Automatic Suggestion for PubMed Query Reformulation

Tuan, Luu Anh; Kim, Jung‐Jae

doi:10.5626/jcse.2012.6.2.161

Cited by 4 publications

(1 citation statement)

References 16 publications

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“…Log files are very valuable resources from real users’ information to generate query suggestions [ 8 ]. However, log files are privacy protected and very hard to get from public sources in a reasonable size and constantly updated.…”

Section: Introductionmentioning

confidence: 99%

LAILAPS-QSM: A RESTful API and JAVA library for semantic query suggestions

Chen¹,

Scholz²,

Zhou³

et al. 2018

PLoS Comput Biol

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In order to access and filter content of life-science databases, full text search is a widely applied query interface. But its high flexibility and intuitiveness is paid for with potentially imprecise and incomplete query results. To reduce this drawback, query assistance systems suggest those combinations of keywords with the highest potential to match most of the relevant data records. Widespread approaches are syntactic query corrections that avoid misspelling and support expansion of words by suffixes and prefixes. Synonym expansion approaches apply thesauri, ontologies, and query logs. All need laborious curation and maintenance. Furthermore, access to query logs is in general restricted. Approaches that infer related queries by their query profile like research field, geographic location, co-authorship, affiliation etc. require user’s registration and its public accessibility that contradict privacy concerns. To overcome these drawbacks, we implemented LAILAPS-QSM, a machine learning approach that reconstruct possible linguistic contexts of a given keyword query. The context is referred from the text records that are stored in the databases that are going to be queried or extracted for a general purpose query suggestion from PubMed abstracts and UniProt data. The supplied tool suite enables the pre-processing of these text records and the further computation of customized distributed word vectors. The latter are used to suggest alternative keyword queries. An evaluated of the query suggestion quality was done for plant science use cases. Locally present experts enable a cost-efficient quality assessment in the categories trait, biological entity, taxonomy, affiliation, and metabolic function which has been performed using ontology term similarities. LAILAPS-QSM mean information content similarity for 15 representative queries is 0.70, whereas 34% have a score above 0.80. In comparison, the information content similarity for human expert made query suggestions is 0.90. The software is either available as tool set to build and train dedicated query suggestion services or as already trained general purpose RESTful web service. The service uses open interfaces to be seamless embeddable into database frontends. The JAVA implementation uses highly optimized data structures and streamlined code to provide fast and scalable response for web service calls. The source code of LAILAPS-QSM is available under GNU General Public License version 2 in Bitbucket GIT repository: https://bitbucket.org/ipk_bit_team/bioescorte-suggestion

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Section: Introductionmentioning

confidence: 99%

LAILAPS-QSM: A RESTful API and JAVA library for semantic query suggestions

Chen¹,

Scholz²,

Zhou³

et al. 2018

PLoS Comput Biol

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Topic Classification for Suicidology

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Velldal

Øvrelid

2012

Journal of Computing Science and Engineering

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Computational techniques for topic classification can support qualitative research by automatically applying labels in preparation for qualitative analyses. This paper presents an evaluation of supervised learning techniques applied to one such use case, namely, that of labeling emotions, instructions and information in suicide notes. We train a collection of one-versus-all binary support vector machine classifiers, using cost-sensitive learning to deal with class imbalance. The features investigated range from a simple bag-of-words and n-grams over stems, to information drawn from syntactic dependency analysis and WordNet synonym sets. The experimental results are complemented by an analysis of systematic errors in both the output of our system and the gold-standard annotations.

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A Dynamic Frequency Controlling Technique for Power Management in Existing Commercial Microcontrollers

Lueangvilai¹,

Robertson²,

Martinez

2012

Journal of Computing Science and Engineering

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Power continues to be a driving force in central processing units (CPU) design. Most of the advanced breakthroughs in power have been in a realm that is applicable to workstation CPUs. Advanced power management systems will manage temperature, dynamic voltage scaling and dynamic frequency scaling in a CPU. The use of power management systems for microcontrollers and embedded CPUs has been modest, and mostly focuses on very large scale integration (VLSI) level optimizations compared to system level optimizations. In this paper, a dynamic frequency controlling (DFC) technique is introduced, to lay the foundation of a system level power management system for commercial microcontrollers. The DFC technique allows a commercial microcontroller to have minor modifications on both the hardware and software side, to allow the clock frequency to change to save power; results in this study show a 10% savings. By adding an additional layer of software abstraction at the interrupt level, the microcontroller can operate without having knowledge of the current clock frequency, and this can be accomplished without having to use an embedded operating system.

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Automatic Suggestion for PubMed Query Reformulation

Cited by 4 publications

References 16 publications

LAILAPS-QSM: A RESTful API and JAVA library for semantic query suggestions

LAILAPS-QSM: A RESTful API and JAVA library for semantic query suggestions

Topic Classification for Suicidology

A Dynamic Frequency Controlling Technique for Power Management in Existing Commercial Microcontrollers

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