A Detailed Study on Aggregation Methods used in Natural Language Interface to Databases (NLIDB)

Historically, databases have been the most crucial issue in the study of information systems, and they constitute an essential part of all information management systems. Since, it complicated due to restricting the number of potential users, particularly non-expert database users who must comprehend the database structure to submit such queries. Natural language interface (NLI), the simplest method to retrieve information, is one possibility for interacting with the database. The transformation of a natural language query into a Structured Query (SQL) in a database is known as a "Natural Language Interface to Database" (NLIDB). This study uses NLIDB to handle the works performed under various aggregations with aggregation functions, a grouping phrase, and a possessing clause. This study carefully examines the numerous systematic aggregation approaches utilized in the NLIDB. This review provides extensive information about the many methods, including query-based, pattern-based, general, keyword-based NLIDB, and grammar-based systems, to extract data for a dissertation from a generic module for use in such systems that support query execution utilizing aggregations

Log Exploration and Analytics Using Large Language Models

Log data are typically stored in databases as schema-based entries in a structured format. Conventionally, log exploration requires an understanding of the fields, schema, and query parameters of the database. This disclosure describes techniques that use tabular large language models (LLMs) to process, mine, and make log data amenable to natural language queries. A relatively unsophisticated user with no database skills can query log files using natural language search. The LLMs can be fine-tuned using prompt engineering and causation information. The conventional, tedious mining of logs across multiple systems using database queries is replaced by a simple natural language interface that provides the ability to determine meaningful relationships and context across events captured within the logs. Natural language queries can enable help desks to do a basic level of troubleshooting, saving time for administrators. As more information gets added, querying and analytics of logs are simplified, with a resultant improvement in the speed and quality of troubleshooting

A proposed framework for the development of an interactive natural language interface to ontologies

The information world is moving towards the integration of different databases (DBs), which contain a lot of structured information (ontologies): these are also known as knowledge repositories. In order to facilitate access to, and to permit the utilization of the massive information stored in these ontologies, a natural language interface (NLI) is used. A natural language interface (NLI) provides the platform for man and machine to interact. A user enters a query in his language and this is translated into a form understandable by the computer. The computer then processes the user’s query and retrieves the exact information desired by the user. Some of the challenges being faced by natural language interfaces to DBs (NLIDBs) include lack of adequate guidance in query formulation, incorrect interpretation of user query, absence of query progress status notification and the need for standardization, among others. In this paper, we have done a systematic review of some of the NLIs in existence. Our investigations have shown clearly, that the effective retrieval of any piece of information depends on the correct mapping of queries made in natural language to machine understandable form. We therefore propose a framework for the development of a friendly NLI that will guide users in formulating their queries and correctly interpret user’s intention, using query authoring services, and feedback and clarification dialogues

An authoring tool for decision support systems in context questions of ecological knowledge

Decision support systems (DSS) support business or organizational decision-making activities, which require the access to information that is internally stored in databases or data warehouses, and externally in the Web accessed by Information Retrieval (IR) or Question Answering (QA) systems. Graphical interfaces to query these sources of information ease to constrain dynamically query formulation based on user selections, but they present a lack of flexibility in query formulation, since the expressivity power is reduced to the user interface design. Natural language interfaces (NLI) are expected as the optimal solution. However, especially for non-expert users, a real natural communication is the most difficult to realize effectively. In this paper, we propose an NLI that improves the interaction between the user and the DSS by means of referencing previous questions or their answers (i.e. anaphora such as the pronoun reference in “What traits are affected by them?”), or by eliding parts of the question (i.e. ellipsis such as “And to glume colour?” after the question “Tell me the QTLs related to awn colour in wheat”). Moreover, in order to overcome one of the main problems of NLIs about the difficulty to adapt an NLI to a new domain, our proposal is based on ontologies that are obtained semi-automatically from a framework that allows the integration of internal and external, structured and unstructured information. Therefore, our proposal can interface with databases, data warehouses, QA and IR systems. Because of the high NL ambiguity of the resolution process, our proposal is presented as an authoring tool that helps the user to query efficiently in natural language. Finally, our proposal is tested on a DSS case scenario about Biotechnology and Agriculture, whose knowledge base is the CEREALAB database as internal structured data, and the Web (e.g. PubMed) as external unstructured information.This paper has been partially supported by the MESOLAP (TIN2010-14860), GEODAS-BI (TIN2012-37493-C03-03), LEGOLANGUAGE (TIN2012-31224) and DIIM2.0 (PROMETEOII/2014/001) projects from the Spanish Ministry of Education and Competitivity. Alejandro Maté is funded by the Generalitat Valenciana under an ACIF grant (ACIF/2010/298)

SpatialNLI: A Spatial Domain Natural Language Interface to Databases Using Spatial Comprehension

A natural language interface (NLI) to databases is an interface that translates a natural language question to a structured query that is executable by database management systems (DBMS). However, an NLI that is trained in the general domain is hard to apply in the spatial domain due to the idiosyncrasy and expressiveness of the spatial questions. Inspired by the machine comprehension model, we propose a spatial comprehension model that is able to recognize the meaning of spatial entities based on the semantics of the context. The spatial semantics learned from the spatial comprehension model is then injected to the natural language question to ease the burden of capturing the spatial-specific semantics. With our spatial comprehension model and information injection, our NLI for the spatial domain, named SpatialNLI, is able to capture the semantic structure of the question and translate it to the corresponding syntax of an executable query accurately. We also experimentally ascertain that SpatialNLI outperforms state-of-the-art methods.Comment: 10 page