Query Templates Research Articles

User-centric design of a 3D search interface for protein-ligand complexes

In this work, we present the frontend of GeoMine and showcase its application, focusing on the new features of its latest version. GeoMine is a search engine for ligand-bound and predicted empty binding sites in the Protein Data Bank. In addition to its basic text-based search functionalities, GeoMine offers a geometric query type for searching binding sites with a specific relative spatial arrangement of chemical features such as heavy atoms and intermolecular interactions. In contrast to a text search that requires simple and easy-to-formulate user input, a 3D input is more complex, and its specification can be challenging for users. GeoMine’s new version aims to address this issue from the graphical user interface perspective by introducing an additional visualization concept and a new query template type. In its latest version, GeoMine extends its query-building capabilities primarily through input formulation in 2D. The 2D editor is fully synchronized with GeoMine’s 3D editor and provides the same functionality. It enables template-free query generation and template-based query selection directly in 2D pose diagrams. In addition, the query generation with the 3D editor now supports predicted empty binding sites for AlphaFold structures as query templates. GeoMine is freely accessible on the ProteinsPlus web server (https://proteins.plus).

HIHISIV: a database of gene expression in HIV and SIV host immune response

In the battle of the host against lentiviral pathogenesis, the immune response is crucial. However, several questions remain unanswered about the interaction with different viruses and their influence on disease progression. The simian immunodeficiency virus (SIV) infecting nonhuman primates (NHP) is widely used as a model for the study of the human immunodeficiency virus (HIV) both because they are evolutionarily linked and because they share physiological and anatomical similarities that are largely explored to understand the disease progression. The HIHISIV database was developed to support researchers to integrate and evaluate the large number of transcriptional data associated with the presence/absence of the pathogen (SIV or HIV) and the host response (NHP and human). The datasets are composed of microarray and RNA-Seq gene expression data that were selected, curated, analyzed, enriched, and stored in a relational database. Six query templates comprise the main data analysis functions and the resulting information can be downloaded. The HIHISIV database, available at https://hihisiv.github.io, provides accurate resources for browsing and visualizing results and for more robust analyses of pre-existing data in transcriptome repositories.

Urban Visual Localization of Block-Wise Monocular Images with Google Street Views

Urban visual localization is the process of determining the pose (position and attitude) of the imaging sensor (or platform) with the help of existing geo-referenced data. This task is critical and challenging for many applications, such as autonomous navigation, virtual and augmented reality, and robotics, due to the dynamic and complex nature of urban environments that may obstruct Global Navigation Satellite Systems (GNSS) signals. This paper proposes a block-wise matching strategy for urban visual localization by using geo-referenced Google Street View (GSV) panoramas as the database. To determine the pose of the monocular query images collected from a moving vehicle, neighboring GSVs should be found to establish the correspondence through image-wise and block-wise matching. First, each query image is semantically segmented and a template containing all permanent objects is generated. The template is then utilized in conjunction with a template matching approach to identify the corresponding patch from each GSV image within the database. Through the conversion of the query template and corresponding GSV patch into feature vectors, their image-wise similarity is computed pairwise. To ensure reliable matching, the query images are temporally grouped into query blocks, while the GSV images are spatially organized into GSV blocks. By using the previously computed image-wise similarities, we calculate a block-wise similarity for each query block with respect to every GSV block. A query block and its corresponding GSV blocks of top-ranked similarities are then input into a photogrammetric triangulation or structure from motion process to determine the pose of every image in the query block. A total of three datasets, consisting of two public ones and one newly collected on the Purdue campus, are utilized to demonstrate the performance of the proposed method. It is shown it can achieve a meter-level positioning accuracy and is robust to changes in acquisition conditions, such as image resolution, scene complexity, and the time of day.

OEQA: Knowledge- and Intention-Driven Intelligent Ocean Engineering Question-Answering Framework

The constantly updating big data in the ocean engineering domain has challenged the traditional manner of manually extracting knowledge, thereby underscoring the current absence of a knowledge graph framework in such a special field. This paper proposes a knowledge graph framework to fill the gap in the knowledge management application of the ocean engineering field. Subsequently, we propose an intelligent question-answering framework named OEQA based on an ocean engineering-oriented knowledge graph. Firstly, we define the ontology of ocean engineering and adopt a top-down approach to construct a knowledge graph. Secondly, we collect and analyze the data from databases, websites, and textual reports. Based on these collected data, we implement named entity recognition on the unstructured data and extract corresponding relations between entities. Thirdly, we propose an intent-recognizing-based user question classification method, and according to the classification result, construct and fill corresponding query templates by keyword matching. Finally, we use T5-Pegasus to generate natural answers based on the answer entities queried from the knowledge graph. Experimental results show that the accuracy in finding answers is 89.6%. OEQA achieves in the natural answer generation in the ocean engineering domain significant improvements in relevance (1.0912%), accuracy (4.2817%), and practicability (3.1071%) in comparison to ChatGPT.

A template-based approach for question answering over knowledge bases

In this paper, we address the problem of answering complex questions formulated by users in natural language. Since traditional information retrieval systems are not suitable for complex questions, these questions are usually run over knowledge bases, such as Wikidata or DBpedia. We propose a semi-automatic approach for transforming a natural language question into a SPARQL query that can be easily processed over a knowledge base. The approach applies classification techniques to associate a natural language question with a proper query template from a set of predefined templates. The nature of our approach is semi-automatic as the query templates are manually written by human assessors, who are the experts of the knowledge bases, whereas the classification and query processing steps are completely automatic. Our experiments on the large-scale CSQA dataset for question-answering corroborate the effectiveness of our approach.

FEBench: A Benchmark for Real-Time Relational Data Feature Extraction

As the use of online AI inference services rapidly expands in various applications (e.g., fraud detection in banking, product recommendation in e-commerce), real-time feature extraction (RTFE) systems have been developed to compute the requested features from incoming data tuples in ultra-low latency. Similar to relational databases, these RTFE procedures can be expressed using SQL-like languages. However, there is a lack of research on the workload characteristics and specialized benchmarks for RTFE, especially in comparison with existing database workloads and benchmarks (e.g., concurrent transactions in TPC-C). In this paper, we study the RTFE workload characteristics using over one hundred real datasets from open repositories (e.g. Kaggle, Tianchi, UCI ML, KiltHub) and those from 4Paradigm. The study highlights the significant differences between RTFE workloads and existing database benchmarks in terms of application scenarios, operator distributions, and query structures. Based on these findings, we propose to develop a realtime feature extraction benchmark named FEBench based on the four important criteria for a domain-specific benchmark proposed by Jim Gray. FEBench consists of selected representative datasets, query templates, and an online request simulator. We use FEBench to evaluate the effectiveness of feature extraction systems including OpenMLDB and Flink and find that each system exhibits distinct advantages and limitations in terms of overall latency, tail latency, and concurrency performance.

Civil Aviation Travel Question and Answer Method Using Knowledge Graphs and Deep Learning

In this paper, a civil aviation travel question and answer (Q&A) method based on integrating knowledge graphs and deep learning technology is proposed to establish a highly efficient travel information Q&A platform and quickly and accurately obtain question information and give corresponding answers to passengers. In the proposed method, a rule-based approach is employed to extract triads from the acquired civil aviation travel dataset to construct a civil aviation travel knowledge graph. Then, the ELECTRA-BiLSTM-CRF model is constructed to recognize the entity, and an improved ALBERT-TextCNN model is used for intent classification. Finally, Cypher query templates are transformed into Cypher query statements and retrieved in the Neo4j database, and the query returns the result, which realizes a new civil aviation travel Q&A method. A self-built civil aviation dataset is selected to prove the effectiveness of the proposed method. The experimental results show that the proposed method based on integrating knowledge graphs and deep learning technology can achieve better Q&A results, and it has better generalization and high accuracy.

T-Rex: Optimizing Pattern Search on Time Series

Pattern search is an important class of queries for time series data. Time series patterns often match variable-length segments with a large search space, thereby posing a significant performance challenge. The existing pattern search systems, for example, SQL query engines supporting MATCH_RECOGNIZE, are ineffective in pruning the large search space of variable-length segments. In many cases, the issue is due to the use of a restrictive query language modeled on time series points and a computational model that limits search space pruning. We built T-ReX to address this problem using two main building blocks: first, a MATCH_RECOGNIZE language extension that exposes the notion of segment variable and adds new operators, lending itself to better optimization; second, an executor capable of pruning the search space of matches and minimizing total query time using an optimizer. We conducted experiments using 5 real-world datasets and 11 query templates, including those from existing works. T-ReX outperformed an optimized NFA-based pattern search executor by 6x in median query time and an optimized tree-based executor by 19X.

Template-Based Question Answering System Over the Semantic Web

A question answering system is the most promising way of retrieving data from the available knowledge base to the end-users to get the appropriate result for their questions. Many question answering systems convert the questions into triples which are mapped to the knowledge base from which the answer is derived. However, these triples do not express the semantic representation of the question, due to which the answers cannot be located. To handle this, a template-based approach is proposed that classifies the question types and finds appropriate SPARQL query template for each type including comparatives and superlatives. The SPARQL query built is executed in the DBpedia endpoint and results are obtained. Compared with other factoid question answering systems, the proposed approach has the potential to deal with a large number of question types, including comparatives and superlatives. Also, the experimental evaluations of the system performed on the QALD 8 dataset presents good performance and can help users to find answers to their questions.

Resident integration with inpatient clinical documentation improvement: a quality improvement project

BackgroundClinical documentation improvement (CDI) is an increasing part of health system quality and patient care with clinical documentation integrity specialists (CDIS) expanding into daily physician workflow. This integration can be...

Automatic question generation with various difficulty levels based on knowledge ontology using a query template

Ontology is a concepts and relationships that can be used to support the question-generation process. However, until now, the ontology models and question templates commonly used to support the question-generation process have remained domain-specific, allowing three weaknesses to persist. First, the role of experts is dominant in the process of ontology generation. Second, the process needs adjustment if it is to be used for other domains. Third, question templates are formed based on the vocabulary of ontology, so they cannot be used to generate questions in other domains. In response to these problems, this research focused on forming an ontology generation model and a template model for generating questions that are not domain-specific. We used a combination of two types of ontology — namely, taxonomy ontology and sentence ontology to form ontology models and question templates that were not domain-specific. We labeled this combination as “knowledge ontology”. We used template queries to retrieve information on the ontology and then translated the results of the query template into questions in natural language. The ratios from our experiments demonstrated that the proposed method was effective for generating questions. Moreover, the method produced good question quality, as evidenced by its high accuracy rate of 90.71%. This research can be applied to help e-learning developers represent information in the form of ontology without involving experts. Furthermore, this research can also help teachers to generate questions automatically with consistent question quality.

Template based Question Answering System Over Semantic Web

Question Answering system is the most promising way of retrieving data from the available knowledge base to the end-users, to get the appropriate result for their questions. Many Question Answering systems convert the questions into triples which are mapped to the Knowledge base from which answer is derived. However, these triples do not express the semantic representation of the question, due to which the answers cannot be located. To handle this, a template-based approach is proposed which classifies the question types and finds appropriate SPARQL query template for each type including comparatives and superlatives. The SPARQL query built is executed in the DBpedia endpoint and results are obtained. Compared with other factoid question answering systems, the proposed approach has the potential to deal with a large number of question types, including comparatives and superlatives. Also, the experimental evaluations of the system performed on the QALD 8 dataset presents good performance and can help users to find answers to their questions.

Prostate cancer management with lifestyle intervention: From knowledge graph to Chatbot

AbstractBackgroundPersonal lifestyle is an important cause of prostate cancer (PCa), hence establishing a corresponding knowledge graph (KG) and a chatbot is a convenient way for preventing and assessing risks. The chatbot based on a KG of PCa‐associated lifestyles will be helpful to PCa management, then save health care resources in the ageing society.ResultsBased on our established knowledge base, we define entities and corresponding relationships to construct the PCa‐associated lifestyles KG for visualization by importing the triples into the Neo4j graph server. The dialogue system uses the Flask framework to determine the classification of questions through entity recognition and relationship extraction and later uses the query template to search the answers from the PCa‐associated lifestyles KG. The PCa‐associated lifestyles KG contains 11 types of entities and 14 types of relationships, the total number of nodes and links is 21 546 and 66 493, respectively. Also, the entity “Lifestyle”, “Paper”, “Baseline” and “Outcome” contain multiple attributes. The established chatbot can answer 12 types of basic questions and predict the probability of a certain lifestyle resulting in a certain PCa. The chatbot is available at http://sysbio.org.cn:5000/Pca/chatbot.ConclusionA chatbot based on PCa‐associated lifestyles KG was constructed to help researchers, physicians or patients learn more about PCa lifestyle management interactively.

SGPT: A Generative Approach for SPARQL Query Generation From Natural Language Questions

SPARQL query generation from natural language questions is complex because it requires an understanding of both the question and the underlying knowledge graph (KG) patterns. Most SPARQL query generation approaches are template-based, tailored to a specific knowledge graph and require pipelines with multiple steps, including entity and relation linking. Template-based approaches are also difficult to adapt for new KGs and require manual efforts from domain experts to construct query templates. To overcome this hurdle, we propose a new approach, dubbed SGPT, that combines the benefits of end-to-end and modular systems and leverages recent advances in large-scale language models. Specifically, we devise a novel embedding technique that can encode linguistic features from the question which enables the system to learn complex question patterns. In addition, we propose training techniques that allow the system to implicitly employ the graph-specific information (i.e., entities and relations) into the language model’s parameters and generate SPARQL queries accurately. Finally, we introduce a strategy to adapt standard automatic metrics for evaluating SPARQL query generation. A comprehensive evaluation demonstrates the effectiveness of SGPT over state-of-the-art methods across several benchmark datasets.

GOMS: Large‐scale ontology management system using graph databases

Large-scale ontology management is one of the main issues when using ontology data practically. Although many approaches have been proposed in relational database management systems (RDBMSs) or object-oriented DBMSs (OODBMSs) to develop large-scale ontology management systems, they have several limitations because ontology data structures are intrinsically different from traditional data structures in RDBMSs or OODBMSs. In addition, users have difficulty using ontology data because many terminologies (ontology nodes) in large-scale ontology data match with a given string keyword. Therefore, in this study, we propose a (graph database-based ontology management system (GOMS) to efficiently manage large-scale ontology data. GOMS uses a graph DBMS and provides new query templates to help users find key concepts or instances. Furthermore, to run queries with multiple joins and path conditions efficiently, we propose GOMS encoding as a filtering tool and develop hash-based join processing algorithms in the graph DBMS. Finally, we experimentally show that GOMS can process various types of queries efficiently.

DSB

We describe a new benchmark, DSB, for evaluating both workload-driven and traditional database systems on modern decision support workloads. DSB is adapted from the widely-used industrial-standard TPC-DS benchmark. It enhances the TPC-DS benchmark with complex data distribution and challenging yet semantically meaningful query templates. DSB also introduces configurable and dynamic workloads to assess the adaptability of database systems. Since workload-driven and traditional database systems have different performance dimensions, including the additional resources required for tuning and maintaining the systems, we provide guidelines on evaluation methodology and metrics to report. We show a case study on how to evaluate both workload-driven and traditional database systems with the DSB benchmark. The code for the DSB benchmark is open sourced and is available at https://aka.ms/dsb.

TREE-BASED SEMANTIC ANALYSIS METHOD FOR NATURAL LANGUAGE PHRASE TO FORMAL QUERY CONVERSION

Context. This work is devoted to the problem of natural language interface construction for ontological graph databases. The focus here is on the methods for the conversion of natural language phrases into formal queries in SPARQL and CYPHER query languages. Objective. The goals of the work are the creation of a semantic analysis method for the input natural language phrases semantic type determination and obtaining meaningful entities from them for query template variables initialization, construction of flexible query templates for the types, development of program implementation of the proposed technique. Method. A tree-based method was developed for semantic determination of a user’s phrase type and obtaining a set of terms from it to put them into certain places of the most suiting formal query template. The proposed technique solves the tasks of the phrase type determination (and this is the criterion of the formal query template selection) and obtaining meaningful terms, which are to initialize variables of the chosen template. In the current work only interrogative and incentive user’s phrases are considered i.e. ones that clearly propose the system to answer or to do something. It is assumed that the considered dialog or reference system uses a graph ontological database, which directly impacts the formal query patterns – the resulting queries are destined to be in SPARQL or Cypher query languages. The semantic analysis examples considered in this work are aimed primarily at inflective languages, especially, Ukrainian and Russian, but the basic principles could be suitable to most of the other languages. Results. The developed method of natural language phrase to a formal query in SPARQL and CYPHER conversion has been implemented in software for Ukrainian and Norwegian languages using narrow subjected ontologies and tested against formal performance criteria. Conclusions. The proposed method allows the dialog system fast and with minimum number of steps to select the most suitable query template and extract informative entities from a natural language phrase given the huge phrase variability in inflective languages. Carried out experiments have shown high precision and reliability of the constructed system and its potential for practical usage and further development.

Flow-loss

Recently there has been significant interest in using machine learning to improve the accuracy of cardinality estimation. This work has focused on improving average estimation error, but not all estimates matter equally for downstream tasks like query optimization. Since learned models inevitably make mistakes, the goal should be to improve the estimates that make the biggest difference to an optimizer. We introduce a new loss function, Flow-Loss, for learning cardinality estimation models. Flow-Loss approximates the optimizer's cost model and search algorithm with analytical functions, which it uses to optimize explicitly for better query plans. At the heart of Flow-Loss is a reduction of query optimization to a flow routing problem on a certain "plan graph", in which different paths correspond to different query plans. To evaluate our approach, we introduce the Cardinality Estimation Benchmark (CEB) which contains the ground truth cardinalities for sub-plans of over 16 K queries from 21 templates with up to 15 joins. We show that across different architectures and databases, a model trained with Flow-Loss improves the plan costs and query runtimes despite having worse estimation accuracy than a model trained with Q-Error. When the test set queries closely match the training queries, models trained with both loss functions perform well. However, the Q-Error-trained model degrades significantly when evaluated on slightly different queries (e.g., similar but unseen query templates), while the Flow-Loss-trained model generalizes better to such situations, achieving 4 -- 8× better 99th percentile runtimes on unseen templates with the same model architecture and training data.

LODQuMa: A Free-ontology process for Linked (Open) Data quality management

For many years, data quality is among the most commonly discussed issue in Linked Open Data (LOD) due to the huge volume of integrated datasets that are usually heterogeneous. Several ontology-based approaches dealing with quality problems have been proposed. However, when datasets lack a well-defined schema, these approaches become ineffective because of the lack of metadata. Moreover, the detection of quality problems based on an analysis between RDF (Resource Description Framework) triples without requiring ontology statistical and semantical information is not addressed. Keeping in mind that ontologies are not always available and they may be incomplete or misused. In this paper, a novel free-ontology process called LODQuMa is proposed to assess and improve the quality of LOD. It is mainly based on profiling statistics, synonym relationships between predicates, QVCs (Quality Verification Cases), and SPARQL (SPARQL Protocol and RDF Query Language) query templates. Experiments on the DBpedia dataset demonstrate that the proposed process is effective for increasing the intrinsic quality dimensions, resulting in correct and compact datasets.

RetIS: Unique Identification System of Goats through Retinal Analysis

Developing an accurate, reliable and tamper-proof unique identification system for identifying individual animals is one of the most important but challenging tasks in intelligent livestock management. Currently, unique identification for animals is done using methods like stenciling, ear tagging, ear notching, branding, etc. which create opportunities for the poachers, middle men and animal thieves to manipulate the tags and transfer animals across international borders, claim false insurance, poach for their meat, bones, etc. illegally. This becomes a huge headache for the livestock owners. In this paper, a novel biometric unique identification system called “RetIS” has been developed based on goat retinal image which is unique, tamper-proof, reliable and accurate. The identification system has been developed based on the origination and distribution pattern of the blood vessels through a circular ring around the optic disc, which remains constant for the entire lifetime of the goat. Goat retinal images have been captured using a handheld fundus camera. The captured images are then segmented, normalized and encoded for the creation of templates, which are eventually stored in a database for matching with query templates based on hamming distance criteria. In matching, it has been observed that if the hamming distance between stored and query template is less than equal to 0.37 (matching >= 63%), it can be confirmed that the stored and query template belongs to the same individual goat providing its unique identification, otherwise the templates belong to two separate individual goats. This model has been tested with more than 200 retinal images obtained from twelve different goats producing 99% accuracy. The performance of this proposed model has been compared with other animal identification technologies and is found to be the most accurate and precise.