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.

Some aspects of the interaction of humic substances with metal ions

Humic substances are the major organic constituents of soils and sediments. They are heterogeneous, polyfunctional, polydisperse, macromolecular and have no accurately known chemical structure. Their interactions with radionuclides are particularly important since they provide leaching mechanisms from disposal sites. The central theme to this research is the interaction of heavy metal actinide analogues with humic materials. Studies described focus on selected aspects of the characteristics and properties of humic substances. Some novel approaches to experiments and data analysis are pursued. Several humic substances are studied; all but one are humic acids, and those used most extensively were obtained commercially. Some routine characterisation techniques are applied to samples in the first instance. Humic substances are coloured, but their ultra-violet and visible absorption spectra are featureless. Yet, they fluoresce over a wide range of wavelengths. Enhanced fluorescence in the presence of luminescent europium(III) ions is explained by energy transfer from irradiated humic acid to the metal ion in a photophysical model. Nuclear magnetic resonance spectroscopy is applied to the study of humic acids and their complexes with heavy metals. Proton and carbon-13 NMR provides some structural and functionality information; Paramagnetic lanthanide ions affect these spectra. Some heavy metals are studied as NMR nuclei, but measurements are restricted by their sensitivity. A humic acid is fractionated yielding a broad molecular weight distribution. Electrophoretic mobilities and particle radii determined by Laser Doppler Electrophoretic Light Scattering are sensitive to the conditions of the supporting media, and the concentration and particle size distribution of humic substances. In potentiometric titrations of humate dispersions, the organic matter responds slowly and the mineral acid addition is buffered. Proton concentration data is modelled and a mechanism is proposed involving two key stages, both resulting in proton release after some conformational changes.

Substances hazardous to health:the nature of the expertise associated with competent risk assessment

This research investigated expertise in hazardous substance risk assessment (HSRA). Competent pro-active risk assessment is needed to prevent occupational ill-health caused by hazardous substance exposure occurring in the future. In recent years there has been a strong demand for HSRA expertise and a shortage of expert practitioners. The discipline of Occupational Hygiene was identified as the key repository of knowledge and skills for HSRA and one objective of this research was to develop a method to elicit this expertise from experienced occupational hygienists. In the study of generic expertise, many methods of knowledge elicitation (KE) have been investigated, since this has been relevant to the development of 'expert systems' (thinking computers). Here, knowledge needed to be elicited from human experts, and this stage was often a bottleneck in system development, since experts could not explain the basis of their expertise. At an intermediate stage, information collected was used to structure a basic model of hazardous substance risk assessment activity (HSRA Model B) and this formed the basis of tape transcript analysis in the main study with derivation of a 'classification' and a 'performance matrix'. The study aimed to elicit the expertise of occupational hygienists and compare their performance with other health and safety professionals (occupational health physicians, occupational health nurses, health and safety practitioners and trainee health and safety inspectors), as evaluated using the matrix. As a group, the hygienists performed best in the exercise, and this group were particularly good at process elicitation and at recommending specific control measures, although the other groups also performed well in selected aspects of the matrix and the work provided useful findings and insights. From the research, two models of HSRA have been derived, an HSRA aid, together with a novel videotape KE technique and interesting research findings. The implications of this are discussed with respect to future training of HS professionals and wider application of the videotape KE method.