A framework for linguistic logic programming

Relating defeasible and normal logic programming through transformation properties

We study the relationship between argumentation (abduction) and disjunctive logic programming. Based on the paradigm of argumentation, an abductive semantic framework for disjunctive logic programming is presented, in which the disjunctions of negative literals are taken as possible assumptions rather than only negative literals as the case of non-disjunctive logic programming. In our framework, three semantics PDH, CDH and WFDH are defined by three kinds of acceptable hypotheses to represent credulous reasoning, moderate reasoning and skeptical reasoning in AI, respectively. On the other hand, our semantic framework could be established in a broader class than that of disjunctive programs (called bi-disjunctive logic programs) and, hence, the corresponding abductive framework is abbreviated as BDAS (Bi-Disjunctive Argumentation-theoretic Semantics). Besides its rich expressive power and nondeterminism, BDAS integrates and naturally extends many key semantics, such as the minimal models, EGCWA, the well-founded model, and the stable models. In particular, a novel and interesting argumentation-theoretic characterization of EGCWA is shown. Thus the framework in this paper does not only provides a new way of performing argumentation (abduction) in disjunctive logic programming, but also is a simple, intuitive and unifying semantic framework for disjunctive logic programming.KeywordsLogic ProgramLogic ProgrammingArgument FrameworkDisjunctive ProgramDisjunctive LogicThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

La era del big data: análisis del lenguaje natural mediante la aplicación de folksonomía

Natural Language Processing(NLP) involves the development of computational models that aid in the development of automated tools for processing and generating natural language. Human developing these computational models require deep insight of linguistic knowledge and are a time consuming process. Hence, to automate this process and accelerate the computational science we use a data-driven approach i.e Statistical learning and Deep Learning. For devolving and sharing of information in natural language and making it accessible in other natural languages, Machine Translation(MT) is entailed. It is an application of NLP. Sanskrit being `father of informatics' [1] was considered as “lingua franca” of world intellectuals [2]. It is also an important language in the Indo-European family and considered as truly “donor” language of India. It has vast knowledge reserves in different discipline of studies such as Ayurveda, astronomy, literature etc. MT makes this rich language available to others with help of the computer. We have proposed and presented the prominent Deep Neural-based MT system for translation of Sanskrit to Hindi. We also present a comparison of Neural MT outperforming Statistical baseline system for this language pair.

Graph theory and the fields of natural language processing and information retrieval are well-studied disciplines. Traditionally, these areas have been perceived as distinct, with different algorithms, different applications and different potential end-users. However, recent research has shown that these disciplines are intimately connected, with a large variety of natural language processing and information retrieval applications finding efficient solutions within graph-theoretical frameworks. This book extensively covers the use of graph-based algorithms for natural language processing and information retrieval. It brings together topics as diverse as lexical semantics, text summarization, text mining, ontology construction, text classification and information retrieval, which are connected by the common underlying theme of the use of graph-theoretical methods for text and information processing tasks. Readers will come away with a firm understanding of the major methods and applications in natural language processing and information retrieval that rely on graph-based representations and algorithms.

We have already developed some kinds of paraconsistent annotated logic programs. In this paper we propose the paraconsistent process order control method based on a paraconsistent annotated logic program called before–after extended vector annotated logic program with strong negation (bf-EVALPSN) with a small example of pipeline process order verification. Bf-EVALPSN can deal with before–after relations between two processes (time intervals) in its annotations, and its reasoning system consists of two kinds of inference rules called the basic bf-inference rule and the transitive bf-inference rule. We introduce how the bf-EVALPSN-based reasoning system can be applied to the safety verification for process order.

The research in the domain of question answering systems is vast. Many Web based question answering systems provide users with long size paragraphs in response to questions. In this research work, we present a question answering system intended for mobile users using natural language annotations fulfilling their requirement of concise piece of information in response to queries. We describe a model of a system that accepts user queries in natural language after interpreting those queries match them with information stored in t-expression format and resulting entry is displayed to users thus helping them in getting the desired information without searching through the huge amount of information available on the Web. The system returns only the exact piece of information in natural language for presentation to the users thus saving them from further web hits and searching through documents for their required information.

Understanding people's emotions through natural language is a challenging task for intelligent systems based on Internet of Things (IoT). The major difficulty is caused by the lack of basic knowledge in emotion expressions with respect to a variety of real world contexts. In this paper, we propose a Bayesian inference method to explore the latent semantic dimensions as contextual information in natural language and to learn the knowledge of emotion expressions based on these semantic dimensions. Our method synchronously infers the latent semantic dimensions as topics in words and predicts the emotion labels in both word-level and document-level texts. The Bayesian inference results enable us to visualize the connection between words and emotions with respect to different semantic dimensions. And by further incorporating a corpus-level hierarchy in the document emotion distribution assumption, we could balance the document emotion recognition results and achieve even better word and document emotion predictions. Our experiment of the wordlevel and the document-level emotion predictions, based on a well-developed Chinese emotion corpus Ren-CECps, renders both higher accuracy and better robustness in the word-level and the document-level emotion predictions compared to the state-of-theart emotion prediction algorithms.

Effectively fusing information between the visual and language modalities remains a significant challenge. To achieve deep integration of natural language and visual information, this research introduces a multimodal fusion neural network model, which combines visual information (RGB images and depth maps) with language information (natural language navigation instructions). Firstly, the authors used faster R-CNN and ResNet50 to extract image features and attention mechanism to further extract effective information. Secondly, GRU model is used to extract language features. Finally, another GRU model is used to fuse the visual- language features, and then the history information is retained to give the next action instruction to the robot. Experimental results demonstrate that the proposed method effectively addresses the localization and decision-making challenges for robotic vacuum cleaners.

Argumentation-based abduction in disjunctive logic programming

In order to solve social science problems or make models for business forecasts, descriptive information in natural language is as important as measured numerical information. While numerical information is widely used in various stages, most descriptive information is employed only to describe the background or the very primitive stage of modelling. However, descriptive information has rich contents. One uses it to organise one’s ideas and to communicate with other people. The modelling method which can employ this rich content information is System Dynamics. It has an interface to express such descriptive information, stock flow diagrams. Moreover, these diagrams are also used when numerical simulations are conducted. However, matching descriptive information and diagrams has depended on model builders’ skill and mental models. This paper shows matching rules between descriptive information and stock flow diagrams as improvement in an existing method.KeywordsDescriptive informationnatural languagesimulation

Computer-aided diagnosis (CAD) has advanced medical image analysis, while large language models (LLMs) have shown potential in clinical applications. However, LLMs struggle to interpret medical images, which are critical for decision-making. Here we show a strategy integrating LLMs with CAD networks. The framework uses LLMs’ medical knowledge and reasoning to enhance CAD network outputs, such as diagnosis, lesion segmentation, and report generation, by summarizing information in natural language. The generated reports are of higher quality and can improve the performance of vision-based CAD models. In chest X-rays, an LLM using ChatGPT improved diagnosis performance by 16.42 percentage points compared to state-of-the-art models, while GPT-3 provided a 15.00 percentage point F1-score improvement. Our strategy allows accurate report generation and creates a patient-friendly interactive system, unlike conventional CAD systems only understood by professionals. This approach has the potential to revolutionize clinical decision-making and patient communication.

Modern-day demands for services often require an availability on a 24/7 basis as well as online accessibility around the globe. For this sake, personalized software systems, called chatbots, are applied. Chatbots offer services, goods or information in natural language. These programs respond to the user in real-time and offer an intuitive and simple interface to interact with. Advantages like these makes them increasingly popular. Chatbots can even act as substitutes for humans for specific purposes. Since the chatbot market is growing, chatbots might outperform and replace classical web applications in the future. For this reason, ensuring correct functionality of chatbots is of high and increasing importance. However, since different implementations and user behavior result in unpredictable results, the chatbot’s output is difficult to predict and classify as well. In fact, testing of chatbots represents a challenge because of the unavailability of a test oracle. In this paper, we introduce a metamorphic testing approach for chatbots. In general, metamorphic testing can be applied to situations where no expected values are available. In addition, we discuss how to obtain test cases for chatbots, i.e. sequences of interactions with a chatbot, in an according manner. We demonstrate our approach using a hotel booking system and discuss first experimental results.

Road traffic crashes (RTCs) are a global public health issue, with traditional analysis methods often hindered by delays and incomplete data. Leveraging social media for real-time traffic safety analysis offers a promising alternative, yet effective frameworks for this integration are scarce. This study introduces a novel multitask learning (MTL) framework utilizing large language models (LLMs) to analyze RTC-related tweets from Australia. We collected 26,226 traffic-related tweets from May 2022 to May 2023. Using GPT-3.5, we extracted fifteen distinct features categorized into six classification tasks and nine information retrieval tasks. These features were then used to fine-tune GPT-2 for language modeling, which outperformed baseline models, including GPT-4o mini in zero-shot mode and XGBoost, across most tasks. Unlike traditional single-task classifiers that may miss critical details, our MTL approach simultaneously classifies RTC-related tweets and extracts detailed information in natural language. Our fine-tunedGPT-2 model achieved an average accuracy of 85% across the six classification tasks, surpassing the baseline GPT-4o mini model’s 64% and XGBoost’s 83.5%. In information retrieval tasks, our fine-tuned GPT-2 model achieved a BLEU-4 score of 0.22, a ROUGE-I score of 0.78, and a WER of 0.30, significantly outperforming the baseline GPT-4 mini model’s BLEU-4 score of 0.0674, ROUGE-I score of 0.2992, and WER of 2.0715. These results demonstrate the efficacy of our fine-tuned GPT-2 model in enhancing both classification and information retrieval, offering valuable insights for data-driven decision-making to improve road safety. This study is the first to explicitly apply social media data and LLMs within an MTL framework to enhance traffic safety.

Model-Based Systems Engineering (MBSE) emerged as a response to the rising complexity of engineered systems, promising to reduce development times by improving consistency, collaboration, and traceability. While MBSE is effective in consolidating design artifacts into the single source of truth (SSOT), research addressing the evolution of these models during system development remains limited. In this paper, we address this gap by proposing a framework for capturing the evolution of MBSE models using a storytelling approach, focusing on the design decisions that drive this evolution. Our approach involves recording model evolution using version control systems and structuring decision information in natural language “Ystatements” to ensure traceability between the model’s incremental development and underlying decision nodes.