A method for automatically eliciting node weights in a hierarchical knowledge-based structure for reasoning with uncertainty

Hierarchical knowledge structures are frequently used within clinical decision support systems as part of the model for generating intelligent advice. The nodes in the hierarchy inevitably have varying influence on the decisionmaking processes, which needs to be reflected by parameters. If the model has been elicited from human experts, it is not feasible to ask them to estimate the parameters because there will be so many in even moderately-sized structures. This paper describes how the parameters could be obtained from data instead, using only a small number of cases. The original method [1] is applied to a particular web-based clinical decision support system called GRiST, which uses its hierarchical knowledge to quantify the risks associated with mental-health problems. The knowledge was elicited from multidisciplinary mental-health practitioners but the tree has several thousand nodes, all requiring an estimation of their relative influence on the assessment process. The method described in the paper shows how they can be obtained from about 200 cases instead. It greatly reduces the experts’ elicitation tasks and has the potential for being generalised to similar knowledge-engineering domains where relative weightings of node siblings are part of the parameter space.

An investigation into inductive parameter learning in complex hierarchical knowledge structures representing clinical expertise

This dissertation investigates the very important and current problem of modelling human expertise. This is an apparent issue in any computer system emulating human decision making. It is prominent in Clinical Decision Support Systems (CDSS) due to the complexity of the induction process and the vast number of parameters in most cases. Other issues such as human error and missing or incomplete data present further challenges. In this thesis, the Galatean Risk Screening Tool (GRiST) is used as an example of modelling clinical expertise and parameter elicitation. The tool is a mental health clinical record management system with a top layer of decision support capabilities. It is currently being deployed by several NHS mental health trusts across the UK. The aim of the research is to investigate the problem of parameter elicitation by inducing them from real clinical data rather than from the human experts who provided the decision model. The induced parameters provide an insight into both the data relationships and how experts make decisions themselves. The outcomes help further understand human decision making and, in particular, help GRiST provide more accurate emulations of risk judgements. Although the algorithms and methods presented in this dissertation are applied to GRiST, they can be adopted for other human knowledge engineering domains.

Technology for Ontological Engineering Lifecycle Support

The research is partially supported by Russian Foundation for Basic Research (grants 06-01-81005 and 07-01- 00053)

A knowledge driven approach towards the validation of externally acquired traceability datasets in supply chain business processes

The sharing of near real-time traceability knowledge in supply chains plays a central role in coordinating business operations and is a key driver for their success. However before traceability datasets received from external partners can be integrated with datasets generated internally within an organisation, they need to be validated against information recorded for the physical goods received as well as against bespoke rules defined to ensure uniformity, consistency and completeness within the supply chain. In this paper, we present a knowledge driven framework for the runtime validation of critical constraints on incoming traceability datasets encapuslated as EPCIS event-based linked pedigrees. Our constraints are defined using SPARQL queries and SPIN rules. We present a novel validation architecture based on the integration of Apache Storm framework for real time, distributed computation with popular Semantic Web/Linked data libraries and exemplify our methodology on an abstraction of the pharmaceutical supply chain.

Knowledge-based Chord Embeddings

This thesis develops AI methods as a contribution to computational musicology, an interdisciplinary field that studies music with computers. In systematic musicology a composition is defined as the combination of harmony, melody and rhythm. According to de La Borde, harmony alone "merits the name of composition". This thesis focuses on analysing the harmony from a computational perspective. We concentrate on symbolic music representation and address the problem of formally representing chord progressions in western music compositions. Informally, chords are sets of pitches played simultaneously, and chord progressions constitute the harmony of a composition. Our approach combines ML techniques with knowledge-based techniques. We design and implement the Modal Harmony ontology (MHO), using OWL. It formalises one of the most important theories in western music: the Modal Harmony Theory. We propose and experiment with different types of embedding methods to encode chords, inspired by NLP and adapted to the music domain, using both statistical (extensional) knowledge by relying on a huge dataset of chord annotations (ChoCo), intensional knowledge by relying on MHO and a combination of the two. The methods are evaluated on two musicologically relevant tasks: chord classification and music structure segmentation. The former is verified by comparing the results of the Odd One Out algorithm to the classification obtained with MHO. Good performances (accuracy: 0.86) are achieved. We feed a RNN for the latter, using our embeddings. Results show that the best performance (F1: 0.6) is achieved with embeddings that combine both approaches. Our method outpeforms the state of the art (F1 = 0.42) for symbolic music structure segmentation. It is worth noticing that embeddings based only on MHO almost equal the best performance (F1 = 0.58). We remark that those embeddings only require the ontology as an input as opposed to other approaches that rely on large datasets.

Roadmapping for technology push and partnership: A contribution for open innovation environments

There are several tools in the literature that support innovation in organizations. Some of the most cited are the so-called technology roadmapping methods, also known as TRM. However, these methods are designed primarily for organizations that adopt the market pull strategy of technology-product integration. Organizations that adopt the technology push integration strategy are neglected in the literature. Furthermore, with the advent of open innovation, it is possible to note the need to consider the adoption of partnerships in the innovation process. Thus, this study proposes a method of technology roadmapping, identified as method for technology push (MTP), applicable to organizations that adopt the technology push integration strategy, such as SMEs and independent research centers in an open-innovation environment. The method was developed through action-research and was assessed from two analytical standpoints: externally, via a specific literature review on its theoretical contributions, and internally, through the analysis of potential users` perceptions on the feasibility of applying MTP. The results indicate both the unique character of the method and its perceived implementation feasibility. Future research is suggested in order to validate the method in different types of organizations (C) 2011 Elsevier Ltd. All rights reserved.

Supporting content-based image retrieval and computer-aided diagnosis systems with association rule-based techniques

In this work, we take advantage of association rule mining to support two types of medical systems: the Content-based Image Retrieval (CBIR) systems and the Computer-Aided Diagnosis (CAD) systems. For content-based retrieval, association rules are employed to reduce the dimensionality of the feature vectors that represent the images and to improve the precision of the similarity queries. We refer to the association rule-based method to improve CBIR systems proposed here as Feature selection through Association Rules (FAR). To improve CAD systems, we propose the Image Diagnosis Enhancement through Association rules (IDEA) method. Association rules are employed to suggest a second opinion to the radiologist or a preliminary diagnosis of a new image. A second opinion automatically obtained can either accelerate the process of diagnosing or to strengthen a hypothesis, increasing the probability of a prescribed treatment be successful. Two new algorithms are proposed to support the IDEA method: to pre-process low-level features and to propose a preliminary diagnosis based on association rules. We performed several experiments to validate the proposed methods. The results indicate that association rules can be successfully applied to improve CBIR and CAD systems, empowering the arsenal of techniques to support medical image analysis in medical systems. (C) 2009 Elsevier B.V. All rights reserved.

Distinguishing general concepts from individuals: an automatic coarse-grained classifier

Named entity recognizers are unable to distinguish if a term is a general concept as "scientist" or an individual as "Einstein". In this paper we explore the possibility to reach this goal combining two basic approaches: (i) Super Sense Tagging (SST) and (ii) YAGO. Thanks to these two powerful tools we could automatically create a corpus set in order to train the SuperSense Tagger. The general F1 is over 76% and the model is publicly available.

Gestão e trabalhadores do conhecimento em Tecnologias da informação (UML)

RESUMO: O conhecimento existe desde sempre, mesmo num estado latente condicionado algures e apenas à espera de um meio (de uma oportunidade) de se poder manifestar. O conhecimento é duplamente um fenómeno da consciência: porque dela procede num dado momento da sua vida e da sua história e porque só nela termina, aperfeiçoando-a e enriquecendo-a. O conhecimento está assim em constante mudança. À relativamente pouco tempo começou-se a falar de Gestão do Conhecimento e na altura foi muito associada às Tecnologias da Informação, como meio de colectar, processar e armazenar cada vez mais, maiores quantidades de informação. As Tecnologias da Informação têm tido, desde alguns anos para cá, um papel extremamente importante nas organizações, inicialmente foram adoptadas com o propósito de automatizar os processos operacionais das organizações, que suportam as suas actividades quotidianas e nestes últimos tempos as Tecnologias da Informação dentro das organizações têm evoluído rapidamente. Todo o conhecimento, mesmo até o menos relevante de uma determinada área de negócio, é fundamental para apoiar o processo de tomada de decisão. As organizações para atingirem melhores «performances» e conseguirem transcender as metas a que se propuseram inicialmente, tendem a munir-se de mais e melhores Sistemas de Informação, assim como, à utilização de várias metodologias e tecnologias hoje em dia disponíveis. Por conseguinte, nestes últimos anos, muitas organizações têm vindo a demonstrar uma necessidade crucial de integração de toda a sua informação, a qual está dispersa pelos diversos departamentos constituintes. Para que os gestores de topo (mas também para outros funcionários) possam ter disponível em tempo útil, informação pertinente, verdadeira e fiável dos negócios da organização que eles representam, precisam de ter acesso a bons Sistemas de Tecnologias de Informação. Numa acção de poderem agir mais eficazmente e eficientemente nas tomadas de decisão, por terem conseguido tirar por esses meios o máximo de proveito possível da informação, e assim, apresentarem melhores níveis de sucesso organizacionais. Também, os Sistemas de «Business Intelligence» e as Tecnologias da Informação a ele associadas, utilizam os dados existentes nas organizações para disponibilizar informação relevante para as tomadas de decisão. Mas, para poderem alcançar esses níveis tão satisfatórios, as organizações necessitam de recursos humanos, pois como podem elas serem competitivas sem Luís Miguel Borges – Gestão e Trabalhadores do Conhecimento em Tecnologias da Informação (UML) ULHT – ECATI 6 trabalhadores qualificados. Assim, surge a necessidade das organizações em recrutar os chamados hoje em dia “Trabalhadores do Conhecimento”, que são os indivíduos habilitados para interpretar as informações dentro de um domínio específico. Eles detectam problemas e identificam alternativas, com os seus conhecimentos e discernimento, eles trabalham para solucionar esses problemas, ajudando consideravelmente as organizações que representam. E, usando metodologias e tecnologias da Engenharia do Conhecimento como a modelação, criarem e gerirem um histórico de conhecimento, incluindo conhecimento tácito, sobre várias áreas de negócios da organização, que podem estar explícitos em modelos abstractos, que possam ser compreendidos e interpretados facilmente, por outros trabalhadores com níveis de competência equivalentes. ABSTRACT: Knowledge has always existed, even in a latent state conditioning somewhere and just waiting for a half (an opportunity) to be able to manifest. Knowledge is doubly a phenomenon of consciousness: because proceeds itself at one point in its life and its history and because solely itself ends, perfecting it and enriching it. The knowledge is so in constant change. In the relatively short time that it began to speak of Knowledge Management and at that time was very associated with Information Technologies, as a means to collect, process and store more and more, larger amounts of information. Information Technologies has had, from a few years back, an extremely important role in organizations, were initially adopted in order to automate the operational processes of organizations, that support their daily activities and in recent times Information Technologies within organizations has evolved rapidly. All the knowledge, even to the least relevant to a particular business area, is fundamental to support the process of decision making. The organizations to achieve better performances and to transcend the goals that were initially propose, tend to provide itself with more and better Information Systems, as well as, the use of various methodologies and technologies available today. Consequently, in recent years, many organizations have demonstrated a crucial need for integrating all their information, which is dispersed by the diver constituents departments. For top managers (but also for other employees) may have ready in time, pertinent, truthful and reliable information of the organization they represent, need access to good Information Technology Systems. In an action that they can act more effectively and efficiently in decision making, for having managed to get through these means the maximum possible advantage of the information, and so, present better levels of organizational success. Also, the systems of Business Intelligence and Information Technologies its associated, use existing data on organizations to provide relevant information for decision making. But, in order to achieve these levels as satisfactory, organizations need human resources, because how can they be competitive without skilled workers. Thus, arises the need for organizations to recruit called today “Knowledge Workers”, they are the individuals enable to interpret the information within a specific domain. They detect problems and identify alternatives, with their knowledge and discernment they work to solve these problems, helping considerably the organizations that represent. And, using Luís Miguel Borges – Gestão e Trabalhadores do Conhecimento em Tecnologias da Informação (UML) ULHT – ECATI 8 methodologies and technologies of Knowledge Engineering as modeling, create and manage a history of knowledge, including tacit knowledge, on various business areas of the organization, that can be explicit in the abstract models, that can be understood and interpreted easily, by other workers with equivalent levels of competence.

Involving users in expert system development

The ultimate criterion of success for interactive expert systems is that they will be used, and used to effect, by individuals other than the system developers. A key ingredient of success in most systems is involving users in the specification and development of systems as they are being built. However, until recently, system designers have paid little attention to ascertaining user needs and to developing systems with corresponding functionality and appropriate interfaces to match those requirements. Although the situation is beginning to change, many developers do not know how to go about involving users, or else tackle the problem in an inadequate way. This paper discusses the need for user involvement and considers why many developers are still not involving users in an optimal way. It looks at the different ways in which users can be involved in the development process and describes how to select appropriate techniques and methods for studying users. Finally, it discusses some of the problems inherent in involving users in expert system development, and recommends an approach which incorporates both ethnographic analysis and formal user testing.

Empirical impact of cache effects for vision system design

Computer vision applications generally split their problem into multiple simpler tasks. Likewise research often combines algorithms into systems for evaluation purposes. Frameworks for modular vision provide interfaces and mechanisms for algorithm combination and network transparency. However, these don’t provide interfaces efficiently utilising the slow memory in modern PCs. We investigate quantitatively how system performance varies with different patterns of memory usage by the framework for an example vision system.