Bisociative knowledge discovery for microarray data analysis

Peer reviewe

Lexical Creativity from Word Associations

A fluent ability to associate tasks, concepts, ideas, knowledge and experiences in a relevant way is often considered an important factor of creativity, especially in problem solving. We are interested in providing computational support for discovering such creative associations. In this paper we design minimally supervised methods that can perform well in the remote associates test (RAT), a well-known psychometric measure of creativity. We show that with a large corpus of text and some relatively simple principles, this can be achieved. We then develop methods for a more general word association model that could be used in lexical creativity support systems, and which also could be a small step towards lexical creativity in computers.Peer reviewe

Indirect Relatedness, Evaluation, and Visualization for Literature Based Discovery

The exponential growth of scientific literature is creating an increased need for systems to process and assimilate knowledge contained within text. Literature Based Discovery (LBD) is a well established field that seeks to synthesize new knowledge from existing literature, but it has remained primarily in the theoretical realm rather than in real-world application. This lack of real-world adoption is due in part to the difficulty of LBD, but also due to several solvable problems present in LBD today. Of these problems, the ones in most critical need of improvement are: (1) the over-generation of knowledge by LBD systems, (2) a lack of meaningful evaluation standards, and (3) the difficulty interpreting LBD output. We address each of these problems by: (1) developing indirect relatedness measures for ranking and filtering LBD hypotheses; (2) developing a representative evaluation dataset and applying meaningful evaluation methods to individual components of LBD; (3) developing an interactive visualization system that allows a user to explore LBD output in its entirety. In addressing these problems, we make several contributions, most importantly: (1) state of the art results for estimating direct semantic relatedness, (2) development of set association measures, (3) development of indirect association measures, (4) development of a standard LBD evaluation dataset, (5) division of LBD into discrete components with well defined evaluation methods, (6) development of automatic functional group discovery, and (7) integration of indirect relatedness measures and automatic functional group discovery into a comprehensive LBD visualization system. Our results inform future development of LBD systems, and contribute to creating more effective LBD systems

Connotation in Computational Creativity

Computational creativity is the application of computers to perform tasks that would be regarded as creative if performed by humans. One approach to computational creativity is to regard it as a search process, where some conceptual space is searched, and perhaps transformed, to find an outcome that would be regarded as creative. Typically, such search processes have been guided by one or more objective functions that judge how creative each solution is on one or more dimensions. This paper introduces a contrasting approach, which is search based on the idea of connotations. Rather than exploring a space constructed solely of potential outcomes, a larger space is explored consisting of such outcomes together with other relevant information. This allows us to define search processes that include a more exploratory process, out of which an outcome emerges via density of connotations. Both the general principles behind this and some specific ideas are explored

Modelo para descoberta de conhecimento baseado em associação semântica e temporal entre elementos textuais

Tese (doutorado) - Universidade Federal de Santa Catarina, Centro Tecnológico, Programa de Pós-Graduação em Engenharia e Gestão do Conhecimento, Florianópolis, 2016.O aumento da complexidade nas atividades organizacionais, a vertiginosa expansão da Internet e os avanços da sociedade do conhecimento são alguns dos responsáveis pelo volume inédito de dados digitais. Essa crescente massa de dados apresenta grande potencial para a análise de padrões e descoberta de conhecimento. Nesse sentido, a análise dos relacionamentos presentes nesse imenso volume de informações pode proporcionar novos e, possivelmente, inesperados insights. A presente pesquisa constatou a escassez de trabalhos que consideram adequadamente a semântica e a temporalidade dos relacionamentos entre elementos textuais, características consideradas importantes para a descoberta de conhecimento. Assim, este trabalho propõe um modelo para descoberta de conhecimento que conta com uma ontologia de alto-nível para a representação de relacionamentos e com a técnica Latent Semantic Indexing (LSI) para determinar a força de associação entre termos que não se relacionam diretamente. A representação do conhecimento de domínio, bem como, a determinação da força associativa entre os termos são realizadas levando em conta o tempo em que os relacionamentos ocorrem. A avaliação do modelo foi realizada a partir de dois tipos de experimentos: um que trata da classificação de documentos e outro que trata da associação semântica e temporal entre termos. Os resultados demonstram que o modelo: i) possui potencial para ser aplicado em tarefas intensivas em conhecimento, como a classificação e ii) é capaz de apresentar curvas da força associativa entre dois termos ao longo do tempo, contribuindo para o levantamento de hipóteses e, consequentemente, para a descoberta de conhecimento.Abstract : The increased complexity in organizational activities, the rapid expansion of the Internet and advances in the knowledge society are some of those responsible for the unprecedented volume of digital data. This growing body of data has great potential for pattern analysis and knowledge discovery. In this sense, the analysis of relationships present in this immense volume of information can provide new and possibly unexpected insights. This research found shortages of studies that adequately consider the semantics and the temporality of relationships between textual elements considered important features for knowledge discovery. This work proposes a model of knowledge discovery comprising a high-level ontology for the representation of relationships and the LSI technique to determine the strength of association between terms that do not relate directly. The representation of domain knowledge and the determination of the associative strength between the terms are made taking into account the time in which the relationships occur. The evaluation of the model was made from two types of experiments: one that deals with the classification of documents and another concerning semantics and temporal association between terms. The results show that the model: i) has the potential to be used as a text classifier and ii) is capable of displaying curves of associative force between two terms over time, contributing to the raising of hypotheses and therefore to discover of knowledge

Word Associations as a Language Model for Generative and Creative Tasks

In order to analyse natural language and gain a better understanding of documents, a common approach is to produce a language model which creates a structured representation of language which could then be used further for analysis or generation. This thesis will focus on a fairly simple language model which looks at word associations which appear together in the same sentence. We will revisit a classic idea of analysing word co-occurrences statistically and propose a simple parameter-free method for extracting common word associations, i.e. associations between words that are often used in the same context (e.g., Batman and Robin). Additionally we propose a method for extracting associations which are specific to a document or a set of documents. The idea behind the method is to take into account the common word associations and highlight such word associations which co-occur in the document unexpectedly often. We will empirically show that these models can be used in practice at least for three tasks: generation of creative combinations of related words, document summarization, and creating poetry. First the common word association language model is used for solving tests of creativity -- the Remote Associates test. Then observations of the properties of the model are used further to generate creative combinations of words -- sets of words which are mutually not related, but do share a common related concept. Document summarization is a task where a system has to produce a short summary of the text with a limited number of words. In this thesis, we will propose a method which will utilise the document-specific associations and basic graph algorithms to produce summaries which give competitive performance on various languages. Also, the document-specific associations are used in order to produce poetry which is related to a certain document or a set of documents. The idea is to use documents as inspiration for generating poems which could potentially be used as commentary to news stories. Empirical results indicate that both, the common and the document-specific associations, can be used effectively for different applications. This provides us with a simple language model which could be used for different languages.Kielimalleja käytetään usein luonnollisten kielten ja dokumenttien ymmärtämiseen. Kielimalli on kielen rakenteellinen esitysmuoto, jota voidaan käyttää kielen analyysiin tai sen tuottamiseen. Tässä työssä esitetään yksinkertainen kielimalli, joka perustuu assosiaatioihin sanojen välillä, jotka esiintyvät samassa lausessa. Ensin tutustumme klassiseen menetelmään analysoida sanojen yhteisesiintymiä tilastollisesti, jonka perusteella esittelemme parametri-vapaan menetelmän tuottaa yleisiä sana-assosiaatioita. Nämä sana-assosiaatiot ovat yhteyksiä sellaisten sanojen välillä, jotka esiintyvät samoissa asiayhteyksissä, kuten esimerkiksi Batman ja Robin. Lisäksi esittelemme menetelmän, joka tuottaa näitä assosiaatioita tietylle dokumentille tai joukolle dokumentteja. Menetelmä perustuu niiden sana-assosiaatioiden huomioimiseen, jotka ovat lähde-dokumenteissa erityisen yleisiä. Näytämme empiirisesti, että kielimallejamme voidaan käyttää ainakin kolmeen tarkoitukseen: luovien sanayhdistelmien tuottamiseen, dokumenttien referointiin ja runojen tuottamiseen. Ratkomme ensin yleisiin sana-assosiaatioihin perustuvalla mallillamme luovuutta testaavia Remote Associates -kokeita. Sen jälkeen tuotamme mallista tehtyjen havaintojen perusteella luovia sanayhdistelmiä. Nämä yhdistelmät sisältävät sanoja, jotka eivät välttämättä ole keskenään toisiinsa liittyviä, mutta ne jakavat joitakin yhdistäviä käsitteitä. Dokumentin referointi viittaa tehtävään, jossa pitää tuottaa rajoitetun pituinen lyhennelmä pidemmästä dokumentista. Esitämme menetelmän joka tuottaa eri kielillä tasoltaan kilpailukykyisiä referaatteja, käyttäen dokumenttikohtaisia sana-assosiaatioita sekä yksinkertaisia graafi-algoritmeja. Assosiaatioiden avulla voidaan tuottaa myös dokementtikohtaisia runoja. Dokumenttien inspiroimia runoja voitaisiin käyttää esimerkiksi uutisartikkeleiden kommentointiin. Tuloksemme niin yleisiin kuin dokumenttikohtaisiin assosiaatioihin perustuvista malleista osoittavat, että näitä malleja voidaan käyttää tehokkaasti eri käyttötarkoituksiin. Tuloksena on yksinkertainen kielimalli, jota voidaan käyttää useiden eri kielten kanssa

Bioinformatics Applications Based On Machine Learning

The great advances in information technology (IT) have implications for many sectors, such as bioinformatics, and has considerably increased their possibilities. This book presents a collection of 11 original research papers, all of them related to the application of IT-related techniques within the bioinformatics sector: from new applications created from the adaptation and application of existing techniques to the creation of new methodologies to solve existing problems

Evaluating computational creativity: a standardised procedure for evaluating creative systems and its application

This thesis proposes SPECS: a Standardised Procedure for Evaluating Creative Systems. No methodology has been accepted as standard for evaluating the creativity of a system in the field of computational creativity and the multi-faceted and subjective nature of creativity generates substantial definitional issues. Evaluative practice has developed a general lack of rigour and systematicity, hindering research progress. SPECS is a standardised and systematic methodology for evaluating computational creativity. It is flexible enough to be applied to a variety of different types of creative system and adaptable to specific demands in different types of creativity. In the three-stage process of evaluation, researchers are required to be specific about what creativity entails in the domain they work in and what standards they test a system’s creativity by. To assist researchers, definitional issues are investigated and a set of components representing aspects of creativity is presented, which was empirically derived using computational linguistics analysis. These components are recommended for use within SPECS, being offered as a general definition of creativity that can be customised to account for any specific priorities for creativity in a given domain. SPECS is applied in a case study for detailed comparisons of the creativity of three musical improvisation systems, identifying which systems are more creative than others and why. In a second case study, SPECS is used to capture initial impressions on the creativity of systems presented at a 2011 computational creativity research event. Five systems performing different creative tasks are compared and contrasted. These case studies exemplify the valuable information that can be obtained on a system’s strengths and weaknesses. SPECS gives researchers vital feedback for improving their systems’ creativity, informing further progress in computational creativity research