Query Answering System Research Articles

An Interactive Multi-Modal Query Answering System with Retrieval-Augmented Large Language Models

Retrieval-augmented Large Language Models (LLMs) have reshaped traditional query-answering systems, offering unparalleled user experiences. However, existing retrieval techniques often struggle to handle multi-modal query contexts. In this paper, we present an interactive M ulti-modal Q uery A nswering (MQA) system, empowered by our newly developed multi-modal retrieval framework and navigation graph index, integrated with cutting-edge LLMs. It comprises five core components: Data Preprocessing, Vector Representation, Index Construction, Query Execution, and Answer Generation, all orchestrated by a dedicated coordinator to ensure smooth data flow from input to answer generation. One notable aspect of MQA is its utilization of contrastive learning to assess the significance of different modalities, facilitating precise measurement of multimodal information similarity. Furthermore, the system achieves efficient retrieval through our advanced navigation graph index, refined using computational pruning techniques. Another highlight of our system is its pluggable processing framework, allowing seamless integration of embedding models, graph indexes, and LLMs. This flexibility provides users diverse options for gaining insights from their multi-modal knowledge base. A preliminary video introduction of MQA is available at https://youtu.be/xvUuo2ZIqWk.

DeepInsight: a CNN-based approach for machine reading comprehension in query answering systems and its applications

DeepInsight: a CNN-based approach for machine reading comprehension in query answering systems and its applications

The power and potentials of Flexible Query Answering Systems: A critical and comprehensive analysis

The popularity of chatbots, such as ChatGPT, has brought research attention to question answering systems, capable to generate natural language answers to user’s natural language queries. However, also in other kinds of systems, flexibility of querying, including but also going beyond the use of natural language, is an important feature. With this consideration in mind the paper presents a critical and comprehensive analysis of recent developments, trends and challenges of Flexible Query Answering Systems (FQASs). Flexible query answering is a multidisciplinary research field that is not limited to question answering in natural language, but comprises other query forms and interaction modalities, which aim to provide powerful means and techniques for better reflecting human preferences and intentions to retrieve relevant information. It adopts methods at the crossroad of several disciplines among which Information Retrieval (IR), databases, knowledge based systems, knowledge and data engineering, Natural Language Processing (NLP) and the semantic web may be mentioned. The analysis principles are inspired by the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) framework, characterized by a top-down process, starting with relevant keywords for the topic of interest to retrieve relevant articles from meta-sources And complementing these articles with other relevant articles from seed sources Identified by a bottom-up process. to mine the retrieved publication data a network analysis is performed Which allows to present in a synthetic way intrinsic topics of the selected publications. issues dealt with are related to query answering methods Both model-based and data-driven (the latter based on either machine learning or deep learning) And to their needs for explainability and fairness to deal with big data Notably by taking into account data veracity. conclusions point out trends and challenges to help better shaping the future of the FQAS field.

Enterprise data and semantic modelling: conceptual model of information technology incident management

Knowledge management methods and their efficient implementation across the organization determine sound and resilient management of processes. This paper studies the semantic integration of enterprise data sources essential to service management processes. Implementing a semantic layer within the enterprise architecture uses various tools, methods, and techniques. The semantic conceptual model unifies and implements intelligent integration of multiple data sources across the enterprise, achieving consistency and more accessible interpretation. Specifically, we draw our attention to incident and problem management within enterprises. We propose an ontology - a conceptual model for the incident management process. The incident ontology presented as an intelligent data integration layer component aims to achieve operational excellence. Besides, this ontology is a fundamental part of the proactive process in problem management. An ontology as a logic-based system supports integrity validation. It infers new, no explicitly modeled facts in the problem domain, thus helping experts better analyze and understand the problem. We discuss the conducted experiment results with the proposed in this article conceptual model using the enterprise knowledge graph platform. It can be perceived as a framework for a query-answering system with components, including ontology schema, data mapping, and classification methods for data graph enrichment.

NLP-Based Query-Answering System for Information Extraction from Building Information Models

The construction industry is information-intensive, and building information modeling (BIM) has been proposed as an information source for supporting decision making by construction project team members in the architecture, engineering, construction, and operation (AECO) industry. Because building information models contain more building data, further use of the aggregated building information to support construction and operation activities has become important. In Industry 4.0, similar-to-real-life virtual assistants, e.g., Apple’s Siri and Google Assistant, are becoming ever more popular. This research developed a query-answering (QA) system for BIM information extraction (IE) by using natural language processing (NLP) methods to build a virtual assistant for construction project team members. The architecture of the developed QA system for BIM IE consists of three major modules: natural language understanding, IE, and natural language generation. A Python-based prototype application was developed based on the architecture of the QA system for BIM IE to evaluate functionalities of the developed QA system using several BIM/industry foundation classes (IFC) models. Seven building information models and 127 test queries were utilized to evaluate the accuracy of the developed QA system for BIM IE. The experimental results indicated that the developed QA system for BIM IE achieved an 81.9 accuracy score. The developed NLP-based QA system for BIM is valid to provide relatively accurate answers based on natural language queries. The contributions of this research facilitate the development of virtual assistants in the AECO industry, and the architecture of the developed QA system can be extended to queries in other areas.

Named entity disambiguation in short texts over knowledge graphs.

The ever-growing usage of knowledge graphs (KGs) positions named entity disambiguation (NED) at the heart of designing accurate KG-driven systems such as query answering systems (QAS). According to the current research, most studies dealing with NED on KGs involve long texts, which is not the case of short text fragments, identified by their limited contexts. The accuracy of QASs strongly depends on the management of such short text. This limitation motivates this paper, which studies the NED problem on KGs, involving only short texts. First, we propose a NED approach including the following steps: (i) context expansion using WordNet to measure its similarity to the resource context. (ii) Exploiting coherence between entities in queries that contain more than one entity, such as “Is Michelle Obama the wife of Barack Obama?”. (iii) Taking into account the relations between words to calculate their similarity with the properties of a resource. (iv) the use of syntactic features. The NED solution approach is compared to state-of-the-art approaches using five datasets. The experimental results show that our approach outperforms these systems by 27% in the F-measure. A system called Welink, implementing our proposal, is available on GitHub, and it is also accessible via a REST API.

The Power and Potentials of Flexible Query Answering Systems: A Critical and Comprehensive Analysis

The Power and Potentials of Flexible Query Answering Systems: A Critical and Comprehensive Analysis

Indoor Fine Particulate Matter Monitoring in a Large Area Using Bidirectional Multihop VLC

These particulate matter (PM) causes lethal diseases to humans, and both short term and long term exposure are known to have hazardous effects. PM10 and PM2.5 have diameters less 10 and $2.5~\mu \text{m}$ , respectively, which makes them more dangerous to the human body. Thus, information regarding PM concentrations in indoor environments of dust-sensitive places is essential for proper precaution. In this study, we measured PM values in a large area and transferred this information using visible light communication (VLC) to the monitoring node. VLC is considered as an efficient technique due to its unique advantages and is currently unregulated. We applied bidirectional VLC to transfer information to ensure two-way communication. We also applied a multihop strategy to make the system function as a query answering system at extended distances. At one end, we generated a request to relay the request to the proper node and received the response in the form of PM data. In our experiment, we implemented four nodes and conducted multihop communication utilizing only a VLC link. We achieved a distance of 13.5 m with a zero-error rate between the two nodes using nonreturn to zero on-off keying (NRZ-OOK) modulation. In case of multihop, we achieved a distance greater than 40 m using four nodes to send dust information with a minimal error rate. This can be applied to large indoor areas where radio frequency is restricted.

Privacy-Preserving Biomedical Database Queries with Optimal Privacy-Utility Trade-Offs.

Sharing data across research groups is an essential driver of biomedical research. While interactive query-answering systems for biomedical databases aim to facilitate the sharing of aggregate insights without divulging sensitive individual-level data, query answers can still leak private information about the individuals in the database. Here, we draw upon recent advances in differential privacy to introduce query-answering mechanisms that provably maximize the utility (e.g., accuracy) of the system while achieving formal privacy guarantees. We demonstrate our accuracy improvement over existing approaches for a range of use cases, including cohort discovery, variant lookup, and association testing. Our new theoretical results extend the proof of optimality of the underlying mechanism, previously known only for count queries with symmetric utility functions, to more general utility functions needed for key biomedical research workflows. Our work presents a path toward interactive biomedical databases that achieve the optimal privacy-utility trade-offs permitted by the theory of differential privacy.

QUERY ANSWERING SYSTEM OF SHAHIH HADITH MUTTAFAQUN ‘ALAIH USING INDONESIAN THESAURUS BASED ON QUERY EXPANSION AND NAÏVE BAYES CLASSIFIER

<p class="Text"><em><span style="font-size: 9.0pt; line-height: 105%;">Abstract</span></em><span style="font-size: 9.0pt; line-height: 105%;">— Hadith are all the words, deeds and provisions of the Prophet Muhammad SAW that are used as the second of Islamic law after Al-Quran. The purpose of this study is to make an Information Retrieval system called the Query Answering System is expected to facilitate users in searching and finding the hadith documents as the user's needs. This study implements the Naïve Bayes Classifier method combined with Indonesian thesaurus as a query expansion to find the hadith documents that relevant to the input query. Based on the testing of 50 query data, the test results show that the use of query expansion gives better results than without using query expansion. Where based on testing of the top 1 data without using query expansion obtained an average recall value of 62%, an average precision value of 62%, an average accuracy value of 92.4% and an average value of the f-measure of 62%, while testing using query expansion obtained an average recall value of 66%, an average precision value of 66%, an average accuracy value of 93.2% and an average f -measure value of 66%. Based on the test results, the use of query expansion shows an improvement in the average recall value of 4%, an improvement in the average precision value of 4%, and an improvement in the average accuracy value of 0.8% and an improvement in the average f-measure value of 4% compared on without using query expansion.</span></p><p class="MsoNormal"> </p><p class="IndexTerms"><em>Index Terms</em>—hadith, information retrieval, query expansion, naïve bayes. </p>

Proving semantic properties as first-order satisfiability

The semantics of computational systems (e.g., relational and knowledge data bases, query-answering systems, programming languages, etc.) can often be expressed as (the specification of) a logical theory Th. Queries, goals, and claims about the behavior or features of the system can be expressed as formulas φ which should be checked with respect to the intended model of Th, which is often huge or even incomputable. In this paper we show how to prove such semantic properties φ of Th by just finding a modelA of Th∪{φ}∪Zφ, where Zφ is an appropriate (possibly empty) theory depending on φ only. Applications to relational and deductive databases, rewriting-based systems, logic programming, and answer set programming are discussed.

RehabRobo-Query: Answering natural language queries about rehabilitation robotics ontology on the cloud

We introduce a novel method to answer natural language queries about rehabilitation robotics, over the formal ontology REHABROBO-ONTO. For that, (i) we design and develop a novel controlled natural language for rehabilitation robotics, called REHABROBO-CNL; (ii) we introduce translations of queries in REHABRoso-CNI, into SPARQL queries, utilizing a novel concept of query description trees; (iii) we use an automated reasoner to find answers to SPARQL queries. To facilitate the use of our method by experts, we develop an intelligent, interactive query answering system, called REHABROBO-QUERY, using Semantic Web technologies, and make it available on the cloud via Amazon web services. REHABROBO-QUERY guides the users to express their queries in natural language and displays the answers to queries in a readable format, possibly with links to detailed information. Easy access to information on REHABROBO-ONTO through complex queries in natural language may help engineers inspire new rehabilitation robot designs, while also guiding practitioners to make more informed decisions on technology based rehabilitation.

GenoSurf: metadata driven semantic search system for integrated genomic datasets

Many valuable resources developed by world-wide research institutions and consortia describe genomic datasets that are both open and available for secondary research, but their metadata search interfaces are heterogeneous, not interoperable and sometimes with very limited capabilities. We implemented GenoSurf, a multi-ontology semantic search system providing access to a consolidated collection of metadata attributes found in the most relevant genomic datasets; values of 10 attributes are semantically enriched by making use of the most suited available ontologies. The user of GenoSurf provides as input the search terms, sets the desired level of ontological enrichment and obtains as output the identity of matching data files at the various sources. Search is facilitated by drop-down lists of matching values; aggregate counts describing resulting files are updated in real time while the search terms are progressively added. In addition to the consolidated attributes, users can perform keyword-based searches on the original (raw) metadata, which are also imported; GenoSurf supports the interplay of attribute-based and keyword-based search through well-defined interfaces. Currently, GenoSurf integrates about 40 million metadata of several major valuable data sources, including three providers of clinical and experimental data (TCGA, ENCODE and Roadmap Epigenomics) and two sources of annotation data (GENCODE and RefSeq); it can be used as a standalone resource for targeting the genomic datasets at their original sources (identified with their accession IDs and URLs), or as part of an integrated query answering system for performing complex queries over genomic regions and metadata.

Effective XQuery keyword using XML query processing

<span>The data has structured is determined using the standard is known as XML whereaslarge amount of data has consumed through internet consist of the both structural data format as well as semi structural data format which gets stored and processed whereas XML allow the data of semi-structured and hierarchical data representation not only consist of concept with individual items from various kind of database but also have relationship among data items. The utilized knowledge bed is provided with concise ideas for both structured and semi structured data files, XML document contents and rapid with exact solutions for the queries required at any time. The user can search their resources with the help of queries. Searching the resources with the help of queries is not a simple task, where inaccurate result and complexity would occur. Hence it is not a better way for searching the resources. This paper proposes the query answering system of Linear search usingwild card searchfor extracting the frequent pattern to maximize your search results in library database on XML document to extract the most relevant feeds from the large file directly. It will help the user to find his resources completely.</span>

A Better Approach to Ontology Integration using Clustering Through Global Similarity Measure

Knowledge representation is a crucial area of work in the intelligent system, especially in query answering system development. Ontology is used to represent shared knowledge of a particular domain for query answering system. Domain-specific ontology can be designed and developed by many groups and researchers, because of which there is heterogeneity in the knowledgebase. Ontology integration or merging is necessary in order to solve this problem of mixed knowledge. Finding similarity between two ontologies is crucial to achieve integration or merging of ontology. In this study, we present a method to generate a cluster of ontologies using global similarity measure of two ontologies. Ontology matching tools are used to find matched classes between two ontologies. Output of ontology matching tool is mapping between two ontologies and is used for generating clusters of ontology. We use Jaccard Similarity Index as a global similarity measure for clustering. Based on this measure, the popular k-means clustering algorithm is used to perform clustering of ontologies. Bins of ontologies are generated from each cluster. From each bin, all ontologies are finally merged into a single ontology, which helps us in reducing search effort in querying knowledge in query processing. The outcome of this research paper to provide better solution for merging ontology. Here, we use agriculture domain ontology corpus from the standard dataset for experimentation.

The Closest Incomplete Distributed Information System for Medical Query Answering System

Abstract The common issue for medical information systems are missing values. Generally, gaps are filled by statistically suggested values or rule-based methods. Another approach is to use the knowledge of information systems working under the same ontology. The medical incomplete system receives a query unable to answer, because of some unknown patient attributes. So, it has to communicate with other medical systems. The result of the collaboration is collective knowledgebase. In this paper, we propose a measure supporting choice of closest pair of systems. It determines the distance between the two systems. We use ERID algorithm to extract rules from incomplete, distributed information systems. Each constructed rule has confidence and support. They allowed to determine the distance between a pair of medical information systems. The proposed solution was verified on the basis of several “manipulated” medical information systems. Next, the solution was verified in systems with randomly selected data. The satisfying results were obtained and based on them, the proposed measure can be successfully used in medical systems to support the work of doctors and the treatment of patients.

System Architecture for Approximate Query Processing

<p><span style="font-size: 10.5pt; font-family: 'Times New Roman','serif'; mso-bidi-font-size: 12.0pt; mso-fareast-font-family: 宋体; mso-font-kerning: 1.0pt; mso-ansi-language: EN-US; mso-fareast-language: ZH-CN; mso-bidi-language: AR-SA;" lang="EN-US"><span style="font-size: 10.5pt; font-family: 'Times New Roman','serif'; mso-bidi-font-size: 12.0pt; mso-fareast-font-family: 宋体; mso-font-kerning: 1.0pt; mso-ansi-language: EN-US; mso-fareast-language: ZH-CN; mso-bidi-language: AR-SA;" lang="EN-US">Decision making is an activity that addresses the problem of extracting knowledge and information from data stored in data warehouses, in order to improve the business processes of information systems. Usually, decision making is based on On-Line Analytical Processing, data mining, or approximate query processing. In the last case, answers to analytical queries are provided in a fast manner, although affected with a small percentage of error. In the paper, we present the architecture of an approximate query answering system. Then, we illustrate our ADAP (Analytical Data Profile) system, which is based on an engine able to provide fast responses to the main statistical functions by using orthogonal polynomials series to approximate the data distribution of multi­dimensional relations. Moreover, several experimental results to measure the approximation error are shown and the response-time to analytical queries is reported.</span></span></p>

Beyond OWL 2 QL in OBDA: Rewritings and Approximations

Ontology-based data access (OBDA) is a novel paradigm facilitating access to relational data, realized by linking data sources to an ontology by means of declarative mappings. DL-Lite_R, which is the logic underpinning the W3C ontology language OWL 2 QL and the current language of choice for OBDA, has been designed with the goal of delegating query answering to the underlying database engine, and thus is restricted in expressive power. E.g., it does not allow one to express disjunctive information, and any form of recursion on the data. The aim of this paper is to overcome these limitations of DL-Lite_R, and extend OBDA to more expressive ontology languages, while still leveraging the underlying relational technology for query answering. We achieve this by relying on two well-known mechanisms, namely conservative rewriting and approximation, but significantly extend their practical impact by bringing into the picture the mapping, an essential component of OBDA. Specifically, we develop techniques to rewrite OBDA specifications with an expressive ontology to "equivalent" ones with a DL-Lite_R ontology, if possible, and to approximate them otherwise. We do so by exploiting the high expressive power of the mapping layer to capture part of the domain semantics of rich ontology languages. We have implemented our techniques in the prototype system OntoProx, making use of the state-of-the-art OBDA system Ontop and the query answering system Clipper, and we have shown their feasibility and effectiveness with experiments on synthetic and real-world data.

Exchange-Repairs

In a data exchange setting with target constraints, it is often the case that a given source instance has no solutions. In such cases, the semantics of target queries trivialize. The aim of this paper is to introduce and explore a new framework that gives meaningful semantics in such cases by using the notion of exchange-repairs. Informally, an exchange-repair of a source instance is another source instance that differs minimally from the first, but has a solution. Exchange-repairs give rise to a natural notion of exchange-repair certain answers (XR-certain answers) for target queries. We show that for schema mappings specified by source-to-target GAV dependencies and target equality-generating dependencies (egds), the XR-certain answers of a target conjunctive query can be rewritten as the consistent answers (in the sense of standard database repairs) of a union of conjunctive queries over the source schema with respect to a set of egds over the source schema, making it possible to use a consistent query-answering system to compute XR-certain answers in data exchange. We then examine the general case of schema mappings specified by source-to-target GLAV constraints, a weakly acyclic set of target tgds and a set of target egds. The main result asserts that, for such settings, the XR-certain answers of conjunctive queries can be rewritten as the certain answers of a union of conjunctive queries with respect to the stable models of a disjunctive logic program over a suitable expansion of the source schema.

Benchmark for Approximate Query Answering Systems

The standard benchmark for Decision Support Systems is TPC-H, which is composed of a database, a workload, and a set of metrics for the performance evaluation. However, TPC-H does not include a methodology for the benchmark of Approximate Query Answering Systems, or the software tools used to obtain fast answers to analytical queries in the decision making process. In the paper, the authors present a methodology to evaluate and compare Approximate Query Answering Systems. To this aim, a methodology that extends the standard TPC-H and a set of new metrics that take into account the specific features of these systems are proposed. Experimental results show the application of these metrics to two systems based on the data analytic approximation by orthonormal series.