RankIE

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.

Variability of patient spine education by Internet search engine

While natural language interfaces (NLIs) are increasingly utilized to simplify the interaction with data visualization tools, improving and adapting the NLIs to the individual needs of users still requires the support of developers. ONYX introduces an interactive task learning (ITL) based approach which enables NLIs to learn from users through natural interactions. Users can personalize the NLI with new commands using direct manipulation, known commands, or by combining both. To further support users during the training process, we derived two design goals for the user interface, namely providing suggestions based on sub-parts of the command and addressing ambiguities through follow-up questions and instantiated them in ONYX. In order to trigger reflections and gain feedback on possible design trade-offs of ONYX and the instantiated design goals, we performed a formative user study to understand how to successfully integrate the suggestions and follow-up question into the interaction.

Since current general interest in anesthesia is unknown, we analyzed internet keyword searches to gauge general interest in anesthesia in comparison with surgery and pain. The trend of keyword searches from 2004 to 2010 related to anesthesia and anaesthesia was investigated using Google Insights for Search. The trend of number of peer reviewed articles on anesthesia cited on PubMed and Medline from 2004 to 2010 was investigated. The average cost on advertising on anesthesia, surgery and pain was estimated using Google AdWords. Searching results in other common search engines were also analyzed. Correlation between year and relative number of searches was determined with p< 0.05 considered statistically significant. Searches for the keyword "anesthesia" or "anaesthesia" diminished since 2004 reflected by Google Insights for Search (p< 0.05). The search for "anesthesia side effects" is trending up over the same time period while the search for "anesthesia and safety" is trending down. The search phrase "before anesthesia" is searched more frequently than "preanesthesia" and the search for "before anesthesia" is trending up. Using "pain" as a keyword is steadily increasing over the years indicated. While different search engines may provide different total number of searching results (available posts), the ratios of searching results between some common keywords related to perioperative care are comparable, indicating similar trend. The peer reviewed manuscripts on "anesthesia" and the proportion of papers on "anesthesia and outcome" are trending up. Estimates for spending of advertising dollars are less for anesthesia-related terms when compared to that for pain or surgery due to relative smaller number of searching traffic. General interest in anesthesia (anaesthesia) as measured by internet searches appears to be decreasing. Pain, preanesthesia evaluation, anesthesia and outcome and side effects of anesthesia are the critical areas that anesthesiologists should focus on to address the increasing concerns.

In Cross-Language Information Retrieval (CLIR) process, the translation effects have a direct impact o n the accuracy of follow-up retrieval results. In diction ary-based approach, we are dealing with the words t hat have more than one meaning which can decrease the retrieval performance if the query translation retur n an incorrect translations. These issues need to be ove rcome using efficient technique. In this study we p roposed a Cross-Language Information Retrieval (CLIR) method based on domain ontology using Quran concepts for disambiguating translation of the query and to improve the dictionary-based query translation. For experimentation, we use Quran ontology written in E nglish and Malay languages as a bilingual parallelcorpora and Quran concepts as a resource for cross- language query translation along with dictionary-ba sed translation. For evaluation, we measure the perform ance of three IR systems. IR 1 is natural language query IR, IR 2 is natural language query CLIR based on dictionary (as a Baseline) and IR 3 is the retrieval of this research proposed method using Mean Average Precision (MAP) and average precision at 11 points of recall. The experimental result shows that our prop osed method brings significant improvement in retri eval accuracy for English document collections, but defi cient for Malay document collections. The proposed CLIR method can obtain query expansion effect and improve retrieval performance in certain language.

Currently, huge amounts of information are stored in databases (DBs). In order to facilitate access to information to all users, natural language interfaces to databases (NLIDBs) have been developed. To this end, these interfaces translate natural language queries to a DB query language. For businesses, the main application of NLIDBs is for decision making by facilitating access to information in a flexible manner. For a NLIDB to be considered complete, it must deal with queries that involve aggregate functions: COUNT, MIN, MAX, SUM and AVG. The prototype developed at the Instituto Tecnologico de Cd. Madero (ITCM) can translate queries in natural language to SQL; however, it did not have a module for dealing with aggregate functions, grouping and subqueries. In this paper a new module of this NLIDB for dealing with aggregate functions, grouping and subqueries is described, and experimental results are presented, which show that this interface has a performance (recall) better than that of C-Phrase.

In this paper, a general architecture is proposed for developing embodied conversational agents with fuzzy ontology knowledge base. The proposed architecture enables agents to interact with the user via multimodal channels in a virtual reality environment for the purpose of language learning. The agents play the role of emotional, rational, and friendly partners who provide a specific domain of knowledge based on user's queries in natural language. These queries are performed by an optimized fuzzy search engine. Two scenarios including two virtual airports and a virtual electronic gadget shop are implemented in this architecture to improve users' oral skills. The results show the users' average oral skills improved 11%. Moreover, 80% of the users ranked agents' logical sequence of actions and the total speed of responses as very good, and 90% of them evaluated agents' appropriateness of responses as very good based on Likert scale.

In the absence of interaction history, image recommendations often depend on content-based approaches. Prompted by user queries in natural language, such systems rank items based on the similarity between textual and visual features. However, these approaches typically rely on static queries and do not offer alternative feedback mechanisms. In this paper, we present VisualReF: an interactive image retrieval prototype that introduces visual relevance feedback through fine-grained user annotations. Built on vision-language models (VLMs) for retrieval, our system allows users to label relevant and irrelevant regions in retrieved images. These regions are captioned using a generative vision-language model to refine the query vector. Our work bridges the gap between conventional static image retrieval and interactive, user-guided search by introducing visual relevance feedback. Finally, our prototype contributes to the field of visual recommendation by empowering researchers with practical tools for: (i) collecting region-level visual relevance signals from users, (ii) supporting integration of human feedback into interactive search pipelines, and (iii) explaining how the relevance feedback model perceives user input.

The goal of this paper is to introduce an approach to user interface (UI) reuse by means of concrete interaction patterns (CIPs) usage. A CIP extends the usual interaction pattern documentation describing interactions and user interface behaviour through User Interface Description Language (UIDL), allowing reuse and implementation of an abstract user interface to different platforms and technologies.

Qur’anic verse recommender systems are increasingly needed to assist users in understanding the vast and profound contents of the Qur’an through natural language queries. With over 6,000 verses, identifying spiritually relevant content remains a challenge, especially for users who are not familiar with specific keywords, themes, or Arabic vocabulary. This paper proposes a novel hybrid embedding-based recommendation approach that combines Sentence-BERT (SBERT) for semantic sentence-level representation and Term Frequency–Inverse Document Frequency (TF-IDF) for keyword emphasis. This dual embedding strategy allows the system to better understand users’ explicit intents as well as the underlying semantic context of the queries, offering more accurate and meaningful verse suggestions. Unlike traditional recommender systems, this solution does not rely on user history or collaborative filtering, effectively addressing the cold-start problem. The system also integrates a Large Language Model (LLM), specifically LLaMA, to generate contextual and spiritually meaningful explanations in Bahasa Indonesia. These explanations are created using zero-shot prompting, enabling dynamic interpretation of the recommended verses based on the user's query context. The system is deployed via a Telegram bot, enabling broad accessibility and ease of use. Evaluations were conducted through expert review and user testing. Experts rated the relevance and quality of the recommendations with an average precision of 82.4%, while general users provided an average satisfaction rating of 4.37 out of 5 across various dimensions including ease of use, clarity, and relevance. This study contributes to the development of AI-driven religious tools by presenting a method that combines semantic embeddings and LLM-generated explanations for intuitive and reflective interaction with the Qur’anic text. It also demonstrates the potential of hybrid NLP approaches and conversational interfaces in enhancing spiritual learning and access to religious knowledge in the digital age.

Android developers often need to search for example code to complete their development tasks. While existing code search systems for Android can deliver code against a search query, they do not recommend code for features that a developer might later need to implement. In this paper, we present FACER-AS (FACER for Android Studio); an Android Studio plugin, which uses FACER (Feature-driven API usage-based Code Examples Recommender) as its back-end code search and recommendation engine. FACER provides relevant code against natural language queries (Stage 1) and also recommends code of multiple related features (Stage 2) to facilitate opportunistic code reuse. To evaluate FACER-AS, we perform a user study involving one professional Android developer who uses our tool for the development of their ongoing live Android projects. We analyze the developer's usage of our tool over a span of seven days and find that FACER-AS achieves a 79% success rate for retrieving code against user queries (Stage 1) and a 41% success rate for recommending code for related features (Stage 2). We also observe a 43% reuse rate of Stage 1 recommendations and a 45% reuse rate of Stage 2 recommendations. Our tool's performance analysis and the developer's positive feedback show that FACER-AS can help Android developers with their coding activities. A video demonstration of our tool is available at https://youtu.be/3yN-39wP_FU and the source code of our tool is available at https://doi.org/10.5281/zenodo.5176816.

Motivated by the idea of reusing existing source code from previous projects within a software company, in this paper, we present a new source code recommendation technique called to help programmers find relevant implementations or sample code based on software requirement specifications. SoCeR assists programmers to search existing code repositories using natural language query. Our proposed approach summarizes Python code into sentences or phrases to match them against user queries. SoCeR extracts and analyzes the content of the code (such as variables, functions, docstrings, and comments) to generate code summary for each function which is then mapped to the respective functions. For evaluation purposes, we developed a web-based tool for users to enter a textual search query and get the relevant code search results that were most relevant to the query. In SoCeR, users can also upload new code to enrich the code base with tested code. If adopted, then SoCeR will benefit a software company to build a trusted code base enabling large-scale software code reuse.

To address the issue of matching inconsistencies between natural language queries and database column names caused by multi-word synonyms, this paper introduces a thesaurus and a knowledge-augmented module into the NL2SQL model to resolve lexical heterogeneity. Compared to existing NL2SQL models, the proposed method constructs a thesaurus and computes semantic similarity to more comprehensively capture the semantic relevance between user queries and database structures. This enables more accurate natural language-to-SQL conversion and reduces matching errors caused by lexical heterogeneity. Empirical results fully demonstrate the effectiveness of the knowledge-augmented approach for the NL2SQL task.

In this paper a natural language query interface is developed for semantic and spatio-temporal querying of MPEG-7 based domain ontologies. The underlying ontology is created by attaching domain ontologies to the core Rhizomik MPEG-7 ontology. The user can pose concept, complex concept, spatial, temporal, object trajectory and directional trajectory queries. Furthermore, the system handles the negative meaning in the user query. When the user enters a natural language query, it is parsed with the link parser. According to query type, the objects, attributes, spatial relation, temporal relation, trajectory relation, time filter and time information are extracted from the parser output by using predefined information extraction rules. After the information extraction, SPARQL queries are generated, and executed against the ontology by using an RDF API. The results are used to calculate spatial, temporal, and trajectory relations between objects. The results satisfying the required relations are displayed in a tabular format and the user can navigate through the

Financial and economic data are typically available in the form of tables and comprise mostly of monetary amounts, numeric and other domain-specific fields. They can be very hard to search and they are often made available out of context, or in forms which cannot be integrated with systems where text is required, such as voice-enabled devices. This work presents a novel system that enables both experts in the finance domain and non-expert users to search financial data with both keyword and natural language queries. Our system answers the queries with an automatically generated textual description using Natural Language Generation (NLG). The answers are further enriched with derived information, not explicitly asked in the user query, to provide the context of the answer. The system is designed to be flexible in order to accommodate new use cases without significant development effort, thus allowing fast integration of new datasets.