Context-based understanding of food-related queries using a culinary knowledge model

Dietary practices are governed by a mix of ethnographic aspects, such as social, cultural and environmental factors. These aspects need to be taken into consideration during an analysis of food-related queries. Queries are usually ambiguous. It is essential to understand, analyse and refine the queries for better search and retrieval. The work is focused on identifying the explicit, implicit and hidden facets of a query, taking into consideration the context – culinary domain. This article proposes a technique for query understanding, analysis and refinement based on a domain specific knowledge model. Queries are conceptualised by mapping the query term to concepts defined in the model. This allows an understanding of the semantic point of view of a query and an ability to determine the meaning of its terms and their interrelatedness. The knowledge model acts as a backbone providing the context for query understanding, analysis and refinement and outperforms other models, such as Schema.org, BBC Food Ontology and Recipe Ontology

Investigation on Applying Modular Ontology to Statistical Language Model for Information Retrieval

The objective of this research is to provide a novel approach to improving retrieval performance by exploiting Ontology with the statistical language model (SLM). The proposed methods consist of two major processes, namely ontology-based query expansion (OQE) and ontology-based document classification (ODC). Research experiments have required development of an independent search tool that can combine the OQE and ODC in a traditional SLM-based information retrieval (IR) process using a Web document collection. This research considers the ongoing challenges of modular ontology enhanced SLM-based search and addresses three contribution aspects. The first concerns how to apply modular ontology to query expansion, in a bespoke language model search tool (LMST). The second considers how to incorporate OQE with the language model to improve the search performance. The third examines how to manipulate such semantic-based document classification to improve the smoothing accuracy. The role of ontology in the research is to provide formally described domains of interest that serve as context, to enhance system query effectiveness

Computational methods for data discovery, harmonization and integration:Using lexical and semantic matching with an application to biobanking phenotypes

Grote gegevensverzamelingen rondom menselijke proefpersonen/patiënten, zoals biobanken en patiënten registraties, zijn onmisbaar geworden voor onderzoek naar ziekte en gezondheid, en de vertaling van dit onderzoek naar zorg en preventie. De afgelopen jaren heeft dit soort onderzoek een enorme vlucht genomen, van beperkte studies in context van specifieke ziektebeelden tot nu grootschalig bestuderen van ziekten en het complexe samenspel van genetische en omgevingsfactoren. Succesvolle uitvoering van dit soort studies vereist enorme datasets. Doordat de data in biobanken typisch is verzameld voor verschillende doelen, en daardoor dus ook qua structuur en samenstelling verschillen, is data integratie een moeizaam en tijdsintensief proces waarbij vele methodologische, technische en ethisch/juridische horden moeten worden genomen. Dit proefschrift beschrijft het onderzoek naar de uitdagingen rondom het ‘poolen’ van phenotypische gegevens over meerdere biobanken. In het bijzonder hebben we ons bezig gehouden met de vraagstukken rondom (i) het effectief in kaart brengen en vindbaar maken van relevante datasets en de bijbehorende data items, (ii) het kunnen vaststellen welke van de data items vanuit elke bron dataset potentieel gecombineerd kunnen worden als basis voor analyseen (iii) op welke wijze deze data efficiënt kunnen worden getransformeerd naar een gestandaardiseerde dataset om daadwerkelijk geïntegreerde analyse mogelijk te maken. Het resultaat is een collectie nieuwe computationele methoden, inclusief bruikbare software, waarmee (semi)automatisch en efficiënt verschillen in data verzameling en beschrijving kunnen worden overbrugd zodat onderzoekers veel sneller dan hiervoor data kunnen vinden, harmoniseren en integreren

Investigating ontology based query expansion using a probabilistic retrieval model

This research briefly outlines the problems of traditional information retrieval systems and discusses the different approaches to inferring context in document retrieval. By context we mean word disambiguation which is achieved by exploring the generalisation-specialisation hierarchies within a given ontology. Specifically, we examine the use of ontology based query expansion for defining query context. Query expansion can be done in many ways and in this work we consider the use of relevance feedback and pseudo-relevance feedback for query expansion. We examine relevance feedback and pseudo-relevance to ascertain the existence of performance differences between relevance feedback and pseudo-relevance feedback. The information retrieval system used is based on the probabilistic retrieval model and the query expansion method is extended using information from a news domain ontology. The aim of this project is to assess the impact of the use of the ontology on the query expansion results. Our results show that ontology based query expansion has resulted in a higher number of relevant documents being retrieved compared to the standard relevance feedback process. Overall, ontology based query expansion improves recall but does not produce any significant improvements for the precision results. Pseudo-relevance feedback has achieved better results than relevance feedback. We also found that reducing or increasing the relevance feedback parameters (number of terms or number of documents) does not correlate with the results. When comparing the effect of varying the number of terms parameter with the number of documents parameter, the former benefits the pseudo-relevance feedback results but the latter has an additional effect on the relevance feedback results. There are many factors which influence the success of ontology based query expansion. The thesis discusses these factors and gives some guidelines on using ontologies for the purpose of query expansion

Um Modelo baseado em contexto para expansão de consultas semânticas em redes colaborativas de organizações

Tese(doutorado) - Universidade Federal de Santa Catarina, Centro Tecnológico. Programa de Pós-Graduação em Engenharia Elétrica.As novas tendências do mundo globalizado levaram organizações e profissionais a focarem em estratégias baseadas em trabalho colaborativo. Tais estratégias têm sido enquadradas no conceito mais geral de Rede Colaborativa (RC), onde organizações e indivíduos trabalham juntos para aumentar o acesso a novas oportunidades de negócio, compartilhar riscos, reduzir custos e atingir metas que seriam inalcançáveis individualmente. A implantação de RCs depende da existência de infraestruturas computacionais que provejam funcionalidades de suporte à colaboração, incluindo compartilhamento e busca de informações, integração de sistemas, gestão de segurança, entre outros. O foco desta tese está na funcionalidade relacionada à busca de informação, requisito fundamental considerando-se o fato de que os parceiros de tal rede compartilham informações que precisam ser recuperadas. Além disso, a busca de informação se justifica pelo seu uso potencial em diversas outras aplicações necessárias a RCs, tais como: suporte à gestão de conhecimento, seleção de indicadores, busca de parceiros, auxílio no suporte à decisão, entre outras. Nesse sentido, este trabalho propõe um arcabouço que define uma infraestrutura de serviços de suporte à busca de informação em RCs. A estratégia adotada neste trabalho foi dividida em dois passos: em primeiro lugar utilizou-se ontologias para o enriquecimento das fontes de informação, com base na definição de anotações semânticas. Ontologias foram também usadas como base para a definição de consultas semânticas. O segundo passo envolveu a utilização do contexto do usuário visando a melhoria dos resultados da busca. No âmbito de uma RC, o contexto pode ser definido por diversos elementos, incluindo processo, tarefa e papel desempenhado pelo usuário. A abordagem utilizada nessa etapa consistiu na definição de um modelo do contexto, que é associado à ontologia da RC, e de um conjunto de regras que, com base no contexto atual do usuário, efetuam uma expansão na consulta original. Em suma, a abordagem proposta usa o contexto do usuário para sugerir novos tópicos a serem buscados aplicando-se restrições à consulta definida pelo usuário. A avaliação deu-se a partir de experimentos com coleções de teste, onde medidas baseadas em precisão e cobertura foram utilizadas na comparação do modelo proposto com um sistema baseado em palavras-chave e com outro baseado em ontologias