We propose a novel module for computing all possible passages through an intersection by assigning indexes to entry and exit points extracted from textual narratives and computing relative directions between them. Our method relies on a contiguity condition that ensures every input record is connected and computes relative directions using predefined tables. The resulting passages serve as the basis for constructing reliable sketch maps with topological accuracy. This approach facilitates early error detection in spatial data and supports future extensions to cases where intersections are incomplete by retaining non-contiguous records until additional data become available.

This paper explores an outline of the application of AI in standardizing place names within urban narratives, addressing discrepancies caused by diverse agent terminologies. By leveraging AI chatbots for named entity recognition, coreference resolution, and entity linking, the study proposes an interactive methodology for homogenizing place names across different accounts. This innovative approach aims to enhance the accuracy of information extraction from narratives, demonstrating the potential of AI models over traditional linguistic methods in resolving place name inconsistencies.

Knowledge about the real world is often recorded in plain text, such as posts on social networks, descriptions in various guides, etc. These messages include spatial information that can be extracted using natural language processing methods. The extracted information can then be represented as a planar graph, which can be further transformed into a topological map using additional information describing the area. This paper outlines an algorithm that takes a given planar graph as input and uses a multi-agent system to place individual points in 2D space, creating a topological map respecting all edge directions given in the narratives.

Maps constructed in Euclidean space are commonly used to visually present information about the real world. However, their creation is resource intensive, be it financial, technical, human, or time-consuming, which can limit their timeliness and detail. A simpler form of visualization of data about the real world is represented by sketch maps, which mainly capture the topology and mutual features’ spatial location. By default, they are drawn by hand. This presupposes that the creator has a good knowledge of the depicted territory, can create a cognitive map, and is skilled in transforming it into a graphical form. Sketch maps can be detailed and up-to-date if these prerequisites are met. Our question was whether it is possible to meet these assumptions in another way: acquire knowledge of the territory by processing narratives related to the area of interest, create a suitable computer representation for further processing, and automatically generate the resulting sketch map. This article presents the last step – creating a sketch map based on spatial data acquired from narratives. The results show that even without metric data, it is possible to automatically generate a sketch map visually close to the actual situation.

A significant amount of real-world information is documented in simple text format, such as messages found on social networks. These messages include various types of data, including spatial details, which can be extracted through natural language processing. The extracted data can be represented as a plain topological graph, stored as tuples that describe individual edges. This paper outlines an algorithm that utilizes these tuples to generate a simplified map.

Navigation and an agent’s map representation in a multi-agent system become problematic when agents are situated in complex environments such as the real world. Challenging modifiability of maps, long updating period, resource-demanding data collection makes it difficult for agents to keep pace with rather quickly expanding cities. This study presents the first steps to a possible solution by exploiting natural language processing and symbolic methods of supervised machine learning. An adjusted algorithm processes formalized descriptions of one’s journey to produce a description of the journey. The explication is represented employing Transparent Intensional Logic. A combination of several explications might be used as a representation of spatial data, which may help the agents to navigate. Results of the study showed that it is possible to obtain a topological representation of a map using natural language descriptions. Collecting spatial data from spoken language may accelerate updating and creation of maps, which would result in up-to-date information for the agents obtained at a rather low cost.

This paper deals with an optimization of methods for recommending relevant text sources. We summarize methods that are based on a theory of Association Rules and Formal Conceptual Analysis which are computationally demanding. Therefore we are applying the ‘Iceberg Concepts’, which significantly prune output data space and thus accelerate the whole process of the calculation. Association Rules and the Relevant Ordering, which is an FCA-based method, are applied on data obtained from explications of an atomic concept. Explications are procured from natural language sentences formalized into TIL constructions and processed by a machine learning algorithm. TIL constructions are utilized only as a specification language and they are described in numerous publications, so we do not deal with TIL in this paper.

In this paper, we deal with the support in the search for appropriate textual sources. Users ask for an atomic concept that is explicated using machine learning methods applied to different textual sources. Next, we deal with the so-obtained explications to provide even more useful information. To this end, we apply the method of computing association rules. The method is one of the data-mining methods used for information retrieval. Our background theory is the system of Transparent Intensional Logic (TIL); all the concepts are formalised as TIL constructions.

In this paper we deal with machine learning methods and algorithms applied to the area of geographic data. First, we briefly introduce learning with a supervisor that is applied in our case. Then we describe the algorithm 'Framework' together with heuristic methods used in it. Definitions of particular geographic objects, i.e. their concepts, are formulated in our background theory Transparent Intensional Logic (TIL) as TIL constructions. These concepts serve as general hypotheses. Basic principles of supervised machine learning are generalization and specialization. Given a positive example, the learner generalizes, while after a near-miss example specialization is applied. Heuristic methods deal with the way generalization and specialization are applied.