FOIS is the flagship conference of the International Association for Ontology and its Applications, a non-profit organization which promotes interdisciplinary research and international collaboration at the intersection of philosophical ontology, linguistics, logic, cognitive science, and computer science.

This book presents the papers delivered at FOIS 2023, the 13th edition of the Formal Ontology in Information Systems conference. The event was held as a sequentially-hybrid event, face-to-face in Sherbrooke, Canada, from 17 to 20 July 2023, and online from 18 to 20 September 2023. In total, 62 articles from 19 different countries were submitted, out of which 25 were accepted for inclusion in the conference and for publication; corresponding to an acceptance rate of 40 percent.

The contributions are separated into the book’s three sections: (1) Foundational ontological issues; (2) Methodological issues around the development, alignment, verification and use of ontologies; and (3) Domain ontologies and ontology-based applications. In these sections, ontological aspects from a wide variety of fields are covered, primarily from various engineering domains including cybersecurity, manufacturing, petroleum engineering, and robotics, but also extending to the humanities, social sciences, medicine, and dentistry. A noticeable trend among the contributions in this edition of the conference is the recognition that improving the tools to analyze, align, and improve ontologies is of paramount importance in continuing to advance the field of formal ontology.

The book will be of interest to all formal and applied ontology researchers, and to those who use formal ontologies and information systems as part of their work.

Image schemas have been proposed as conceptual building blocks corresponding to the hypothesised most fundamental embodied experiences. We formally investigate how combinations of image schemas (or 'image schematic profiles') can model essential aspects of events, and discuss benefits for artificial intelligence and cognitive systems research, in particular concerning the role of such basic events in concept formation. More specifically, as exemplary illustrations and proof of concept the image schemas Object, Contact, and Path are combined to form the events Blockage, Bouncing, and Caused-Movement. Additionally, an outline of a proposed conceptual hierarchy of levels of modelling for image schemas and similar cognitive theories is given.

Autonomous robots struggle with plan adaption in uncertain and changing environments. Although modern robots can make popcorn and pancakes, they are incapable of performing such tasks in unknown settings and unable to adapt action plans if ingredients or tools are missing. Humans are continuously aware of their surroundings. For robotic agents, real-time state updating is time-consuming and other methods for failure handling are required. Taking inspiration from human cognition, we propose a plan adaption method based on event segmentation of the image-schematic states of subtasks within action descriptors. For this, we reuse action plans of the robotic architecture CRAM and ontologically model the involved objects and image-schematic states of the action descriptor cutting. Our evaluation uses a robot simulation of the task of cutting bread and demonstrates that the system can reason about possible solutions to unexpected failures regarding tool use.

Optimising object order in stacking problems remains a hard problem for cognitive robotics research. In this paper, we continue our work on using the spatiotemporal relationships called image schemas to represent affordance spaces founded on object properties. Based on object properties, we introduce a stacking-order algorithm and describe the action descriptors using an image-schematic event segmentation format by describing a small subset using the Image Schema Logic ISL^𝐹𝑂𝐿.

In this extended abstract, we present initial work on intelligent object stacking by household robots using a symbolic approach grounded in image schema research. Image schemas represent spatiotemporal relationships that capture objects’ affordances and dispositions. Therefore, they offer the first step to ground semantic information in symbolic descriptions. We hypothesise that for a robot to successfully stack objects of different dispositions, these relationships can be used to more intelligently identify both task constraints and relevant event segments. 

In recent years, there has been a growth in the use of reference conceptual models, in general, and domain ontologies, in particular, to capture information about complex and critical domains. These models play a fundamental role in different types of critical semantic interoperability tasks. Therefore, it is essential that domain experts are able to understand and reason using the models' content. In other words, it is important that conceptual models are cognitively tractable. However, it is unavoidable that when the information of the represented domain grows, so does the size and complexity of the artifacts and models that represent them. For this reason, more sophisticated techniques for complexity management in ontology-driven conceptual models, need to be developed. Some approaches are based on the notion of model modularization. In this paper, we follow the work on model modularization to present an approach for view extraction for the ontology-driven conceptual modeling language OntoUML. We provide a formal definition for ontological views over OntoUML conceptual models that completely leverages on the ontologically well-grounded real-world semantics of that language. Moreover, we present a plug-in tool, particularly developed for an OntoUML model-based editor that implements this formal view structure in terms of queries defined over the OntoUML metamodel embedded in that tool. 

Cognitive linguistics has provided compelling evidence that semantic structure in natural language reflects conceptual structure that arises from our embodied experience in the world. To capture this conceptual structure, a set of spatio-temporal cognitive building blocks called image schemas was introduced by Lakoff and Johnson. Detecting image schemas in natural language can provide further insights into how embodied experiences are encoded in natural language and potentially contribute to research on conceptual understanding and symbol grounding in cognitive systems. Methods for (semi-)automatically extracting image schemas from natural language are an open challenge. We propose a spectral clustering approach paired with semantic role labeling to semi-automatically extract image schemas from multilingual text, obtaining a precision of more than 80% on three languages. 

Image schemas provide preverbal conceptual structures and are suggested to be the conceptual building blocks from which cognitive phenomena such as language and reasoning are constructed. 'Motion along a path' is one of the first image schemas infants remember, making PATH-following one of the earliest cognitive building blocks. We are interested in the importance of this developmentally relevant image schema in abstract adult language. For this purpose, we propose a semi-automated method to extract image-schematic structures related to PATH-following from a multilingual financial terminology. Two major assumptions are that a linguistic mapping of image schemas facilitates the understanding of complex concepts and is persistent across languages. Our results show that complex textual representations can be made simpler to understand by extracting the underlying image schemas and that they are persistent across languages. Another result includes the identification of novel specifications of predefined image-schematic structures.

Natural language understanding remains one of the weak spots of Artificial Intelligence and cognitive systems in general. In cognitive linguistics, image schemas were introduced as spatio-temporal relations learned from sensorimotor processes that constitute conceptual building blocks for highlevel cognition, such as language and reasoning. In this role, they have been successfully employed in computational concept invention and conceptual metaphor research. However, due to their abstract nature identifying them in natural language is an open challenge. To address this issue, this paper proposes a spectral clustering method combined with semantic role labeling to semi-automatically detect image schemas in natural language. In the majority of identified spatial clusters from the Europarl corpus the proposed method detected image schemas, which shows that it works effectively on large corpora. The outcome of this method is a repository of natural language expressions annotated with image schemas that can be used to improve spatial language understanding and provide examples to supervised machine learning approaches.

In recent years, there has been a growth in the use of reference conceptual models to capture information about complex and critical domains. However, as the complexity of domain increases, so does the size and complexity of the models that represent them. Over the years, different techniques for complexity management in large conceptual models have been developed. In particular, several authors have proposed different techniques for model abstraction. In this paper, we leverage on the ontologically well-founded semantics of the modeling language OntoUML to propose a novel approach for model abstraction in conceptual models. We provide a precise definition for a set of Graph-Rewriting rules that can automatically produce much-reduced versions of OntoUML models that concentrate the models' information content around the ontologically essential types in that domain, i.e., the so-called Kinds. The approach has been implemented using a model-based editor and tested over a repository of OntoUML models.

Metaphors are an undeniable part of many forms of art and they hint at the underlying conceptualisation that takes place in the silent conversation between an art piece and its perceiver. Abstract art, in particular, requires the viewer not only to analyse the colour palette and the shapes of the strokes but to subconsciously react to the underlying structures that often define the metaphors. After introducing a few cognitive theories involved in perception and knowledge transfer the paper introduces the reader to the painting "Beneath the Paint". It is an abstract acrylic painting that plays on the two conceptual metaphor structures 'UP is GOOD' and 'DARK is BAD' and by presenting them in contradiction force the viewer to subconsciously choose its primary conceptualisation.

The recent remarkable success of generative AI models to create text and images has already started altering our perspective of intelligence and the “uniqueness” of humanity in this world. Simultaneously, arguments on why AI will never exceed human intelligence are ever-present as seen in Landgrebe and Smith (2022). To address whether machines may rule the world after all, this paper zooms in on one of the aspects of intelligence Landgrebe and Smith (2022) neglected to consider: creativity. Using Rhodes four Ps of creativity as a starting point, this paper evaluates the creative ability in visual generative AI models with respect to the state of the art in creativity theory. The most part of the reflective evaluation is performed through a case study in generating illustrations of dogs using the generative AI tool Midjourney. 

In this book the author's theoretical framework builds on linguistic and psychological research, arguing that similar image-schematic notions should be grouped together into interconnected family hierarchies, with complexity increasing with regard to the addition of spatial and conceptual primitives. She introduces an image schema logic as a language to model image schemas, and she shows how the semantic content of image schemas can be used to improve computational concept invention.

The book will be of value to researchers in artificial intelligence, cognitive science, psychology, and creativity.

In cognitive linguistics, image schemas were introduced as mental generalisations from embodied experiences capturing notions such as CONTAINMENT, SUPPORT and SOURCE_PATH_GOAL movement. These spatiotemporal relationships can be found in human cognition as information skeletons for analogical reasoning, as a grounding factor for abstract language and as conceptual building blocks for concepts and events.

Despite the progress seen in research on artificial intelligence, computational systems still struggle with natural language comprehension, to perform meaningful analogical transfers and to display creative capacity in terms of concept invention. The dissertation’s main hypothesis is that, as image schemas appear to be a key component in these processes in human cognition, an integration of formalised image schemas could advance the computational work in these fields. This dissertation presents the prerequisites to investigate the fruitfulness of this hypothesis, namely, a theoretical framework for the formalisation of image schemas and their integration into computational conceptual blending.

The contribution of the theoretical framework is threefold. First, building on research findings from linguistics and psychology, it is argued that similar image-schematic notions should be grouped together into interconnected family hierarchies, with increasing complexity in regards to the addition of spatial and conceptual primitives. Second, the Image Schema Logic, ISL^FOL, is introduced as a formal language to model image schemas, as well as their combinations. Third, methods for how the semantic content of image schemas could be used to improve computational concept invention is presented.

In addition to the theoretical framework, two empirical studies are presented. The first provides support for the idea that image schemas model conceptualisations for objects and concepts. The second presents linguistic support to structure image schemas as families, as well as providing the first step towards an an automatic method to automatically identify image schemas in natural language.

Metaphor and analogy have been suggested to be at the core of cognition [8]. Conceptual blending, a theory for concept invention, is build on similar mechanisms with the difference that two source domains are merged into a novel domain [3,4]. Investigations into concept invention have lead to an increased interest in the physical body‘s sensorimotor experiences that appears to play a central role in both metaphors and conceptual blending. In embodied/grounded cognition, concepts, including the most abstract ones, are understood to be directly related to these experiences [13,3]. Expressions such as “burning with passion”, “heartbreak” and “life is a roller-coaster” all display bodily experiences.

While embodied theories explain parts of language and conceptual thought, there is limited understanding what these experiences correspond to cognitively. One theory to make these experiences more concrete is the theory of image schemas [9.12]. Understood as conceptual building blocks, they model spatial relationships between objects, an observer, and the environment. Commonly mentioned image schemas are ‘above’, ‘containment’, ‘support’ and ‘path’-following.

Image schemas develop in infancy and become more fine-tuned during cognitive development to adapt to the environment [14,15]. Through analogy they help to build expectations of novel situations (e. g. if ‘support’ has been learned from “tables ‘support’ plates”, “desks ‘support‘ books” can be inferred) and it is in the disruption of expectations that novel image schema concepts are learned and new understandings comes to formed (e. g. how water does not always ‘support’ objects).

Many metaphors utilize these image schemas by instead of transferring attributes they transfer the image schematic relationships [10]. An example is how ‘above’ and up/down schemas explain status and social hierarchies (“She is above my league”, “a career ladder” and “falling from grace”). This phenomenon is also used in the arts. For example musical-pitch is often visualized as a vertical axis [1,2].

Our current work investigates how these image schematic skeletons can be used in computational concept invention in formal approaches to conceptual blending [11], trying to bridge the gap between human language comprehension and computational language production (see [6,7]).

Introductory paragraph: Invited to participate in the documentation project of .co.kr in the %wrong Browser series, I was asked to – to the best of my professional ability – enable the experiencing of a digital art piece in a future when technological advancements and paradigm shifts made it impossible access. Drawing on my scientifc background, my usual research method is to formally structure, identify and analyze semantic micro-patterns of concepts and events with the goal of integrating them into formal systems for artifcial intelligence. Approaching the task in this way, I performed what for lack of a better word could be called a “documentalysis” (the amalgamation of documentation and analysis) on the art piece. This was a form of interactive, experience-based documentation in which I was trying to separate syntactical parts of the art piece and analyze their semantic content.

Image schemas have been proposed to be the conceptual building blocks that constitute the semantic skeleton for concepts, events and narratives. Learned from embodied experiences, they encompass abstract information in notions such as Containment, Source_Path_Goal and Scale. The theory originated from cognitive linguistics as a means to explain the vast prevalence of embodied metaphors and spatial language. However, it has become a valid contribution in many areas investigating the nature of thought. For formal analysis of image schemas, the abstract and undetermined notions in the image schemas require precise and concrete representations. To deal with this, the image schemas can be decomposed into different types of conceptual primitives. By adding or removing these primitives, the schematic narrative changes in subtle but essential ways. To demonstrate the power of using a methodology that isolates these primitives, this paper presents a formal analysis of the transfer of forces and motion by looking at the semantic differences in a selected number of synonyms for ‘pushing’. The analysis is done using the visualisation tool The Diagrammatic Image Schema Language and the notions are formalised using The Image Schema Logic.

In the cognitive sciences, image schemas are considered to be the conceptual building blocks learned from sensorimotor processes in early infancy. They are used in language and higher levels of cognition as information skeletons. Despite the potential of integrating image schemas into formal systems to aid for instance common-sense reasoning, computational analogy and concept invention, normalisations of image schemas are sparse. In particular in respect to their dynamic nature. In this paper, we therefore describe how some of the dynamic aspects of the image schema Containment can be formally approached using an image schema logic based on the Region Connection Calculus (RCC8), the Qualitative Trajectory Calculus (QTC), Ligozat's cardinal directions (CD), and Linear Temporal Logic over the reals (RTL), with 3D Euclidean space assumed for the spatial domain. The distinctions in our formalisations are motivated with concrete examples from natural language, derived from semi-automated image schema extraction, and illustrate that we target some of the essential distinctions regarding containers and movement. 

Image schemas were introduced as mental generalisations learned from the sensorimotor experiences in infancy that in adulthood shape language formation and conceptualisations. So far, little empirical research has been devoted to investigate to which degree image schemas are involved in object conceptualisation more concretely. To address this, this experimental study investigates the relationship between abstract image schemas and their involvement in conceptualisations of common, everyday objects. The experimental set-up asks participants to describe objects using abstract representations of image schemas. The results from the study support the claim that image-schematic thinking is prevalent in the conceptualisation of objects, thus providing empirical evidence for the idea that image schemas can serve as conceptual building blocks for the meaning of objects. 

Even in the absence of verbal language, meaning and nonverbal communication still remain. For abstract domains such as music, poetry and visual arts, perceivers often have a strong intuition about the particular message of individual pieces. One theory for this is the embodied nature of semantics as well as repeated exposure to cultural conventions. It is suggested that embodiment takes the form of mental patterns called image schemas. They are spatiotemporal relationships between objects and their environment. Intrinsically meaningful, they often feature as a structural skeleton for conceptual metaphors, where information from one domain is transferred onto another. These information skeletons are never directly exposed, and instead hidden in between the lines. With the purpose of exposing their presence, we present a series of examples of conventional metaphors and popular cultural references where the image-schematic skeleton is violated. Our brief analysis demonstrates that by manipulating this embodied skeleton, previously non-expressed meaning is revealed.

The theory of image schemas was introduced as a missing link between embodied experience and mental representation. The theory proposes a relatively small number of conceptual building blocks based on spatio-temporal relationships called "image schemas'' upon which reasoning and different forms of communication can be built.  While image schemas often are spoken about as spatio-temporal relationships, rather often the temporal dimension is omitted. Identifying and formally discussing image schemas in their static sense is complicated enough, but it is conceptually impossible to discuss the phenomena of image schemas while ignoring the dynamics of temporal change. For instance, the image schema CONTAINMENT is proposed to be learned from the movement of objects in and out of containers rather than the inside-border-outside relationship presented in cognitive linguistics research. It is a prerequisite that an infant understands in and out movement before it can understand concepts such as enclosure and containment.  Image schemas have found increased interest in research on artificial intelligence as they offer a cognitively inspired bridge to computational concept comprehension and concept invention. One assumption is that the integration of image schemas will enable artificial intelligence and language comprehension tools to support a better 'understanding' of abstract language, conceptual metaphors, or analogies. However, currently the state of rendering image schemas formally has been primarily restricted to describing them as purely static relationships. In order to have a more accurate formal description, the temporal dimensions need more attention. This abstract is intended to highlight the importance of time and change for image schemas, as these constitute some of the most important aspects of these conceptual building blocks. The theory of image schemas is therefore naturally and closely linked to the fields of multi-modal and qualitative modelling, which we intend to explore further in our work, in particular with attention to the cognitive adequacy of the chosen formalisms. Formalising image schemas qualitatively may then employ e.g. temporal logics, trajectory calculus, and a variety of spatial calculi. Here, the appropriate combination of these formal methods is of the essence for capturing the full multi-modality of the respective image schema.

Embodied cognition has emphasised the importance of sensorimotor processes for concept formation. According to this theory, image schemas are suggested to be a cognitive representation of certain basic spatial relationships. At the same time, the shape of objects is essential for categorisation and understanding. While information on both shapes and image schemas are primarily obtained through visual perception, their cognitive nature differs. Shape is an attribute of an object, whereas an image schema is a basic spatial relationship between (several) objects in a particular environment and context.We here discuss interdependencies between these two modes of perception with regard to their roles in concept formation.

Image schemas were introduced as mental “patterns of our perceptual interactions [...] that gives structure to our experience” [1].

Often described as spatiotemporal relationships between objects, agents and their environment, image schemas take the role of a conceptual skeleton of objects, abstract concepts, and the mental construction of events. Thus, they play a central role in metaphors, analogical transfer and novel generation through conceptual blending [2]. For instance, a concept like a “career ladder” can be broken down into the conceptual metaphor “UP is GOOD”, in turn, based on the spatial relationship underlying the image schema Verticality.

While cognitive linguistics and developmental psychology have investigated these conceptual primitives for decades [3,4], the possible impact they have for computer science, in particular artificial intelligence and cognitively-inspired robotics, is less investigated with few exceptions [5].

In the past few years novel work has focused on bridging this gap by formally structuring the abstract image-schematic notions into families of hierarchies. This helps not only their formal representation but aims to solve some of the inconsistency issues regarding terminology for the linguistics and psychology disciplines. To use these for computational purposes in computational conceptual blending, analogy engines and robotics, moreover the image schema logic (ISL) was introduced, a formal language that can represent each individual image-schematic notion. Preliminary results demonstrate how this method can formally model the conceptualization of (to computers) complicated events and take part as heuristics for the construction of novel concepts in blending.

We report on these and further findings which show that the introduction of image schemas is a possible step to improve computational problems in commonsense reasoning, natural language processing and computational creativity

Cognitive linguistics introduced image schemas as a missing link between embodied experiences and high-level conceptualisation in language and metaphorical thinking. They are described as the abstract spatio-temporal relationships that function as conceptual building blocks for everyday concepts and events. Although there is increasing interest in the area of cognitively motivated artificial intelligence, where image schemas are suggested to be a core piece in the puzzle to model human-level conceptualisation and reasoning, so far rather few formal logical approaches can be found in the literature, in particular regarding attention to the dynamic aspects of image schemas. A fundamental problem here is that the typical mainstream approaches in contemporary KR do not map well to various scenarios found in image schema modelling. In this paper, we introduce a spatio-temporal logic for ‘directed movement of objects’, with the aim to model formally image schematic events such as Blockage, Caused_Movement and ‘bouncing’.

Image schemas are recognised as a fundamental ingredient in human cognition and creative thought. They have been studied extensively in areas such as cognitive linguistics. With the goal of exploring their potential role in computational creative systems, we here study the viability of the idea to formalise image schemas as a set of interlinked theories. We discuss in particular a selection of image schemas related to the notion of ‘path’, and show how they can be mapped to a formalised family of microtheories reflecting the different aspects of path following. Finally, we illustrate the potential of this approach in the area of concept invention, namely by providing several examples illustrating in detail in what way formalised image schema families support the computational modelling of conceptual blending.

In cognitive linguistics, image schemas are understood as conceptual building blocks that are learned in early infancy and which shape not only language but conceptualisation as a whole. In this chapter, we discuss the role that image schemas play in concept invention, with a focus on computational conceptual blending. Moreover, we motivate and outline a formalisation approach to image schemas representing them as interlinked families of theories.

Image schemas are recognised as a fundamental ingredient in human cognition and creative thought. They have been studied extensively in areas such as cognitive linguistics. However, the very notion of image schemas is still ill-defined, with varying terminology and definitions throughout the literature. For the purpose of formalising image schemas in order to exploit their role in computational creative systems, we here study the viability of the idea to formalise image schemas as graphs of interlinked theories. We discuss in particular a selection of image schemas related to the notion of ‘path’ and show how they can be mapped to a formalised family of micro theories reflecting the di erent aspects of path following.

In cognitive science, image schemas are identified as fundamental patterns of cognition. They are schematic prelinguistic conceptualisations of events and serve as conceptual building blocks for concepts. This paper proposes that image schemas can play an important role in computational concept invention, namely within the computational realisation of conceptual blending. We propose to build a library of formalised image schemas, and illustrate how they can guide the search for a base space in the concept invention work flow. Their schematic nature is captured by the idea of organising image schemas into families. Formally, they are represented as heterogeneous, interlinked theories.

In cognitive science, image schemas are identified as the fundamental patterns for the cognition of objects, which are perceived, conceptualised and manipulated in space and time. In this paper, we discuss a role for image schemas in computational concept creation. We propose to build a library of formalised image schemas, and illustrate how they can guide the search for a base space in the concept invention workflow. 

Formal knowledge representation struggles to represent the dynamic changes within complex events in a cognitively plausible way. Image schemas, on the other hand, are spatiotemporal relationships used in cognitive science as building blocks to conceptualise objects and events on a high level of abstraction. In this paper, we explore this modelling gap by looking at how image schemas can capture the skeletal information of events and describe segmentation cuts essential for conceptualising dynamic changes. The main contribution of the paper is the introduction of a more systematic approach for the combination of image schemas with one another in order to capture the conceptual representation of complex concepts and events. To reach this goal we use the image schema logic ISL, and, based on foundational research in cognitive linguistics and developmental psychology, we motivate three different methods for the formal combination of image schemas: merge, collection, and structured combination. These methods are then used for formal event segmentation where the changes in image-schematic state generate the points of separation into individual scenes. The paper concludes with a demonstration of our methodology and an ontological analysis of the classic commonsense reasoning problem of ‘cracking an egg’. 

Image schema profiles are described as clusters of spatiotemporal relationships learned from embodied experiences and function as the gathered conceptual information for event concepts. Looking at such profiles allows not only to model aspects of human conceptualisation but also offers a method to approach event conceptualisation for more formal purposes. This article investigates this research program by looking closer at how humans conceptualise events and specifies three combination methods of image schema profiles that each offer different aspects for concept construction. As a proof of concept, we present an in-depth analysis of the classic commonsense reasoning problem of ‘Cracking an Egg’ as a demonstration of how these profiles can be used in formal knowledge representation. This is formalised using the Image Schema Logic, ISLM, a combined logic targeted at the spatiotemporal relationships present in image schemas.

In this extended abstract, we present the Diagrammatic Image Schema Language (DISL). DISL is a structured visual language to represent image-schematic relationships in different situations and linguistic expressions. Building on previous visualisation research, as well as how image schemas can be ontologically organised, DISL offers a more systematic representation language for abstract concepts than previously possible. In particular, we discuss some of the underlying motivations and design decisions for DISL and introduce the conceptual primitives that are covered by DISL. Their use is illustrated with the classic example of OBJECT_INTO_CONTAINER.

Image schemas are mental patterns learned from perceptual experiences capturing conceptual constructions in expressions. In linguistic analysis, their visualizations are often context-dependent without a generalizable structure. Addressing this, we introduce The Diagrammatic Image Schema Language: a formal representation language that systematizes a set of visual combination rules for different conceptual primitives from the cognitive science literature. These primitives are distinguished from a formal point of view to allow for more general application. DISL also contains a logical exchange format in which the diagrams may be made machine-readable. Using DISL, the semantic structure of complex scenarios can be represented and computed. 

Conceptual blending has been proposed as the cognitive machinery for concept generation. While computational approaches to conceptual blending have been implemented with some success, the automatic approaches still struggle to consistently produce concepts and blends that ‘make sense’ and have value. Mechanisms and optimality principles for blending have been introduced, yet their formal integration remains sparse. In this paper, we suggest to partly bypass this problem by identifying some conceptual heuristics for blending. This is done through a top-down analysis of three prototypical superheroes, an exemplary domain for conceptual blends and human imagination. We formalise the superheroes and backtrace their properties into their respective input spaces and from there map the inherited properties to cognitive theories for conceptualisation. It is our belief that computational blending systems could greatly benefit from conceptual heuristics for blending, identified in this top-down fashion. As a proof of concept of the identified superhero-blending heuristics, we blend the superhero ‘Flowerman’.

Dealing with robotic actions in uncertain environments has been demonstrated to be hard. Many classic planning approaches to robotic action make the closed world assumption, rendering them inefficient for everyday household activities, as they function without generalizability to other contexts or the ability to deal with unexpected changes. In contrast, humans robustly execute underspecified instructions in unfamiliar environments. In this paper, we initiate our research program where we propose the use of functional relations in the form of image-schematic micro-theories, formally represented in ISLFOL, to enrich action descriptors with semantic components. It builds on the body of work in embodied cognition showing that human conceptualization of action sequences is founded on abstract patterns learned from physical experiences in the form of spatiotemporal relationships between object, agents and environments. These theories are used to inform action selection mechanisms for behavioral robotics written in EL++ and we argue how these micro-patterns can be applied in a more general way to deal with underspecified action commands and commonsense problem-solving.

In conceptual blending, the transfer of properties from the input spaces relies on a shared semantic base. At the same time, interesting blends are supposed to resolve deep semantic clashes where many concept combinations correspond to impossible blends, i.e. blends whose input spaces lack any obvious similarities. Instead of a shared structure, the blends are based on bidirectional affordance structures. While humans can easily map this information, computational systems for creative constructions require an understanding of how these features relate to one another. In this paper, we discuss this problem from the perspective of linguistics and computational blending and propose a method combining theory weakening and semantic prioritisation. To demonstrate the problem space, we look at the Sesame Street character ‘The Cookie Monster’ and formalise the blending process using description logic.

Going from natural language directions to fully specified executable plans for household robots involves a challenging variety of reasoning steps. In this paper, a processing pipeline to tackle these steps for natural language directions is proposed and implemented. It uses the ontological Socio-physical Model of Activities (SOMA) as a common interface between its components. The pipeline includes a natural language parser and a module for natural language grounding. Several reasoning steps formulate simulation plans, in which robot actions are guided by data gathered using human computation. As a last step, the pipeline simulates the given natural language direction inside a virtual environment. The major advantage of employing an overarching ontological framework is that its asserted facts can be stored alongside the semantics of directions, contextual knowledge, and annotated activity models in one central knowledge base. This allows for a unified and efficient knowledge retrieval across all pipeline components, providing flexibility and reasoning capabilities as symbolic knowledge is combined with annotated sub-symbolic models.

The Distributed Ontology, Model, and Specification Language DOL provides logic-independent structuring, linking, and modularity constructs. Its homogeneous OWL fragment, DOWL, we argue, can be seen as an ideal language for formalising ontology patterns in description logics. It naturally consumes earlier formalisms such as C-OWL or DDL, and extends these with various expressive means useful for the modelling of patterns. To substantiate this, we illustrate DOWL's expressive power with a number of examples, including ontology design patterns, networks of ontologies, and ontology combinations. The latter are used to formalise conceptual blending, based on DOWL features such as renaming, filtering, forgetting, interpretation, and colimit computation. 

We discuss steps towards a formalisation of the principles of an agentive naïve proto-physics, designed to match a level of abstraction that reflects the pre-linguistic conceptualisations and elementary notions of agency, as they develop during early human cognitive development. To this end, we present an agentive extension of the multi-dimensional image schema logic ISL based on variants of STIT theory, thus replacing the temporal dimension of ISL with an action-agnostic theory of agency. To begin grasping the notion of 'animate agent', we apply the newly defined logic to model the image schematic notion of 'self movement' as a means to distinguish the agentive capabilities of a mouse from those of a ball. Finally, we outline the prospects for employing the theory in cognitive robotics.

A major challenge in natural language understanding research in artificial intelligence (AI) has been and still is the grounding of symbols in a representation that allows for rich semantic interpretation, inference, and deduction. Across cognitive linguistics and other disciplines, a number of principled methods for meaning representation of natural language have been proposed that aim to emulate capacities of human cognition. However, little cross-fertilization among those methods has taken place. A joint effort of human-level meaning representation from AI research and from cognitive linguistics holds the potential of contributing new insights to this profound challenge. To this end, this paper presents a first comparison of image schemas to an AI meaning representation system called Conceptual Dependency (CD). Restricting our study to the domain of physical and spatial conceptual primitives, we find connections and mappings from a set of action primitives in CD to a remarkably similar set of image schemas. We also discuss important implications of this connection, from formalizing image schemas to improving meaning representation systems in AI.

One of the problems an agent faces when operating in a partially known, dynamic, sometimes unpredictable environment is to keep track of aspects of the world relevant to its task, and, if possible, restrict its attention to only these aspects. We present our first steps towards constructing a system that combines image schematic knowledge and reasoning with reactive robotics, and which enables perception that focuses on, and keeps track of, relevant entities and relationships. While our approach is more reasoning intensive than is usual in reactive robotics, the formalism we use for inference is fast and allows an agent to adjust, in real time, the complexity of its action selection procedures according to the complexity of the relevant part of the environment. We illustrate our approach with a few simulated examples of robots performing navigation tasks. In some examples, interaction with obstacles is necessary to complete the navigation tasks, adding complexity to the scenario.

Human beings and other biological agents appear driven by curiosity to explore the affordances of their environments. Such exploration is its own reward – children have fun when playing – but it probably also serves the practical purpose of learning theories with which to predict outcomes of actions. Cognitive robots however have yet to match the performance of human beings at learning and reusing manipulation skills. In this paper, we implement a method that emulates the curiosity drive and uses it as a heuristic to guide (simulated) exploration of a particular task – pouring liquids. The result of this exploration is a collection of symbolic rules linking qualitative descriptions of object arrangements and the pouring action with qualitative descriptions of likely outcomes. The manner in which qualitative descriptions of object arrangements and actions are converted to numerical descriptions for the purpose of simulation parametrization is via probability distributions, which themselves are adjusted in the process of simulated exploration. This allows the grounding of the symbolic descriptions to attempt to adapt itself to the task. The resulting symbolic rules form a theory that, together with the probability distributions that ground it in numerical parametrizations, is intended to be used to predict qualitative outcomes or select manners of pouring towards achieving a goal.