminary vocabulary associated to the elements in the ontology (lexicalisations). Examples of sentences involving such lexicalisation (e.g. ISA relation) in the corpus are automatically retrieved by the system. Retrieved examples are validated by the user and used by an adaptive Information Extraction system to generate patterns that discover other lexicalisations of the same objects in the ontology, possibly identifying new concepts or relations. New instances are added to the existing ontology or used to tune it. This process is repeated until a satisfactory ontology is obtained. The methodology largely automates the ontology construction process and the output is an ontology with an associated trained leaner to be used for further ontology modifications.

The ontology: Chimaera or Pegasus

In the context of the needs of the Semantic Web and Knowledge Management, we consider what the requirements are of ontologies. The ontology as an artifact of knowledge representation is in danger of becoming a Chimera. We present a series of facts concerning the foundations on which automated ontology construction must build. We discuss a number of different functions that an ontology seeks to fulfill, and also a wish list of ideal functions. Our objective is to stimulate discussion as to the real requirements of ontology engineering and take the view that only a selective and restricted set of requirements will enable the beast to fly.

Dynamic iterative ontology learning

The fundamental failure of current approaches to ontology learning is to view it as single pipeline with one or more specific inputs and a single static output. In this paper, we present a novel approach to ontology learning which takes an iterative view of knowledge acquisition for ontologies. Our approach is founded on three open-ended resources: a set of texts, a set of learning patterns and a set of ontological triples, and the system seeks to maintain these in equilibrium. As events occur which disturb this equilibrium, actions are triggered to re-establish a balance between the resources. We present a gold standard based evaluation of the final output of the system, the intermediate output showing the iterative process and a comparison of performance using different seed input. The results are comparable to existing performance in the literature.

An incremental tri-partite approach to ontology learning

In this paper we present a new approach to ontology learning. Its basis lies in a dynamic and iterative view of knowledge acquisition for ontologies. The Abraxas approach is founded on three resources, a set of texts, a set of learning patterns and a set of ontological triples, each of which must remain in equilibrium. As events occur which disturb this equilibrium various actions are triggered to re-establish a balance between the resources. Such events include acquisition of a further text from external resources such as the Web or the addition of ontological triples to the ontology. We develop the concept of a knowledge gap between the coverage of an ontology and the corpus of texts as a measure triggering actions. We present an overview of the algorithm and its functionalities.

Attenuated total reflection-FT-IR spectroscopic imaging of protein crystallization

Protein crystallization is of strategic and commercial relevance in the post-genomic era because of its pivotal role in structural proteomics projects. Although protein structures are crucial for understanding the function of proteins and to the success of rational drug design and other biotechnology applications, obtaining high quality crystals is a major bottleneck to progress. The major means of obtaining crystals is by massive-scale screening of a target protein solution with numerous crystallizing agents. However, when crystals appear in these screens, one cannot easily know if they are crystals of protein, salt, or any other molecule that happens to be present in the trials. We present here a method based on Attenuated Total Reflection (ATR)-FT-IR imaging that reliably identifies protein crystals through a combination of chemical specificity and the visualizing capability of this approach, thus solving a major hurdle in protein crystallization. ATR-FT-IR imaging was successfully applied to study the crystallization of thaumatin and lysozyme in a high-throughput manner, simultaneously from six different solutions. This approach is fast as it studies protein crystallization in situ and provides an opportunity to examine many different samples under a range of conditions.

Purification and crystallization of the hydroxylase component of the methanesulfonate monooxygenase from Methylosulfonomonas methylovora strain M2

The aim of the work described in this paper was two-fold: (1) the purification of the hydroxylase component of the MSAMO to electrophoretic homogeneity using a four-step chromatographic strategy and (2) the crystallization of the two-component hydroxylase of the MSAMO in order to enhance our understanding of the precise three-dimensional structure of the MSAMO, thus yielding an insight into the nature of the active site of this enzyme. Optimised crystallization conditions were identified allowing growth of crystals of the hydroxylase component of the MSAMO within five days. Crystals exhibited a brown colour suggesting the presence on an intact Rieske-iron sulfur centre and diffracted to 7.0 Å when a few degrees of data were evaluated on a beam line X11. © 2006 Elsevier Inc. All rights reserved.

Clinical decision support system for point of care use: ontology driven design and software implementation

OBJECTIVES: The objective of this research was to design a clinical decision support system (CDSS) that supports heterogeneous clinical decision problems and runs on multiple computing platforms. Meeting this objective required a novel design to create an extendable and easy to maintain clinical CDSS for point of care support. The proposed solution was evaluated in a proof of concept implementation. METHODS: Based on our earlier research with the design of a mobile CDSS for emergency triage we used ontology-driven design to represent essential components of a CDSS. Models of clinical decision problems were derived from the ontology and they were processed into executable applications during runtime. This allowed scaling applications' functionality to the capabilities of computing platforms. A prototype of the system was implemented using the extended client-server architecture and Web services to distribute the functions of the system and to make it operational in limited connectivity conditions. RESULTS: The proposed design provided a common framework that facilitated development of diversified clinical applications running seamlessly on a variety of computing platforms. It was prototyped for two clinical decision problems and settings (triage of acute pain in the emergency department and postoperative management of radical prostatectomy on the hospital ward) and implemented on two computing platforms-desktop and handheld computers. CONCLUSIONS: The requirement of the CDSS heterogeneity was satisfied with ontology-driven design. Processing of application models described with the help of ontological models allowed having a complex system running on multiple computing platforms with different capabilities. Finally, separation of models and runtime components contributed to improved extensibility and maintainability of the system.

Optimization of protein crystallization:the OptiCryst project

Protein crystallization has gained a new strategic and commercial relevance in the postgenomic era due to its pivotal role in structural genomics. Producing high quality crystals has always been a bottleneck to efficient structure determination, and this problem is becoming increasingly acute. This is especially true for challenging, therapeutically important proteins that typically do not form suitable crystals. The OptiCryst consortium has focused on relieving this bottleneck by making a concerted effort to improve the crystallization techniques usually employed, designing new crystallization tools, and applying such developments to the optimization of target protein crystals. In particular, the focus has been on the novel application of dual polarization interferometry (DPI) to detect suitable nucleation; the application of in situ dynamic light scattering (DLS) to monitor and analyze the process of crystallization; the use of UV-fluorescence to differentiate protein crystals from salt; the design of novel nucleants and seeding technologies; and the development of kits for capillary counterdiffusion and crystal growth in gels. The consortium collectively handled 60 new target proteins that had not been crystallized previously. From these, we generated 39 crystals with improved diffraction properties. Fourteen of these 39 were only obtainable using OptiCryst methods. For the remaining 25, OptiCryst methods were used in combination with standard crystallization techniques. Eighteen structures have already been solved (30% success rate), with several more in the pipeline.

In situ studies of phase separation and crystallization directed by Marangoni instabilities during spin-coating

Results of a pioneering study are presented in which for the first time, crystallization, phase separation and Marangoni instabilities occurring during the spin-coating of polymer blends are directly visualized, in real-space and real-time. The results provide exciting new insights into the process of self-assembly, taking place during spin-coating, paving the way for the rational design of processing conditions, to allow desired morphologies to be obtained. © 2013 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Ontology-based protein-protein interactions extraction from literature using the hidden vector state model

This paper proposes a novel framework of incorporating protein-protein interactions (PPI) ontology knowledge into PPI extraction from biomedical literature in order to address the emerging challenges of deep natural language understanding. It is built upon the existing work on relation extraction using the Hidden Vector State (HVS) model. The HVS model belongs to the category of statistical learning methods. It can be trained directly from un-annotated data in a constrained way whilst at the same time being able to capture the underlying named entity relationships. However, it is difficult to incorporate background knowledge or non-local information into the HVS model. This paper proposes to represent the HVS model as a conditionally trained undirected graphical model in which non-local features derived from PPI ontology through inference would be easily incorporated. The seamless fusion of ontology inference with statistical learning produces a new paradigm to information extraction.