Estimating Commonsense Knowledge from a Linguistic Analysis on Information Distribution (2024)

Abstract: Commonsense Knowledge (CSK) is defined as a complex and multifaceted structure, encompassing a wide range of knowledge and reasoning generally acquired through everyday experiences. As CSK is often implicit in communication, it poses a challenge for AI systems to simulate human-like interaction. This work aims to deepen the CSK information structure from a linguistic perspective, starting from its organisation in conversations. To achieve this goal, we developed a three-level analysis model to extract more insights about this knowledge, focusing our attention on the second level. In particular, we aimed to extract the distribution of explicit actions and their execution order in the communicative flow. We built an annotation scheme based on FrameNet and applied it to a dialogical corpus on the culinary domain. Preliminary results indicate that certain frames occur earlier in the dialogues, while others occur towards the process’s end. These findings contribute to the systematic nature of actions by establishing clear patterns and relationships between frames.

## introduction the development of high-quality artificial intelligence hinges on the critical challenge of equipping machines with commonsense knowledge (csk) @xcite . this is essential for implementing systems that can elevate human-machine interaction to a more human-like level. the csk was described as embodying the fundamental understanding of causal relationships, physical properties, social norms, and cultural references, crucial for effective communication and problem-solving in everyday situations @xcite . due to its multifaceted nature, csk is generally taken for granted and is typically omitted in communication (written or oral) @xcite , except in cases of ambiguity or when the listener requires clarification @xcite . in this regard, the field of knowledge representation (kr) has made significant contributions to the acquisition and application of csk, leading to the design and construction of resources containing such information @xcite . nevertheless, since it is impossible to represent all human knowledge in one single resource @xcite , we posit that the most intriguing aspect of csk lies in its consideration as a process, rather than a static collection of information. modelling the processes underlying csk directly from linguistic data presents a more significant challenge compared to representing predefined knowledge. in this work, we propose a three-level analysis model to investigate the structure of csk. specifically, we focus on the second level, which entails a semantic analysis grounded in frame semantics @xcite and framenet @xcite applied to a dialogic corpus on the culinary domain. by analysing the frames distribution within the dialogues, we aim to extract insights that can, in future work, contribute to a more comprehensive understanding of csk structure. the paper is organised as follows: section 2 provides a general overview of the state of the art and section 3 outlines the scope of the study. section 4 discusses the motivation behind selecting the culinary domain and the development of the knowledge base, which underpins the three-level analysis model detailed in section 5. section 6 describes the annotation scheme applied to the cookdial dialogical corpus and the methodology used for retrieving frames distribution. finally, section 7 presents the results obtained, followed by the conclusions and future work in section 8. ## state of the art the csk has been described as embodying the fundamental understanding of the world shared among individuals, including (i) information about events that occur over time, (ii) the consequences of one's own and others' actions, (iii) the characteristics of physical objects, (iv) their perceptions, (v) their properties, and (vi) its interrelationships @xcite . a universally shared academic definition of csk does not currently exist. nevertheless, @xcite attempted to limit the scope of the investigation by identifying the most representative characteristics that provide a complete description of this type of knowledge, such as (i) sharedness, (ii) fundamentality, (iii) implicitness. from a linguistic perspective, these features appear interesting as they recall some aspects of the communal common ground (ccg) @xcite , one of the four typologies of common ground (cg) @xcite . despite the ccg and the csk involving shared understandings and assumptions, these are essentially distinct concepts: ccg implies a specific connection between an individual and other members of a shared community, emphasising the interaction between the interlocutors; in contrast, csk concerns an individual's interaction with the world at large, often shared implicitly and unconsciously @xcite . ccg involves active agreement between speakers, establishing shared beliefs and defining a common language for group identities and boundaries (macwhinney and o'grady, 2015). on the other hand, csk does not require explicit agreement, assuming that it is already universally shared among speakers @xcite . the ongoing need for advancements in equipping ai with robust and adaptable csk capabilities has provided a significant stimulus for research in kr, which has contributed to the development of large-scale csk databases @xcite @xcite . although significant progress has been made in this regard @xcite , limitations persist in their ability to capture the open-ended semiotic process, where significance is continuously crafted, contested, and renegotiated within shifting horizons of understanding @xcite . ## objectives given the vast amount of information that csk encapsulates and the limitations of aforementioned state-of-the-art approaches, we rather frame it as a process. in this case, knowledge is understood as a process that generates structured relationships between actions and entities resulting from recurrent interactions stored in a database, and not as a mere repository of pre-existing facts. indeed, it is more intriguing to analyse the processes by which this knowledge is formed rather than dwelling on its representation. our goal is, therefore, to uncover the processes that comprise this knowledge, introducing a three-level analysis designed to extract more detailed information about csk structure. for the scope of this work, the focus is on the second level, where we aim to identify the distribution of explicit semantic information within the communicative flow in the culinary domain. in future work, this analysis will facilitate the identification and schematisation of implicit information in a given domain. in particular, this will be possible by considering csk as the result of the analysis of graph patterns and their probability. ## data sources for our investigation, we took into account the culinary domain, guided by two main factors: (i) culinary practices are presumed to be highly familiar due to their everyday nature -most people routinely prepare meals; (ii) the domain exhibits strong action co-occurrences, as individual actions are linked (e.g., the action of beating eggs implies the action of cracking eggs). to facilitate the identification of the entities and actions involved in recipe instructions along with their relationships and cooccurrences, the initial step involves constructing our knowledge graph. we employed three main resources: the recipe1m+ dataset @xcite , flavordb @xcite , and the epic-kitchens dataset @xcite , collectively representing the knowledge base of ingredients, recipe titles with instructions, food flavours, and information about daily activities performed in the kitchen that are not explicitly mentioned in recipe instructions (e.g., take eggs -crack eggs -throw eggshell into bin). the domain construction follows the methodology described in @xcite , where multiple sources were integrated into neo4j @xcite . this data organisation facilitates the cross-referencing of information, enabling the establishment of intricate relationships within the domain. for carrying out the linguistic analysis, the cook-dial dialogue corpus @xcite is employed. the corpus comprises 260 human-tohuman english dialogues based on the culinary proceedings of clib 2024 domain, in which an agent, given a recipe document extracted from the risec corpus @xcite , guides the user to cook a dish. data were collected by applying the experimental wizard-of-oz method (fraser and gilbert, 1991), involving two participants interacting via a live chat platform. the application setup simulated the interaction between a voice assistant (agent) and a user. the agent had full access to the text of the recipe, while the user only knew its title. from this corpus was possible to identify actions relevant to the preparation of dishes, analysing their distribution within the dialogue flow. ## analysis model the foundation of a good communication is a set of regulative principles that facilitate its success, managing dialogue in accordance with logical and relevant criteria, as well as respecting the principle of cooperation between speakers. the maxim of quality @xcite states that the contribution to the conversation should be as informative as is required. therefore, a speaker is not expected to provide an excess or deficiency of information; rather, they will offer only the necessary information. consequently, people typically assume a division of the knowledge they share (whiting and watts, 2024). although some information is explicitly introduced into the discourse, some other is assumed and not explicitly discussed, agreed upon, or questioned @xcite . knowing what it can be presupposed and what must be made explicit, in other words, showing communicative competence @xcite , still represents a challenge for conversational agents. for this reason, we propose to classify this knowledge into three typologies: foreground knowledge, background knowledge and presupposed knowledge. this classification allows us for a more structured approach to managing information, thereby facilitate a clearer understanding and more effective analysis of the data. we define foreground knowledge as information explicitly expressed in both oral communication and written texts. in contrast, background knowledge refers to basic fundamental information about entities often left omitted. lastly, following the semantic-pragmatic approach to presuppositions @xcite , we categorise as presupposed knowledge the implicit information automatically inferred by speakers. these typologies are interrelated, as the former facilitates the accurate interpretation of the latter. though instructions for whisking the eggs may not explicitly mention it, we inherently infer essential presupposed knowledge, including prior actions like egg-breaking and the use of a tool (e.g., a fork) for the beating process, as long as the background knowledge about the nature of eggs themselves (e.g., eggs are liquid and can be beaten). to uncover the processes underlying the foreground information, a three-level analysis is presented and summarised in figure (1 ). 1. i level. this level relates to the comprehensive ontological knowledge about entities and actions. this knowledge is represented by sources integrated into the graph database described in section 4. the action whisk the eggs assumes that the knowledge of the entity egg is already available for the hearer, regardless of whether it has been explicitly described in the dialogue or not. this assumption is based on the fact that the knowledge of the object is part of the shared understanding of the world. this ontological information can be retrieved by querying the database when necessary (e.g., i need to know the state of an ingredient to perform actions). ## methodology to identify the semantic characteristics of foreground information, an annotation scheme was developed using the framenet lexical database @xcite based on frame semantics @xcite for describing word senses. a semantic frame is defined as a coherent structure of concepts which evokes a situation, an event or a state along with its participants. in framenet, each concept (frame) is schematised with its definition, its examples, and its frame elements, which represents the semantic roles required by the lexical unit (lu) evoking the frame. the sentence bake the cookies at 350 degrees corresponds to apply heat frame described as follows: a cook applies heat to food, where the temperature setting of the heat and duration of application may be specified. a heating instrument, generally indicated by a locative phrase, may also be expressed. cook, food, temperature setting, heating instrument, duration are the fes of the frame. words such as fry, bake or boil represent the lus evoking the frame. in this work, we identified 29 domain-based frames (defined as frame intent, fi) along with their fe, as shown in to gain frame recurrences and their positions within dialogues, we annotated 46 dialogues using label studio @xcite (2), an open-source data labelling platform which facilitates the creation of annotated datasets. two annotators were engaged to annotate the first ten dialogues, ensuring the annotation agreement. the masi (measuring agreement on set-valued items) distance @xcite was employed as it is particularly useful for handling multiple labels for a single item, ranging from 1 to indicate identical sets, to 0 to indicate completely disjointed sets. additionally, the krippendorff's alpha @xcite was applied to assess the annotation quality, calculating the metric of weighted agreement. results show an agreement value of 0.75, confirming the validity of the annotation scheme. ## results following the completion of the annotation phase, we extracted the dialogues from the platform, executing a python script to ascertain the fi's distribution within the dialogue stream. for enhanced visualisation purposes, the data was then converted into a graphical representation, as illustrated in (3). from 29 frames, we identified 19 relevant for our analysis. results show that certain fi as taking (e.g. take a bowl), soaking (e.g. soak the chicken), emptying (e.g. drain the turkey), cause temperature change (e.g. preheat the oven to 400 degrees), and cause change of phase (e.g. melt 1/4 cup butter) occur earlier, while cause to continue (e.g. keep the chicken warm), cause to move in place (e.g. turn the pancake), reshaping (e.g. roll up each crepes), placing (e.g. put the chicken on plate) and closure (e.g. seal the bag) oc-cur towards the process's end. this distribution reflects the natural flow of a culinary task, where initial steps involve preparing ingredients (taking, soaking, emptying) and manipulating temperature (cause temperature change, cause change of phase), while later stages focus on cooking food (cause to move in place), monitoring progress (cause to continue), modelling the shape (reshaping) and finalising the process (placing, closure). the specific action sequences that frequently occur at particular points in the dialogue enable a deeper investigation into presupposed knowledge and facilitate the extraction of action co-occurrences semantically implied by the foreground knowledge. ## conclusions and future works in this paper, we proposed a three-level analysis for deepen the investigation of csk structure. in particular, we focused on the second level, annotating 46 dialogues extracted from the cookdial corpus to calculate fi recurrences and their positions within dialogues. the analysis revealed that there are fi predominantly appeared in the initial stages of the dialogue and others towards the end of it, reflecting the natural flow of a cooking process. those results hold significant importance as they contribute to the systematic nature of this information by establishing clear patterns and relationships between frames. a further study is underway on epic kitchens, allowing us to identify presupposed actions that can be omitted from recipe instructions without impacting completion.