Deriving query suggestions for site search

Modern search engines have been moving away from simplistic interfaces that aimed at satisfying a user's need with a single-shot query. Interactive features are now integral parts of web search engines. However, generating good query modification suggestions remains a challenging issue. Query log analysis is one of the major strands of work in this direction. Although much research has been performed on query logs collected on the web as a whole, query log analysis to enhance search on smaller and more focused collections has attracted less attention, despite its increasing practical importance. In this article, we report on a systematic study of different query modification methods applied to a substantial query log collected on a local website that already uses an interactive search engine. We conducted experiments in which we asked users to assess the relevance of potential query modification suggestions that have been constructed using a range of log analysis methods and different baseline approaches. The experimental results demonstrate the usefulness of log analysis to extract query modification suggestions. Furthermore, our experiments demonstrate that a more fine-grained approach than grouping search requests into sessions allows for extraction of better refinement terms from query log files. © 2013 ASIS&T

A Hybrid Model for Document Retrieval Systems.

A methodology for the design of document retrieval systems is presented. First, a composite index term weighting model is developed based on term frequency statistics, including document frequency, relative frequency within document and relative frequency within collection, which can be adjusted by selecting various coefficients to fit into different indexing environments. Then, a composite retrieval model is proposed to process a user\u27s information request in a weighted Phrase-Oriented Fixed-Level Expression (POFLE), which may apply more than Boolean operators, through two phases. That is, we have a search for documents which are topically relevant to the information request by means of a descriptor matching mechanism, which incorporate a partial matching facility based on a structurally-restricted relationship imposed by indexing model, and is more general than matching functions of the traditional Boolean model and vector space model, and then we have a ranking of these topically relevant documents, by means of two types of heuristic-based selection rules and a knowledge-based evaluation function, in descending order of a preference score which predicts the combined effect of user preference for quality, recency, fitness and reachability of documents

Classifying complex topics using spatial-semantic document visualization: An evaluation of an interaction model to support open-ended search tasks

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.In this dissertation we propose, test and develop a novel search interaction model to address two key problems associated with conducting an open-ended search task within a classical information retrieval system: (i) the need to reformulate the query within the context of a shifting conception of the problem and (ii) the need to integrate relevant results across a number of separate results sets. In our model the user issues just one highrecall query and then performs a sequence of more focused, distinct aspect searches by browsing the static structured context of a spatial-semantic visualization of this retrieved document set. Our thesis is that unsupervised spatial-semantic visualization can automatically classify retrieved documents into a two-level hierarchy of relevance. In particular we hypothesise that the locality of any given aspect exemplar will tend to comprise a sufficient proportion of same-aspect documents to support a visually guided strategy for focused, same-aspect searching that we term the aspect cluster growing strategy. We examine spatial-semantic classification and potential aspect cluster growing performance across three scenarios derived from topics and relevance judgements from the TREC test collection. Our analyses show that the expected classification can be represented in spatial-semantic structures created from document similarities computed by a simple vector space text analysis procedure. We compare two diametrically opposed approaches to layout optimisation: a global approach that focuses on preserving the all similarities and a local approach that focuses only on the strongest similarities. We find that the local approach, based on a minimum spanning tree of similarities, produces a better classification and, as observed from strategy simulation, more efficient aspect cluster growing performance in most situations, compared to the global approach of multidimensional scaling. We show that a small but significant proportion of aspect clustering growing cases can be problematic, regardless of the layout algorithm used. We identify the characteristics of these cases and, on this basis, demonstrate a set of novel interactive tools that provide additional semantic cues to aid the user in locating same-aspect documents

WHISK: Web Hosted Information into Summarized Knowledge

Today’s online content increases at an alarmingly rate which exceeds users’ ability to consume such content. Modern search techniques allow users to enter keyword queries to find content they wish to see. However, such techniques break down when users freely browse the internet without knowing exactly what they want. Users may have to invest an unnecessarily long time reading content to see if they are interested in it. Automatic text summarization helps relieve this problem by creating synopses that significantly reduce the text while preserving the key points. Steffen Lyngbaek created the SPORK summarization pipeline to solve the content overload in Reddit comment threads. Lyngbaek adapted the Opinosis graph model for extractive summarization and combined it with agglomerative hierarchical clustering and the Smith-Waterman algorithm to perform multi-document summarization on Reddit comments.This thesis presents WHISK as a pipeline for general multi-document text summarization based on SPORK. A generic data model in WHISK allows creating new drivers for different platforms to work with the pipeline. In addition to the existing Opinosis graph model adapted in SPORK, WHISK introduces two simplified graph models for the pipeline. The simplified models removes unnecessary restrictions inherited from Opinosis graph’s abstractive summarization origins. Performance measurements and a study with Digital Democracy compare the two new graph models against the Opinosis graph model. Additionally, the study evaluates WHISK’s ability to generate pull quotes from political discussions as summaries

Contributions to Lifelogging Protection In Streaming Environments

Tots els dies, més de cinc mil milions de persones generen algun tipus de dada a través d'Internet. Per accedir a aquesta informació, necessitem utilitzar serveis de recerca, ja siguin motors de cerca web o assistents personals. A cada interacció amb ells, el nostre registre d'accions, logs, s'utilitza per oferir una millor experiència. Per a les empreses, també són molt valuosos, ja que ofereixen una forma de monetitzar el servei. La monetització s'aconsegueix venent dades a tercers, però, els logs de consultes podrien exposar informació confidencial de l'usuari (identificadors, malalties, tendències sexuals, creences religioses) o usar-se per al que es diu "life-logging ": Un registre continu de les activitats diàries. La normativa obliga a protegir aquesta informació. S'han proposat prèviament sistemes de protecció per a conjunts de dades tancats, la majoria d'ells treballant amb arxius atòmics o dades estructurades. Desafortunadament, aquests sistemes no s'adapten quan es fan servir en el creixent entorn de dades no estructurades en temps real que representen els serveis d'Internet. Aquesta tesi té com objectiu dissenyar tècniques per protegir la informació confidencial de l'usuari en un entorn no estructurat d’streaming en temps real, garantint un equilibri entre la utilitat i la protecció de dades. S'han fet tres propostes per a una protecció eficaç dels logs. La primera és un nou mètode per anonimitzar logs de consultes, basat en k-anonimat probabilística i algunes eines de desanonimització per determinar fuites de dades. El segon mètode, s'ha millorat afegint un equilibri configurable entre privacitat i usabilitat, aconseguint una gran millora en termes d'utilitat de dades. La contribució final es refereix als assistents personals basats en Internet. La informació generada per aquests dispositius es pot considerar "life-logging" i pot augmentar els riscos de privacitat de l'usuari. Es proposa un esquema de protecció que combina anonimat de logs i signatures sanitizables.Todos los días, más de cinco mil millones de personas generan algún tipo de dato a través de Internet. Para acceder a esa información, necesitamos servicios de búsqueda, ya sean motores de búsqueda web o asistentes personales. En cada interacción con ellos, nuestro registro de acciones, logs, se utiliza para ofrecer una experiencia más útil. Para las empresas, también son muy valiosos, ya que ofrecen una forma de monetizar el servicio, vendiendo datos a terceros. Sin embargo, los logs podrían exponer información confidencial del usuario (identificadores, enfermedades, tendencias sexuales, creencias religiosas) o usarse para lo que se llama "life-logging": Un registro continuo de las actividades diarias. La normativa obliga a proteger esta información. Se han propuesto previamente sistemas de protección para conjuntos de datos cerrados, la mayoría de ellos trabajando con archivos atómicos o datos estructurados. Desafortunadamente, esos sistemas no se adaptan cuando se usan en el entorno de datos no estructurados en tiempo real que representan los servicios de Internet. Esta tesis tiene como objetivo diseñar técnicas para proteger la información confidencial del usuario en un entorno no estructurado de streaming en tiempo real, garantizando un equilibrio entre utilidad y protección de datos. Se han hecho tres propuestas para una protección eficaz de los logs. La primera es un nuevo método para anonimizar logs de consultas, basado en k-anonimato probabilístico y algunas herramientas de desanonimización para determinar fugas de datos. El segundo método, se ha mejorado añadiendo un equilibrio configurable entre privacidad y usabilidad, logrando una gran mejora en términos de utilidad de datos. La contribución final se refiere a los asistentes personales basados en Internet. La información generada por estos dispositivos se puede considerar “life-logging” y puede aumentar los riesgos de privacidad del usuario. Se propone un esquema de protección que combina anonimato de logs y firmas sanitizables.Every day, more than five billion people generate some kind of data over the Internet. As a tool for accessing that information, we need to use search services, either in the form of Web Search Engines or through Personal Assistants. On each interaction with them, our record of actions via logs, is used to offer a more useful experience. For companies, logs are also very valuable since they offer a way to monetize the service. Monetization is achieved by selling data to third parties, however query logs could potentially expose sensitive user information: identifiers, sensitive data from users (such as diseases, sexual tendencies, religious beliefs) or be used for what is called ”life-logging”: a continuous record of one’s daily activities. Current regulations oblige companies to protect this personal information. Protection systems for closed data sets have previously been proposed, most of them working with atomic files or structured data. Unfortunately, those systems do not fit when used in the growing real-time unstructured data environment posed by Internet services. This thesis aims to design techniques to protect the user’s sensitive information in a non-structured real-time streaming environment, guaranteeing a trade-off between data utility and protection. In this regard, three proposals have been made in efficient log protection. The first is a new method to anonymize query logs, based on probabilistic k-anonymity and some de-anonymization tools to determine possible data leaks. A second method has been improved in terms of a configurable trade-off between privacy and usability, achieving a great improvement in terms of data utility. Our final contribution concerns Internet-based Personal Assistants. The information generated by these devices is likely to be considered life-logging, and it can increase the user’s privacy risks. The proposal is a protection scheme that combines log anonymization and sanitizable signatures

CHORUS Deliverable 2.2: Second report - identification of multi-disciplinary key issues for gap analysis toward EU multimedia search engines roadmap

After addressing the state-of-the-art during the first year of Chorus and establishing the existing landscape in multimedia search engines, we have identified and analyzed gaps within European research effort during our second year. In this period we focused on three directions, notably technological issues, user-centred issues and use-cases and socio- economic and legal aspects. These were assessed by two central studies: firstly, a concerted vision of functional breakdown of generic multimedia search engine, and secondly, a representative use-cases descriptions with the related discussion on requirement for technological challenges. Both studies have been carried out in cooperation and consultation with the community at large through EC concertation meetings (multimedia search engines cluster), several meetings with our Think-Tank, presentations in international conferences, and surveys addressed to EU projects coordinators as well as National initiatives coordinators. Based on the obtained feedback we identified two types of gaps, namely core technological gaps that involve research challenges, and “enablers”, which are not necessarily technical research challenges, but have impact on innovation progress. New socio-economic trends are presented as well as emerging legal challenges

Promoting understandability in consumer healt information seach

Nowadays, in the area of Consumer Health Information Retrieval, techniques and methodologies are still far from being effective in answering complex health queries. One main challenge comes from the varying and limited medical knowledge background of consumers; the existing language gap be- tween non-expert consumers and the complex medical resources confuses them. So, returning not only topically relevant but also understandable health information to the user is a significant and practical challenge in this area. In this work, the main research goal is to study ways to promote under- standability in Consumer Health Information Retrieval. To help reaching this goal, two research questions are issued: (i) how to bridge the existing language gap; (ii) how to return more understandable documents. Two mod- ules are designed, each answering one research question. In the first module, a Medical Concept Model is proposed for use in health query processing; this model integrates Natural Language Processing techniques into state-of- the-art Information Retrieval. Moreover, aiming to integrate syntactic and semantic information, word embedding models are explored as query expan- sion resources. The second module is designed to learn understandability from past data; a two-stage learning to rank model is proposed with rank aggregation methods applied on single field-based ranking models. These proposed modules are assessed on FIRE’2016 CHIS track data and CLEF’2016-2018 eHealth IR data collections. Extensive experimental com- parisons with the state-of-the-art baselines on the considered data collec- tions confirmed the effectiveness of the proposed approaches: regarding un- derstandability relevance, the improvement is 11.5%, 9.3% and 16.3% in RBP, uRBP and uRBPgr evaluation metrics, respectively; in what concerns to topical relevance, the improvement is 7.8%, 16.4% and 7.6% in P@10, NDCG@10 and MAP evaluation metrics, respectively; Sumário: Promoção da Compreensibilidade na Pesquisa de Informação de Saúde pelo Consumidor Atualmente as técnicas e metodologias utilizadas na área da Recuperação de Informação em Saúde estão ainda longe de serem efetivas na resposta às interrogações colocadas pelo consumidor. Um dos principais desafios é o variado e limitado conhecimento médico dos consumidores; a lacuna lin- guística entre os consumidores e os complexos recursos médicos confundem os consumidores não especializados. Assim, a disponibilização, não apenas de informação de saúde relevante, mas também compreensível, é um desafio significativo e prático nesta área. Neste trabalho, o objetivo é estudar formas de promover a compreensibili- dade na Recuperação de Informação em Saúde. Para tal, são são levantadas duas questões de investigação: (i) como diminuir as diferenças de linguagem existente entre consumidores e recursos médicos; (ii) como recuperar textos mais compreensíveis. São propostos dois módulos, cada um para respon- der a uma das questões. No primeiro módulo é proposto um Modelo de Conceitos Médicos para inclusão no processo da consulta de informação que integra técnicas de Processamento de Linguagem Natural na Recuperação de Informação. Mais ainda, com o objetivo de incorporar informação sin- tática e semântica, são também explorados modelos de word embedding na expansão de consultas. O segundo módulo é desenhado para aprender a com- preensibilidade a partir de informação do passado; é proposto um modelo de learning to rank de duas etapas, com métodos de agregação aplicados sobre os modelos de ordenação criados com informação de campos específicos dos documentos. Os módulos propostos são avaliados nas coleções CHIS do FIRE’2016 e eHealth do CLEF’2016-2018. Comparações experimentais extensivas real- izadas com modelos atuais (baselines) confirmam a eficácia das abordagens propostas: relativamente à relevância da compreensibilidade, obtiveram-se melhorias de 11.5%, 9.3% e 16.3 % nas medidas de avaliação RBP, uRBP e uRBPgr, respectivamente; no que respeita à relevância dos tópicos recupera- dos, obtiveram-se melhorias de 7.8%, 16.4% e 7.6% nas medidas de avaliação P@10, NDCG@10 e MAP, respectivamente