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

<div><p>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: <a href="https://bitbucket.org/ipk_bit_team/bioescorte-suggestion" target="_blank">https://bitbucket.org/ipk_bit_team/bioescorte-suggestion</a></p></div

Example for word vector representation computed by a feedforward neural network.

<p>The word vector representations estimate the influence of a word in the context of the semantic relationship expressed by the particular word vector. This matrix is a 2 × 4 matrix, representing a vocabulary size of 4 and vector dimensions (number of expected relationships) of 2. The word vector w₁ could represent the relationships “yield” and w₂ “lipid source” respectively.</p

Test set for query suggestion benchmarking. Based on query log analysis five major classes of query can be identified: Trait, biological entity, taxonomy metabolic function.

<p>In the second column, related to each class most frequent query objectives where randomly selected from query log.</p

Different terminations of the false tendon (green) with Purkinje (PK) fibre system (white).

<p>(a) False tendon directly connected to the main PK system, on a left ventricular surface based on triangles with papillary muscle, where the endocardium is red and the epicardium is blue. (b) False tendon terminating in small PK fibre branching, on a left ventricular volume mesh based on tetrahedra with papillary muscle, with myocardium in red.</p

Example of the myocaridal fibre orientation.

<p>Myocardial fibre orientation generated with the Streeter model in one exemplary heart.</p

The three stages of the Purkinje growing algorithm.

<p>Algorithm applied on the left ventricular surface with papillary muscle. (a) First stage with main fibres in blue and endpoints marked by yellow sphere, (b) finer second stage in green and (c) with final branching of the last stage in red.</p

QRS_d prolongation.

<p>In a few cases the QRS duration may be prolonged by adding a false tendon. In these cases (left without false tendon), the last point activated is still reached by the wave front from the right ventricle, but there is an earlier onset of myocardial activation, here seen in the papillary muscle (right with false tendon).</p

QRS Duration as a Function of Left Ventricular Length.

<p>The QRS duration (QRS_d) for different sizes of the left ventricle, were a LBBB and a false tendon to the anterior papillary muscle is present. The improvement between the “LBBB, no FT” and “LBBB with FT”-case is given by QRS_Diff.</p

Manual division of the myocardium by a plane in left and right ventricle.

<p>This division has been used to estimate the activation time in the different ventricles.</p

Histogram of the QRS_d in the study population (n = 70).

<p>(a) Cumulative histogram for all sub groups, (b) Histogram for the healthy, the LBBB, all case with direct connected FT and LBBB and all case with delta connected FT and LBBB. <b>LBBB</b> left bundle branch block, <b>PPM</b> posterior papillary muscle, <b>APM</b> anterior papillary muscle, <b>VFW</b> ventricular free wall, <b>FT</b> false tendon.</p