Rethinking ‘Advanced Search’: A New Approach to Complex Query Formulation

Knowledge workers such as patent agents, recruiters and media monitoring professionals undertake work tasks where search forms a core part of their duties. In these instances, the search task often involves the formulation of complex queries expressed as Boolean strings. However, creating effective Boolean queries remains an ongoing challenge, often compromised by errors and inefficiencies. In this demo paper, we present a new approach to query formulation in which concepts are expressed on a two-dimensional canvas and relationships are articulated using direct manipulation. This has the potential to eliminate many sources of error, makes the query semantics more transparent, and offers new opportunities for query refinement and optimisatio

Search strategy formulation for systematic reviews: Issues, challenges and opportunities

Systematic literature reviews play a vital role in identifying the best available evidence for health and social care research, policy, and practice. The resources required to produce systematic reviews can be significant, and a key to the success of any review is the search strategy used to identify relevant literature. However, the methods used to construct search strategies can be complex, time consuming, resource intensive and error prone. In this review, we examine the state of the art in resolving complex structured information needs, focusing primarily on the healthcare context. We analyse the literature to identify key challenges and issues and explore appropriate solutions and workarounds. From this analysis we propose a way forward to facilitate trust and to aid explainability and transparency, reproducibility and replicability through a set of key design principles for tools to support the development of search strategies in systematic literature reviews

Three-dimensional information retrieval (3DIR): A graph theoretic formulation for exploiting 3D geometry and model topology in information retrieval

The 3DIR project investigated the use of 3D visualization to formulate queries, compute the relevance of information items, and visualize search results. Workshops identified the user needs. Based on these, a graph theoretic formulation was created to inform the emerging system architecture. A prototype was developed. This enabled relationships between 3D objects to be used to widen a search. An evaluation of the prototype demonstrated that a tight coupling between text-based retrieval and 3D models could enhance information retrieval but add an extra layer of complexity

Twenty-five years of end-user searching, Part 2: Future research directions

This is the second part of a two-part article that examines 25 years of published research findings on end-user searching of online information retrieval (IR) systems. In Part 1 (Markey, 2007 ), it was learned that people enter a few short search statements into online IR systems. Their searches do not resemble the systematic approach of expert searchers who use the full range of IR-system functionality. Part 2 picks up the discussion of research findings about end-user searching in the context of current information retrieval models. These models demonstrate that information retrieval is a complex event, involving changes in cognition, feelings, and/or events during the information seeking process. The author challenges IR researchers to design new studies of end-user searching, collecting data not only on system-feature use, but on multiple search sessions and controlling for variables such as domain knowledge expertise and expert system knowledge. Because future IR systems designers are likely to improve the functionality of online IR systems in response to answers to the new research questions posed here, the author concludes with advice to these designers about retaining the simplicity of online IR system interfaces.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/56094/1/20601_ftp.pd

CHORUS Deliverable 2.1: State of the Art on Multimedia Search Engines

Based on the information provided by European projects and national initiatives related to multimedia search as well as domains experts that participated in the CHORUS Think-thanks and workshops, this document reports on the state of the art related to multimedia content search from, a technical, and socio-economic perspective. The technical perspective includes an up to date view on content based indexing and retrieval technologies, multimedia search in the context of mobile devices and peer-to-peer networks, and an overview of current evaluation and benchmark inititiatives to measure the performance of multimedia search engines. From a socio-economic perspective we inventorize the impact and legal consequences of these technical advances and point out future directions of research

Leveraging Crowdsourcing Data For Deep Active Learning - An Application: Learning Intents in Alexa

This paper presents a generic Bayesian framework that enables any deep learning model to actively learn from targeted crowds. Our framework inherits from recent advances in Bayesian deep learning, and extends existing work by considering the targeted crowdsourcing approach, where multiple annotators with unknown expertise contribute an uncontrolled amount (often limited) of annotations. Our framework leverages the low-rank structure in annotations to learn individual annotator expertise, which then helps to infer the true labels from noisy and sparse annotations. It provides a unified Bayesian model to simultaneously infer the true labels and train the deep learning model in order to reach an optimal learning efficacy. Finally, our framework exploits the uncertainty of the deep learning model during prediction as well as the annotators' estimated expertise to minimize the number of required annotations and annotators for optimally training the deep learning model. We evaluate the effectiveness of our framework for intent classification in Alexa (Amazon's personal assistant), using both synthetic and real-world datasets. Experiments show that our framework can accurately learn annotator expertise, infer true labels, and effectively reduce the amount of annotations in model training as compared to state-of-the-art approaches. We further discuss the potential of our proposed framework in bridging machine learning and crowdsourcing towards improved human-in-the-loop systems

A Beginner\u27s Guide to Research Using Electronic Health Record Data

Since the passage of the American Recovery and Reinvestment Act in 2009, the use of Electronic Health Records (EHRs) in the healthcare system has increased substantially. Accompanying this surge in EHR usage is a surge in healthcare data and increased opportunities to improve our understanding of health care through research using these data. The use of EHR data for research has many benefits, limitations and considerations. Using data that was originally intended to facilitate billing, insurance, and maintenance of clinical records for research can be fraught with challenges, but they can also be a rich source of information. This paper addresses some of these benefits and challenges, along with additional considerations, including ensuring the best quality data, selecting a good study design, tailoring research questions and queries to available data, and understanding ethical issues in using patient data for research. Researchers should develop a clear understanding of the pitfalls inherent in EHR research before beginning a project. As is the case with most research, many of the drawbacks can be reduced with careful preparation, formulation of a research question, procedures and data management