SKATE: A Natural Language Interface for Encoding Structured Knowledge

In this paper we presented a framework for action descriptions and its connection to natural language interfaces for artificial agents. The core point of this approach is the use of a generic action/object hierarchy, which allows interpretation of natural language command sentences, issued by a human user, as well as planning and reasoning processes on the conceptual level, and connects to the level of agent executable actions. The linguistic analysis is guided by a case frame representation, which provides a connection to actions (and objects) represented on the conceptual level. Planning processes can be implemented using typical precondition and effect descriptions of actions in the conceptual hierarchy. The level of primitive actions (leaf nodes of this conceptual hierarchy) connects to the agents' executable actions. The level of primitive actions can thus be adapted to different types of physical agents with varying action sets. Further work includes the construction of a suitable, general action ontology, based on standard ontologies like FrameNet (ICSI, 2007), Ontolingua (KSL, 2007), Mikrokosmos (CRL, 1996), Cyc (Cycorp, 2007), or SUMO (Pease, 2007; IEEE SUO WG, 2003), which will be enriched with precondition and effect formulas. Other topics to be pursued relate to communication of mobile physical agents (humans) in a "speech-controlled" environment. The scenario is related to the "smart house" but instead of being adaptive and intelligent, the house (or environment) is supposed to respond to verbal instructions and questions by the human user. A special issue, we want to address, is the development of a sophisticated context model, and the use of contextual information to resolve ambiguities in the verbal input and to detect impossible or unreasonable actions.

Nowadays most of people of developed economy countries interact with software every day. As a result of computer systems expansion to all scope of people's activity the problem of transition from visual and command interfaces to natural language user interfaces is thrown into the sharp relief. Computational linguistics and natural language processing methods are described in this article. Represent a formal definition of a dialogue system as a Markov decision process. Methods of natural language manipulation are applied in machine translation software systems, systems of search and exchange the data, text annotation and expert systems. Prototype of natural language user interface to structured data source is developed. As a result it is convert natural language user's query to SQL query to database. Also showed application of Levenshtein distance for processing natural language query to SQL query to database. Natural language user interface is created to predefined subject field. User interface interacts to database that contain information about existent program libraries and frameworks. Consequently, using natural language processing methods it is possible to develop user natural language user interface providing capability to interact with machine.

A feasibility study of the case hierarchy model for the construction and porting of natural language interfaces

Social scientists are increasingly interested in analyzing the semantic information (e.g., emotion) of unstructured data (e.g., Tweets), where the semantic information is not natively present. Performing this analysis in a cost-efficient manner requires using machine learning (ML) models to extract the semantic information and subsequently analyze the now structured data. However, this process remains challenging for domain experts. To demonstrate the challenges in social science analytics, we collect a dataset, QUIET-ML, of 120 real-world social science queries in natural language and their ground truth answers. Existing systems struggle with these queries since (1) they require selecting and applying ML models, and (2) more than a quarter of these queries are vague, making standard tools like natural language to SQL systems unsuited. To address these issues, we develop LEAP, an end-to-end library that answers social science queries in natural language with ML. LEAP filters vague queries to ensure that the answers are deterministic and selects from internally supported and user-defined ML functions to extend the unstructured data to structured tables with necessary annotations. LEAP further generates and executes code to respond to these natural language queries. LEAP achieves a 100% pass @ 3 and 92% pass @ 1 on QUIET-ML, with a $1.06 average end-to-end cost, of which code generation costs $0.02.

: If we want to build intelligent information retrieval systems, we will have to give them the capabilities of understanding Natural language, automatically organizing and reorganizing their memories, and using intelligent heuristics for searching their memories. These systems will have to analyze and understand both new text and Natural Language queries. In answering questions, they will have to direct memory search to reasonable places. This requires good organization of both the conceptual content of text and knowledge necessary for understanding those texts and accessing memory. The CYRUS and FRUMP systems (Kolodner (1978), Schank and Kolodner (1979), Dejong (1979)) comprise an information retrieval system called CyFr. Together, they have the analysis and retrieval capabilities mentioned above. FRUMP analysis news stories from the UPI wire for their conceptual content, and produces summaries of those stories. It sends summaries of stories about important people to CYRUS automatically adds those stories to its memory, and can then retrieve that information to answer question posed to it in natural language. This paper describes the problems involved in building such an intelligent system. It proposes solutions to some of those problems bases on recent research in Artificial Intelligence and Natural Language processing, and describes the CyFr system, which implements those solutions. The solutions we propose and implement are based on a model of human understanding and memory retrieval. (Author)

This paper describes two approaches to the automatic generation of behavioral VHDL models from descriptions written in natural language. Both approaches are based on a modeling style in which behavior is represented by a system of interconnected processes. The first approach employs a semantic grammar to directly generate a single VHDL process from a paragraph written in a restricted English called ModelSpeak. The second approach accepts more general English and generates models consisting of multiple processes. series of sentences describing the behavior of a single process and generated correct VHDL code for it. This interface requires the behavior be expressed in a highly restricted English called ModelSpeak (10). Conditional expressions (if statements) may be nested to two levels, and interaction is invoked by the interface to resolve scope ambiguity of loops. This interface is implemented with a semantic grammar (3) in Prolog. The second interface (Approach II) to the Modeler's Assistant for natural language is substantially more complex, but accepts more general English expressions and generates multiple-process models. In this approach, each sentence is parsed by a bottom-up, parallel chart parser to generate a set of valid parse trees. The trees are examined by a Semantic Analyzer (7) to determine one which is semantically correct, and a conceptual graph (14) (semantic network) of its meaning is generated. Once the conceptual graph of individual sentences are joined together (12), a program called the Linker (8) analyzes the graph to produce a Process Model Graph and VHDL process code for the Modeler's Assistant. Nodes in the conceptual graph representing verbs typically generate processes or the conditions which govern them. Nodes representing objects generate signals. The modeling style underlying the Linker produces a sizable collection of processes, each consisting of a single if_then statement. To avoid unnecessary bus resolutions, multiple processes assigning to the same signal are merged. Since signal types are generally not expressed in the English description, they may be added interactively later. Before describing the approaches in more detail, related research on natural language interfaces is reviewed briefly.

The emergence of natural language processing has revolutionized the way users interact with tabular data, enabling a shift from traditional query languages and manual plotting to more intuitive, language-based interfaces. The rise of large language models (LLMs) such as ChatGPT and its successors has further advanced this field, opening new avenues for natural language processing techniques. This survey presents a comprehensive overview of natural language interfaces for tabular data querying and visualization, which allow users to interact with data using natural language queries. We introduce the fundamental concepts and techniques underlying these interfaces with a particular emphasis on semantic parsing, the key technology facilitating the translation from natural language to SQL queries or data visualization commands. We then delve into the recent advancements in Text-to-SQL and Text-to-Vis problems from the perspectives of datasets, methodologies, metrics, and system designs. This includes a deep dive into the influence of LLMs, highlighting their strengths, limitations, and potential for future improvements. Through this survey, we aim to provide a roadmap for researchers and practitioners interested in developing and applying natural language interfaces for data interaction in the era of large language models.

Natural language interface (NLI) enables an efficient and effective interaction by allowing a user to submit a single phrase in natural language to the system. Free hand gestures can be added to an NLI to specify the referents for deictic terms in speech. By combining NLI with other modalities to a multimodal user interface, speech utterance length can be reduced, and users need not clearly specify the referent verbally. Integrating deictic terms with deictic gestures is a critical function in multimodal user interface. This paper presents a novel approach to extend chart parsing used in natural language processing (NLP) to integrate multimodal input based on speech and manual deictic gesture. The effectiveness of the technique has been validated through experiments, using a traffic incident management scenario where an operator interacts with a map on large display at a distance and issues multimodal commands through speech and manual gestures. The preliminary experiment of the proposed algorithm shows encouraging results.

Extraction of knowledge data from knowledge database using natural language query is a difficult task. Different types of natural language processing (NLP) techniques have been developed to handle this knowledge data extraction task. This paper proposes an automated query-response model termed Extended Automated Knowledge Provider System (EAKPS) that can manage various types of natural language queries from user. The EAKPS uses combination based technique and it can handle assertive, interrogative, imperative, compound and complex type query sentences. The algorithm of EAKPS generates structure query language (SQL) for each natural language query to extract knowledge data from the knowledge database resident within the EAKPS. Extraction of noun or noun phrases is another issue in natural language query processing. Most of the times, determiner, preposition and conjunction are prefixed to a noun or noun phrase and it is difficult to identify the noun/noun phrase with prefix during query processing. The proposed system is able to identify these prefixes and extract exact noun or noun phrases from natural language queries without any manual intervention.

The database interface, called the "natural language interface", uses natural language to allow users to access data without the need for SQL queries. Using query language to interact with databases is always a sophisticated issue. Due to this complexity, the client must use the clear reports that are included in some pre-executed programming projects when using information that is already present in database points of confinement. But, users can make it possible for each nonprofessional user to ask questions and provide requirements in natural language, which the computer can then process to produce the appropriate data. This study introduces a system developed using the "Natural Language Interface to Database" (NLIDB). This approach has led to the emergence of "experts" who can detect compound sentences in English and use them in lessons. After parsing the input sentences, the natural language is converted to SQL.

In this contribution, we present a framework for an intelligent natural language interface (NLI) that suits the need of embedded platform, using agent-based approach. The proposed framework is motivated by an ongoing speech technology research project aimed at developing a generic synthesizer for information disseminating systems in local languages. The architecture is based on various forms of action representations with a sequence of transformations that converts users' input (text or speech) into a suitable set of agent actions that produce response to the input. This approach incrementally minimizes the complexity and ambiguity of the natural language input by using predefined sets of interim actions at different levels, hence, increasing the robustness and reliability of the NLI.

As an organization grows through mergers and acquisitions and through the integration of independently designed information sub-systems, it could find itself faced with a collection of databases implemented on heterogeneous database management systems. This paper presents a system to provide integrated access to multiple, independently designed databases through a portable natural language front-end and a multiple database access system back-end.The overall system is divided into a natural language interface (NLI) front-end and a multiple database access system (MDAS) back-end. The MDAS integrates the various conceptual views into a single global external schema. In a prototype of the overall system, the NLI will accept a natural language query and produce a relational calculus query expression over a global external schema. The MDAS will construct the response to the query by accessing the separate constituent databases through the local database management systems (DBMS's).

Natural Language Interfaces (NLIs) are a viable, human-readable alternative to complex, formal query languages like SPARQL, which are typically used for accessing semantically structured data (e.g. RDF and OWL repositories). However, in order to cope with natural language ambiguities, NLIs typically support a more restricted language. A major challenge when designing such restricted languages is habitability-how easily, naturally and effectively users can use the language to express themselves within the con-strains imposed by the system. In this paper, we investigate two methods for improving the habitability of a Natural Language Interface: feedback and clarification dialogues. We model feedback by showing the user how the system interprets the query, thus suggesting repair through query reformulation. Next, we investigate how clarification dialogues can be used to control the query interpretations generated by the system. To reduce the cognitive overhead, clarification dialogues are coupled with a learning mechanism. Both methods are shown to have a positive effect on the overall performance and habitability.

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

Structured Query Language (SQL) is commonly used in Relational Database Management Systems (RDBMS) and is currently one of the most popular data definition and manipulation languages. Its core functionality is implemented, with only some minor variations, throughout all RDBMS products. It is an effective tool in the process of managing and querying data in relational databases. This paper describes a method to effectively automate the conversion of a data query from a Natural Language Query (NLQ) to Structured Query Language (SQL) with Online Analytical Processing (OLAP) cube data warehouse objects. To obtain or manipulate the data from relational databases, the user must be familiar with SQL and must also write an appropriate and valid SQL statement. However, users who are not familiar with SQL are unable to obtain relevant data through relational databases. To address this, we propose a Natural Language Processing (NLP) model to convert an NLQ into an SQL query. This allows novice users to obtain the required data without having to know any complicated SQL details. The model is also capable of handling complex queries using the OLAP cube technique, which allows data to be pre-calculated and stored in a multi-dimensional and ready-to-use format. A multi-dimensional cube (hypercube) is used to connect with the NLP interface, thereby eliminating long-running data queries and enabling self-service business intelligence. The study demonstrated how the use of hypercube technology helps to increase the system response speed and the ability to process very complex query sentences. The system achieved impressive performance in terms of NLP and the accuracy of generating different query sentences. Using OLAP hypercube technology, the study achieved distinguished results compared to previous studies in terms of the speed of the response of the model to NLQ analysis, the generation of complex SQL statements, and the dynamic display of the results. As a plan for future work, it is recommended to use infinite-dimension (n-D) cubes instead of 4-D cubes to enable ingesting as much data as possible in a single object and to facilitate the execution of query statements that may be too complex in query interfaces running in a data warehouse. The study demonstrated how the use of hypercube technology helps to increase system response speed and process very complex query sentences.