INTERLEAVING LOGIC AND COUNTING

Natural language is poised to become a key medium for human-machine interactions in the era of large language models. In the field of biochemistry, tasks such as property prediction and molecule mining are critically important yet technically challenging. Bridging molecular expressions in natural language and chemical language can significantly enhance the interpretability and ease of these tasks. Moreover, it can integrate chemical knowledge from various sources, leading to a deeper understanding of molecules. Recognizing these advantages, we introduce the concept of conversational molecular design, a novel task that utilizes natural language to describe and edit target molecules. To better accomplish this task, we develop ChatMol, a knowledgeable and versatile generative pretrained model. This model is enhanced by incorporating experimental property information, molecular spatial knowledge, and the associations between natural and chemical languages. Several typical solutions including large language models (e.g. ChatGPT) are evaluated, proving the challenge of conversational molecular design and the effectiveness of our knowledge enhancement approach. Case observations and analysis offer insights and directions for further exploration of natural-language interaction in molecular discovery. Codes and data are provided in https://github.com/Ellenzzn/ChatMol/tree/main.

This paper is devoted to present a new approach to natural language understanding which is called here goal-oriented parsing. The interaction in natural language with artificial systems (robots, data base systems, program generators, question-answering systems, etc.) does not require in most cases of actual interest a full (human like) comprehension of natural language in all its details and nuances. A partial understanding is often enough, wich extracts from the natural language expressions the only significant information which is necessary to construct a correct formal input for the target system. In such a model of comprehension the same meaning is assigned to several different natural language expressions, thus defining a many-to-one mapping between natural language sentences and corresponding formal representations. We argue that a bounded scope, restricted, goal-oriented understanding of natural language may greatly increase the efficency of representation models and parsing algorithms, thus allowing the construction of effective systems. This claim is supported by the design and implementation of a natural language interface to a relational data base called NLI and developed at the Milan Polytechnic Artificial Intelligence Project. In the paper the architecture of the system, the linguistic models, and the parsing algorithms are presented and illustrated through selected examples. Promising directions for future research are outlined as well.

In this paper, we present a system that translates first order logic (FOL) formulas into natural language (NL) sentences. The motivation comes from an intelligent tutoring system teaching logic as a knowledge representation language, where it is used as a means for feedback to the users. FOL to NL conversion is achieved by using a rule-based approach, where we exploit the pattern matching capabilities of rules. So, the system consists of a rule-based component and a lexicon. The rule-based unit implements the conversion process, which is based on a linguistic analysis of a FOL sentence, and the lexicon provides lexical and grammatical information that helps in producing the NL sentences. The whole system is implemented in Jess, a java-based expert system shell. The conversion process currently covers a restricted set of FOL formulas.

Simulation modeling provides powerful decision support capabilities for production planning in manufacturing systems, yet widespread adoption remains limited by significant barriers in model development and maintenance. Traditional simulation approaches require substantial expertise, time investment, and financial resources, creating accessibility challenges particularly for small and medium enterprises. Current research on automatic simulation generation overlooks the significant potential that generative AI can offer in bridging the gap between natural language expression and executable model creation. This paper presents a novel methodology integrating large language models (LLMs) with automatic simulation model generation (ASMG) to enable direct generation of executable simulation models from natural language conversations. Unlike existing ASMG approaches that require formal specifications or simulation knowledge, our framework reduces programming requirements by allowing users to create functional models through conversational interaction. Our approach leverages natural language processing capabilities to transform user requests into executable simulation models without requiring specialized programming knowledge, enhancing human–machine interaction in production planning environments. The framework implements a four-step process: user intent expression through natural language interfaces, knowledge extraction using structured templates, simulation model construction via object-oriented data-driven components, and simulation execution with comprehensive result analysis for resource utilization, bottleneck identification, and production scheduling optimization. Performance testing demonstrates successful model generation across 18 supported action types with response times ranging from 8 s for simple operations to 5 min for complete manufacturing system creation. By enabling direct conversion of natural language requests into functional simulation models, this LLM-enhanced ASMG approach represents a step toward expanding access to simulation technology for non-expert users while maintaining the analytical power needed for complex manufacturing decision support and production control.

First-order logic augments the expressive power of propositional logic as it links the logical assertions to properties of objects of some non-empty universe: the domain of discourse. This is achieved by allowing the propositional symbols to take arguments that range over elements of the domain of discourse. These are now called predicate symbols and are interpreted as relations on the domain. Elements of the domain of discourse are denoted by terms built up from variables, constants, and functions applied to other terms. First-order logic also expands the lexicon of propositional logic with the quantifiers “for all” and “there exists” that are interpreted consistently with their natural language meaning.This chapter is devoted to classical first-order logic. Our presentation is similar to the one conducted for propositional logic. We first define the syntax of first-order logic, followed by its semantics. Next we define a proof system for it and present the fundamental theoretical results of soundness and completeness. We also discuss the decision problems related to this logic. The remaining sections of the chapter cover variations and extensions of first-order logic, as well as first-order theories.

1.Introduction Social media gives us instant access to a continuous stream of information generated by users around the world. This enables real-time monitoring of users’ behavior (Abbar et al., 2015), events’ life-cycles (Weng and Lee. 2010), and large-scale analysis of human interactions in general. Social media platforms are also used to propagate influence, spread content, and share information about events happening in real-time. Detecting the location of events directly from user-generated text can be useful in different contexts, such as humanitarian response, detecting the spread of diseases, or monitoring traffic. In this abstract, we define a system that can be used for any of the purposes described above, and illustrate its usefulness with an application for locating traffic-related events (e.g., traffic jams) in Doha. The goal of this project is to design a system that, given a social-media post describing an event, predicts whether or not the event belongs to a specific category (e.g., traffi...

We present in this paper the design and implementation of the OntoNL Framework, a natural language interface generator for knowledge repositories, as well as a natural language system for interactions with multimedia repositories which was built using the OntoNL Framework. The system allows the users to specify natural language requests about the multimedia content with rich semantics that result to digital content delivery. We propose and evaluate a semantic relatedness measure for OWL domain ontologies that concludes to the semantic ranking of ontological, grammatically-related structures. This procedure is used to disambiguate in a particular domain of context and represent in an ontology query language, natural language expressions. The ontology query language that we use is the SPARQL. The construction of the queries is automated and also dependent on the semantic relatedness measurement of ontology concepts. We also present the results of experimentation with the system.

Chapter 44 - Natural Language Interface Systems

Providers of communication services must be able to respond rapidly to the growing diversity of their customers' service needs as society becomes more dependent on information technologies. This responsiveness will be enhanced if the users themselves can define their service requirements, ideally using natural language. The problem with natural language expressions is their diversity, due to the various viewpoints from which they are written. Thus, we need a means of understanding the semantics of these diverse expressions. In the study outlined here, aimed at understanding the concepts in communication service requirements written in natural language, the authors first clarify the factors causing diversity of expression. These are found to be combinations of three intersecting viewpoints, namely: the interfaces between the network, terminals, and users; the viewpoint from both sides of each interface; and the logical/physical information viewpoint. Next, the target domain of communication services is modeled from the standpoints of operation procedures and functions, communication service concepts are systematized in terms of these models, and a communication service ontology is constructed. Then a semantic recognition method is proposed based on a mapping between concepts and expressions. The effectiveness of the methodology is demonstrated by converting natural language descriptions to formal language specifications. © 1997 Scripta Technica, Inc. Electron Comm Jpn Pt 1, 80(6): 23–36, 1997

ABSTRACTThere are expressions in natural language that are challenging for classical logic. I address one of them in this paper. It is an expression affirming two predicates with constants and negating the conjunction of those very predicates with the same constants. It is inconsistent in first‐order predicate calculus. My proposal is that a non‐axiomatic logic can deal with the expression with no difficulties. Just a few rules and concepts of that logic suffice. Based on the literature, I also resort to relations between certain statements in the non‐axiomatic logic and the Stoic conditional.

Drill Text Understanding with Integrated Natural Language and Picture Processing

An empirical study of representing adjectives over knowledge bases: Approach, lexicon and application

In recent years, the ever-increasing quantities of entities in large knowledge bases on the Web, such as DBpedia, Freebase and YAGO, pose new challenges but at the same time open up new opportunities for intelligent information access. These knowledge bases (KBs) have become valuable resources in many research areas, such as natural language processing (NLP) and information retrieval (IR). Recently, almost every major commercial Web search engine has incorporated entities into their search process, including Google’s Knowledge Graph, Yahoo!’s Web of Objects and Microsoft’s Satori Graph/Bing Snapshots. The goal is to bridge the semantic gap between natural language text and formalized knowledge. Within the context of globalization, multilingual and cross-lingual access to information has emerged as an issue of major interest. Nowadays, more and more people from different countries are connecting to the Internet, in particular the Web, and many users can understand more than one language. While the diversity of languages on the Web has been growing, for most people there is still very little content in their native language. As a consequence of the ability to understand more than one language, users are also interested in Web content in other languages than their mother tongue. There is an impending need for technologies that can help in overcoming the language barrier for multilingual and cross-lingual information access. In this thesis, we face the overall research question of how to allow for semantic-aware and cross-lingual processing of Web documents and user queries by leveraging knowledge bases. With the goal of addressing this complex problem, we provide the following solutions: (1) semantic annotation for addressing the semantic gap between Web documents and knowledge; (2) semantic search for coping with the semantic gap between keyword queries and knowledge; (3) the exploitation of cross-lingual semantics for overcoming the language barrier between natural language expressions (i.e., keyword queries and Web documents) and knowledge for enabling cross-lingual semantic annotation and search. We evaluated these solutions and the results showed advances beyond the state-of-the-art. In addition, we implemented a framework of cross-lingual semantic annotation and search, which has been widely used for cross-lingual processing of media content in the context of our research projects.

This paper proposes a mapping technique for automatically translating rules expressed in a format based on natural language, i.e. Semantics of Business Vocabulary and Business Rules (SBVR) standard, into production rules that can be executed by a computer (i.e. Rule engine). The proposed approach achieves a twofold purpose: on the one hand non IT skilled people (i.e. Domain expert) can effectively focus on business rules definition by using statements in natural language, and on the other hand the IT staff will have to manage business rules in a format ready to be executed by a rule engine. The main goal is to overcome some weaknesses in the software development process that could produce inconsistencies between the domain requirements identification and the implemented software functionalities. An exhaustive analysis of the mapping technique is provided and a real case study is presented in order to prove the validity of our work.

In this paper we extend the models for the “logic of categories” to a wider class of categories than is usually considered. We consider two kinds of logic, a restricted first-order logic and the full higher-order logic of elementary topoi.The restricted first-order logic has as its only logical symbols ∧, ∃, Τ, and =. We interpret this logic in a category with finite limits equipped with a factorization system (in the sense of [4]). We require to satisfy two additional conditions: ⊆ Monos, and any pullback of an arrow in is again in . A category with a factorization system satisfying these conditions will be called an EM-category.The interpretation of the restricted logic in EM-categories is given in §1. In §2 we give an axiomatization for the logic, and in §§3 and 5 we give two completeness proofs for this axiomatization. The first completeness proof constructs an EM-category out of the logic, in the spirit of Makkai and Reyes [8], though the construction used here differs from theirs. The second uses Boolean-valued models and shows that the restricted logic is exactly the ∧, ∃-fragment of classical first-order logic (adapted to categories). Some examples of EM-categories are given in §4.The restricted logic is powerful enough to handle relations, and in §6 we assign to each EM-category a bicategory of relations Rel() and a category of “functional relations” fr. fr is shown to be a regular category, and it turns out that Rel( and Rel(fr) are biequivalent bicategories. In §7 we study complete objects in an EM-category where an object of is called complete if every functional relation into is yielded by a unique morphism into . We write c for the full subcategory of consisting of the complete objects. Complete objects have some, but not all, of the properties that sheaves have in a category of presheaves.