Query Formulation Research Articles

Criteria2Query: a natural language interface to clinical databases for cohort definition.

ObjectiveCohort definition is a bottleneck for conducting clinical research and depends on subjective decisions by domain experts. Data-driven cohort definition is appealing but requires substantial knowledge of terminologies and clinical data models. Criteria2Query is a natural language interface that facilitates human-computer collaboration for cohort definition and execution using clinical databases.Materials and MethodsCriteria2Query uses a hybrid information extraction pipeline combining machine learning and rule-based methods to systematically parse eligibility criteria text, transforms it first into a structured criteria representation and next into sharable and executable clinical data queries represented as SQL queries conforming to the OMOP Common Data Model. Users can interactively review, refine, and execute queries in the ATLAS web application. To test effectiveness, we evaluated 125 criteria across different disease domains from ClinicalTrials.gov and 52 user-entered criteria. We evaluated F1 score and accuracy against 2 domain experts and calculated the average computation time for fully automated query formulation. We conducted an anonymous survey evaluating usability.ResultsCriteria2Query achieved 0.795 and 0.805 F1 score for entity recognition and relation extraction, respectively. Accuracies for negation detection, logic detection, entity normalization, and attribute normalization were 0.984, 0.864, 0.514 and 0.793, respectively. Fully automatic query formulation took 1.22 seconds/criterion. More than 80% (11+ of 13) of users would use Criteria2Query in their future cohort definition tasks.ConclusionsWe contribute a novel natural language interface to clinical databases. It is open source and supports fully automated and interactive modes for autonomous data-driven cohort definition by researchers with minimal human effort. We demonstrate its promising user friendliness and usability.

Unique Analytical Modelling of Secure Communication in Wireless Sensor Network to Resist Maximum Threats

Security problems in Wireless Sensor Network (WSN) are still open-end problems. Qualitative evaluation of the existing approaches of security in WSN shows adoption of either complex cryptographic use or attack-specific solution. As WSN is an integral part of upcoming Internet-of-Things (IoT), the attack scenario becomes more complicated owing to the integration of two different forms of networks and so is for the attackers. Therefore, this paper introduces a novel secure communication technique that considers time, energy, and traffic environment as prominent constraints to perform security modeling. The proposed solution designed using analytical methodology has some unique capability to resist any form of illegitimate queries of network participation and yet maintain a superior form of communication service. The simulated outcome shows that the proposed system offers reduced end-to-end delay and highest energy retention as compared to other existing security approaches.

Can One Improve Now-Casts of Crop Prices in Africa? Google Can.

With increasing Internet user rates across Africa, there is considerable interest in exploring new, online data sources. Particularly, search engine metadata, i.e. data representing the contemporaneous online-interest in a specific topic, has gained considerable interest, due to its potential to extract a near real-time online signal about the current interest of a society. The objective of this study is to analyze whether search engine metadata in the form of Google Search Query (GSQ) data can be used to improve now-casts of maize prices in nine African countries, these are Ethiopia, Kenya, Malawi, Mozambique, Rwanda, Tanzania and Uganda, Zambia and Zimbabwe. We formulate as benchmark an auto-regressive model for each country, which we subsequently augment by two specifications based on contemporary GSQ data. We test the models in in-sample, and in a pseudo out-of-sample, one-step-ahead now-casting environment and compare their forecasting errors. The GSQ specifications improve the now-casting fit in 8 out 9 countries and reduce the now-casting error between 3% and 23%. The largest improvement of maize price now-casts is achieved for Malawi, Kenya, Zambia and Tanzania, with improvements larger than 14%.

Leveraging Linked Open Data to Automatically Answer Arabic Questions

The interchangeably connected Web technologies and the advancements that accompany the semantic web content’s leaps, have raised many challenges in the results’ retrieval process especially for the Arabic Language. This research targets an important, yet insufficiently precedent, area in using Linked Open Data (LOD) for Automatic Question Answering systems in the Arabic Language. The significance of work presented, comes from its ability to overcome many challenges in querying Arabic content. Some of these challenges are: (a) bridging the gap between natural language and linked data by mapping users’ queries to a standard semantic web query language such as SPARQL, (b) facilitating multilingual access to semantic data, and (c) maintaining the quality of data. Another challenging aspect was the lack of related work and publicly available resources for Arabic Question Answering Systems over Linked Data, despite the vastly growing Arabic corpus on the web. This paper presents a novel approach that targets Automatic Arabic Questions’ Answering Systems whilst bypassing many featured challenges in the field. A hybrid approach that evaluates the effectiveness of using LOD to automatically answer Arabic questions is developed. The approach is developed to map users’ questions in Modern Standard Arabic, to a standard query language for LOD (i.e. SPARQL) through: (i) extracting entities from questions and linking them over the web using Named-Entity Recognition and Disambiguation (NER/NED), and (ii) extracting properties among extracted named entities using a dependency parsing approach integrated with Wikidata ontology. To evaluate our proposed system, an Arabic questions dataset was created including: (a) Question body in Arabic language, (b) Question type, (c) SPARQL Query formulation, and (d) Question answer. Evaluation results are promising with a Precision of 84%, a Recall of 81.3%, and an F-Measure of 82.8%.

Domain and Schema Independent Semantic Model Verbalization: A Conceptual Overview

Semantic Web-based technologies have become extremely popular and its a success that has spread across many domains, additional to the computer science domain. Nevertheless, the reusability aspects associated with the created and available semantic knowledge models are very low. The main bottleneck associated with this issue is, the difficulty associated in understanding the complex schema of a knowledge model created and barriers associated with querying the knowledge models using SPARQL or SQWRL query formulations. This research emphasizes on proposing a verbalizer which can go beyond existing Controlled Natural Language (CNL) type verbalizers and to verbalizer knowledge stored in a knowledge model file written in either RDF or OWL format, despite its domain and schematics.

Selecting and tuning the optimal query form of different SQL commands

Selecting and tuning the optimal query form of different SQL commands

Selecting and tuning the optimal query form of different SQL commands

This paper presents the proposed approach of SQL tuning plan and tool in database systems based on experiments using testing data workload in Oracle 11g XE; to get an optimal execution plan for a given SQL, statements based on process time consumed and statistics about the object related to tables are referenced in the statement. These statistics include the SQL command, running time consumption, tables themselves, their indexes, access methods, and other derived objects. The quality of final execution plan depends primarily on the quality of the information found about the SQL command to improve a poorly-performed query. This work contributes to research knowledge by proposing necessary knowledge and information about database tuning using a useful tool to evaluate SQL commands. The proposed approach helps researchers, database administrators and developers to assess the impact of their decisions in the selection of the optimal SQL statement form. Oracle 11g XE is taken for the experimentation purpose.

SEMANTIC AMBIGUOUS QUERY FORMULATION USING STATISTICAL LINGUISTICS TECHNIQUE

Natural language query systems mitigate the complexity of structured query. Usually, natural language processing is implemented to solve several problems, such as information retrieval. However, problems such as natural language ambiguity remain unsolved due to the complexity of natural language itself. This issue thus requires further research. Recent studies on semantic query formulation have attempted to resolve ambiguous natural language by proposing different disambiguation approaches. Most such processes are either implemented manually or semi-automated. In the same vein, most recent systems solve ambiguity by using an external dictionary such as WordNet or by providing suggestions manually. The present research proposes a statistical linguistic technique for solving the problem of ambiguity automatically. The proposed technique is experimentally tested on a Quran ontology with queries from the Islamic Research Foundation Website and increases the result of precision and recall by 6% and 10%, respectively.

Human Genome-Edited Babies: First Responder with Concerns Regarding Possible Neurological Deficits!

The ultimate outcome in genome-editing research stepped into unknown territories last month when two babies were brought into the world with clustered regularly interspaced short palindromic repeats (CRISPR)-CRISPR-associated protein 9 (Cas9) facilitated knockdown of chemokine receptor 5 (CCR5). An immediate outcry by the public and the scientific community followed, which is still ongoing with much apprehensions and criticism of the ethical and scientific aspects of the procedure and its effects on the future of genome editing needed in other stubborn inheritable diseases for which there is no cure at present. With the debate on the consequences of this particular receptor knockdown still going on and the after-shocks in the form of queries expected to continue for some time in the future, we enter the arena of this particular genome editing as first responders with concerns regarding the neurological aftermath of CCR5 knockout in the babies born.

Understanding collaborative tourism information searching to support online travel planning

PurposeCollaborative information searching is common for people when planning their group trip. However, little research has explored how tourists collaborate during information search. Existing tourism Web portals or search engines rarely support tourists’ collaborative information search activities. Taking advantage of previous studies of collaborative tourism information search behavior, in the current paper the purpose of this paper is to propose the design of a collaborative search system collaborative tourism information search (ColTIS) to support online information search and travel planning.Design/methodology/approachColTIS was evaluated and compared with Google Talk-embedded Tripadvisor.com through a user study involving 18 pairs of participants. The data included pre- and post-search questionnaires, web search logs and chat history. For quantitative measurement, statistical analysis was performed using SPSS; for log data and the qualitative feedback from participants, the content analysis was employed.FindingsResults suggest that collaborative query formulation, division of search tasks, chatting and results sharing are important means to facilitate tourists’ collaborative search. ColTIS was found to outperform Tripadvisor significantly regarding the ease of use, collaborative support and system usefulness.Originality/valueThe innovation of the study lies in the development of an integrated real-time collaborative tourism information search system with unique features. These features include collaborative query reformulation, travel planner and automatic result and query sharing that assist multiple people search for holiday information together. For system designers and tourism practitioners, implications are provided.

On Influential Trends in Interactive Video Retrieval: Video Browser Showdown 2015–2017

The last decade has seen innovations that make video recording, manipulation, storage, and sharing easier than ever before, thus impacting many areas of life. New video retrieval scenarios emerged as well, which challenge the state-of-the-art video retrieval approaches. Despite recent advances in content analysis, video retrieval can still benefit from involving the human user in the loop. We present our experience with a class of interactive video retrieval scenarios and our methodology to stimulate the evolution of new interactive video retrieval approaches. More specifically, the video browser showdown evaluation campaign is thoroughly analyzed, focusing on the years 2015–2017. Evaluation scenarios, objectives, and metrics are presented, complemented by the results of the annual evaluations. The results reveal promising interactive video retrieval techniques adopted by the most successful tools and confirm assumptions about the different complexity of various types of interactive retrieval scenarios. A comparison of the interactive retrieval tools with automatic approaches (including fully automatic and manual query formulation) participating in the TRECVID 2016 ad hoc video search task is discussed. Finally, based on the results of data analysis, a substantial revision of the evaluation methodology for the following years of the video browser showdown is provided.

Temporal specificity-based text classification for information retrieval

Time is an important aspect in temporal information retrieval (TIR), a subfield of information retrieval (IR). Web search engines like Google or Bing are common examples of IR systems. An important constituent of a search engine is news retrieval, where users present their information needs in the form of temporal queries. Users are usually interested in news documents focusing on a particular time period. Existing search engines rarely fulfill the temporal information requirements as they ignore the temporal information available in the content of news documents, also known as document focus time. Furthermore, information related to multiple time periods in a news document makes the identification of document focus time a challenging task. Therefore, it is necessary to classify news documents based on temporal specificity before it is possible to use the temporal information in the retrieval process. In this study, we formulate the temporal specificity problem as a time-based classification task by classifying news documents into three temporal classes, i.e. high temporal specificity, medium temporal specificity, and low temporal specificity. For such classification, rule-based and temporal specificity score (TSS)-based classification approaches are proposed. In the former approach, news documents are classified using a defined set of rules that are based on temporal features. The later approach classifies news documents based on a TSS score using the temporal features. The results of the proposed techniques are compared with four machine learning classification algorithms: Bayes net, support vector machine, random forest, and decision tree. The results show that the proposed rule-based classifier outperforms the four algorithms by achieving 82 % accuracy, whereas TSS classification achieves 77 % accuracy.

Mixed language queries in online searches

PurposeWith the increasing number of online multilingual resources, cross-language information retrieval (CLIR) has drawn much attention from the information retrieval (IR) research community. However, few studies have examined how and why multilingual searchers seek information in two or more languages, specifically how they switch and mix language in queries to get satisfying results. The purpose of this paper is to focus on Chinese–English bilinguals’ intra-sentential code-switching behaviors in online searches. The scenarios and reasons of code-switching, factors that may affect code-switching, the patterns of mixed language query formulation and reformulation and how current IR systems and other search tools can facilitate such information needs were examined.Design/methodology/approachIn-depth semi-structured interviews were used as the research method. In total, 30 participants were recruited based on their English proficiency, location and profession, using a purposive sampling method.FindingsFour scenarios and four reasons for using Chinese–English mixed language queries to cover information needs were identified, and results suggest that linguistic and cultural/social factors are of equivalent importance in code-switching behaviors. English terms and Chinese terms in queries play different roles in searches, and mixed language queries are irreplaceable by either single language queries or other search facilitating features. Findings also suggest current search engines and tools need greater emphasis in the user interface and more user education is required.Originality/valueThis study presents a qualitative analysis of bilinguals’ code-switching behaviors in online searches. Findings are expected to advance the theoretical understanding of bilingual users’ search strategies and interactions with IR systems, and provide insights for designing more effective IR systems and tools to discover multilingual online resources, including cross-language controlled vocabularies, personalized CLIR tools and mixed language query assistants.

A heuristic algorithm for generating the numerical terms of a linguistic variable

In this paper we describe an easy-to-implement algorithm for automatedgeneration of the linguistic variable term membership functions to allow for information search in a relational database based on qualitative criteria by means of the SQL query language. The proposed algorithm makes it possible to calculate the parameters of the triangular and trapezoid membership functions taking into account the distribution of the variable of interest stored in the database. The algorithm defines the intervals covered by the term bases, so that each interval contains about the same number of values. Upper bounds of the defined intervals are used to calculate the parameters of membership functions. The parameters of the membership functions generated with this algorithm can be easily calculated with the limited computational means of the SQL language. We review the algorithm realizations for the generation of 3 and 5 terms of a linguistic variable based on a sample from a database containing 100 or 500 different values. The membership functions obtained through the algorithm have the required properties of orderliness, completeness, consistency and normality. They do not require further approximation. Unlike the known methods, the algorithm does not require significant computing resources, the use of specialized software, settings configuring, or a training set formation. The algorithm implementation creates opportunities to support fuzzy search queries in relational databases using the means of the SQL language, as limited as they are. Thus, the system’s level of intelligence would be increased, and the user would be provided with the means of search query formulation in a natural language. The linguistic variable terms generated using our algorithm can be used within the framework of a fuzzy rule-based knowledge base of an information system, as well as to perform fuzzy inference.

TOWARDS AN INTELLIGENT PLATFORM FOR BIG 3D GEOSPATIAL DATA MANAGEMENT

Abstract. The use of intelligent technologies within 3D geospatial data analysis and management will decidedly open the door towards efficiency, cost transparency, and on-time schedules in planning processes. Furthermore, the mission of smart cities as a future option of urban development can lead to an environment that provides high-quality life along stable structures. However, neither geospatial information systems nor building information modelling systems seem to be well prepared for this new development. After a review of current approaches and a discussion of their limitations we present our approach on the way to an intelligent platform for the management and analysis of big 3D geospatial data focusing on infrastructure projects such as metro or railway tracks planning. three challenges are presented focusing on the management of big geospatial data with existing geo-database management systems, the integration of heterogeneous data, and the 3D visualization for database query formulation and query results. The approach for the development of a platform for big geospatial data analysis is discussed. Finally, we give an outlook on our future research supporting intelligent 3D city applications in the United Arab Emirates.

OptiqueVQS: A visual query system over ontologies for industry

An important application of semantic technologies in industry has been the formalisation of information models using OWL 2 ontologies and the use of RDF for storing and exchanging application data. Moreover, legacy data can be virtualised as RDF using ontologies following the ontology-based data access (OBDA) approach. In all these applications, it is important to provide domain experts with query formulation tools for expressing their information needs in terms of queries over ontologies. In this work, we present such a tool, OptiqueVQS, which is designed based on our experience with OBDA applications in Statoil and Siemens and on best HCI practices for interdisciplinary engineering environments. OptiqueVQS implements a number of unique techniques distinguishing it from analogous query formulation systems. In particular, it exploits ontology projection techniques to enable graph-based navigation over an ontology during query construction. Secondly, while OptiqueVQS is primarily ontology driven, it exploits sampled data to enhance selection of data values for some data attributes. Finally, OptiqueVQS is built on well-grounded requirements, design rationale, and quality attributes. We evaluated OptiqueVQS with both domain experts and casual users and qualitatively compared our system against prominent visual systems for ontology-driven query formulation and exploration of semantic data. OptiqueVQS is available online and can be downloaded together with an example OBDA scenario.

Errors and Complications in SQL Query Formulation

SQL is taught in almost all university level database courses, yet SQL has received relatively little attention in educational research. In this study, we present a database management system independent categorization of SQL query errors that students make in an introductory database course. We base the categorization on previous literature, present a class of logical errors that has not been studied in detail, and review and complement these findings by analyzing over 33,000 SQL queries submitted by students. Our analysis verifies error findings presented in previous literature and reveals new types of errors, namely logical errors recurring in similar manners among different students. We present a listing of fundamental SQL query concepts we have identified and based our exercises on, a categorization of different errors and complications, and an operational model for designing SQL exercises.

Children's query types and reformulations in Google search

We investigated the searching behaviors of twenty-four children in grades 6, 7, and 8 (ages 11–13) in finding information on three types of search tasks in Google. Children conducted 72 search sessions and issued 150 queries. Children's phrase- and question-like queries combined were much more prevalent than keyword queries (70% vs. 30%, respectively). Fifty two percent of the queries were reformulations (33 sessions). We classified children's query reformulation types into five classes based on the taxonomy by Liu et al. (2010). We found that most query reformulations were by Substitution and Specialization, and that children hardly repeated queries. We categorized children's queries by task facets and examined the way they expressed these facets in their query formulations and reformulations. Oldest children tended to target the general topic of search tasks in their queries most frequently, whereas younger children expressed one of the two facets more often. We assessed children's achieved task outcomes using the search task outcomes measure we developed. Children were mostly more successful on the fact-finding and fully self-generated task and partially successful on the research-oriented task. Query type, reformulation type, achieved task outcomes, and expressing task facets varied by task type and grade level. There was no significant effect of query length in words or of the number of queries issued on search task outcomes. The study findings have implications for human intervention, digital literacy, search task literacy, as well as for system intervention to support children's query formulation and reformulation during interaction with Google.

Which shirt for my first date? Towards a flexible attribute-based fashion query system

In this paper, we present a query & retrieval system specially designed for upper-body clothing products such as Shirts, Jackets etc. To facilitate precise learning, each clothing product image is divided into its constituent parts (e.g. torso and sleeves) to enable the learning of the respective attributes of the parts. The regions associated with the constituent clothing parts are first identified by an algorithm which exploits the structure of the upper-body clothing items. Our proposed “Part Guided” Convolutional Neural Network (CNN) is then “steered” towards different regions according to the attributes and expedite the training. In order to extract compact color information and enable flexible query formulation in a richer space, a Markov Random Field based color encoding method is adopted. A detailed dataset called “Shirt Attributes Dataset” was constructed to evaluate the performance of the proposed system. The proposed system allows more specific query formulations and achieves better results compared to different baselines in terms of classification and retrieval accuracy.

Course Recommendation Based on Query Classification Approach

This article describes how with a non-formal education, a scholar has to choose courses among various domains to meet the research aims. In spite of this, the availability of large number of courses, makes the process of selecting the appropriate course a tedious, time-consuming, and risky decision, and the course selection will directly affect the performance of a scholar. The best approach to solve such problems and to produce desirable results is to use a “recommendation system.” Recommender systems at the core employ information retrieval techniques and the ongoing effort of such information retrieval systems is to deliver the most relevant information to the learner. Therefore, if a recommender system is able to recognize the intent and requirements that a user express in the form of queries, it can generate more valid recommendations. This article presents an N-Gram classification technique which can be used to generate course recommendations to scholars depend on the requirements and domain of interest. This way of personalization can improve the quality of research and learning experience by recommending courses which are otherwise overlooked by scholars, as it takes the time to go through the curriculum and finding the best possible match.