Dynamic Neuro-Symbolic Knowledge Graph Construction for Zero-shot Commonsense Question Answering

Understanding narratives requires reasoning about implicit world knowledge related to the causes, effects, and states of situations described in text. At the core of this challenge is how to access contextually relevant knowledge on demand and reason over it. In this paper, we present initial studies toward zero-shot commonsense question answering by formulating the task as inference over dynamically generated commonsense knowledge graphs. In contrast to previous studies for knowledge integration that rely on retrieval of existing knowledge from static knowledge graphs, our study requires commonsense knowledge integration where contextually relevant knowledge is often not present in existing knowledge bases. Therefore, we present a novel approach that generates contextually-relevant symbolic knowledge structures on demand using generative neural commonsense knowledge models. Empirical results on two datasets demonstrate the efficacy of our neuro-symbolic approach for dynamically constructing knowledge graphs for reasoning. Our approach achieves significant performance boosts over pretrained language models and vanilla knowledge models, all while providing interpretable reasoning paths for its predictions

Engagement with digital media in home environment and school readiness in croatian preschool children

Our aim was to investigate the use of various digital media for different purposes in home environment and its relation to the level of school readiness, namely graphomotor skills, logical reasoning and letter knowledge in children aged 6 to 7. Children (N=92) were tested for graphomotor skills, logical reasoning and letter knowledge while their parents completed a questionnaire providing us with the data about their own and their children’s access to digital media in home environment. Results show low but significant negative correlations between the time spent using a computer on weekends and the number of letters children can identify correctly, as well as between the time spent using a smartphone on weekends and children’s graphomotor skills.info:eu-repo/semantics/publishedVersio

Self-Modelling in Inference about Absence

Representing the absence of an object requires one to know that they would know if it were present. This form of second-order, counterfactual reasoning critically relies on access to a mental self-model, specifying expected perceptual and cognitive states under different world states. This thesis addresses open questions regarding inference about absence in perceptual decision making: its reliance on prior metacognitive knowledge, relative encapsulation from metacognitive monitoring, neural underpinning, and relation with default-reasoning. I start by showing that in visual search, implicit metacognitive knowledge about spatial attention supports inference about the absence in the first trial of an experiment, and that this knowledge is dissociable from explicit metacognitive knowledge. Further underscoring the richness and complexity of this knowledge, I find that people are able to accurately predict their future search times, even for complex, unfamiliar displays. Participants’ predictions were better aligned with their own search times than with those of other participants, suggesting that this self-knowledge is person-specific. I then ask what factors contribute to confidence in decisions about presence and absence. Reverse-correlation analysis reveals stimulus features that contribute to detection decisions and confidence. I discuss these findings in the context of sensory noise estimation. Using functional MRI, I find that a network of frontal and parietal regions that are implicated in decision confidence are mostly invariant to whether subjective confidence is rated with respect to decisions about presence or absence. In interpreting these results, I formulate computational models that monitor fluctuations in external stimulus strength and in internal attentional states. Finally, in six behavioural experiments, different levels of the cognitive hierarchy are found to be sensitive to different notions of absence. I conclude with a discussion of ways in which inference about absence can be used by cognitive scientists for probing implicit metacognitive beliefs and studying the mental self-model

The relationship between American Sign Language vocabulary and the development of language-based reasoning skills in deaf children

The language-based analogical reasoning abilities of Deaf children are a controversial topic. Researchers lack agreement about whether Deaf children possess the ability to reason using language-based analogies, or whether this ability is limited by a lack of access to vocabulary, both written and signed. This dissertation examines factors that scaffold the development of language-based analogical reasoning through signed language. First it examines how background factors, such as age, race/ethnicity, or additional disabilities can affect the development of language-based analogical reasoning. Second, it looks at how different kinds of American Sign Language (ASL) vocabulary support the development of language-based analogical reasoning. Five-hundred and fifty-six Deaf children were given five tasks from the ASL Assessment Instrument; one analogies task and four vocabulary tasks: an antonyms task, a synonyms task, a definitions task, and a contextual-based vocabulary task. The data showed that background traits can and do affect how well Deaf children reason using language-based analogies. The most important predictor of performance on the analogies task was ASL vocabulary knowledge, although other factors such as age, race/ethnicity, and additional disabilities can impact task performance. The data also showed that ASL vocabulary knowledge that promotes metalinguistic thinking is the best predictor of language-based analogical reasoning abilities. Potential applications to the classroom and to teacher training are also discussed

Belief attribution in deaf and hearing infants

Based on anticipatory looking and reactions to violations of expected events, infants have been credited with ‘theory of mind’ (ToM) knowledge that a person’s search behaviour for an object will be guided by true or false beliefs about the object’s location. However, little is known about the preconditions for looking patterns consistent with belief attribution in infants. In this study, we compared the performance of 17- to 26-month-olds on anticipatory looking in ToM tasks. The infants were either hearing or were deaf from hearing families and thus delayed in communicative experience gained from access to language and conversational input. Hearing infants significantly outperformed their deaf counterparts in anticipating the search actions of a cartoon character that held a false belief about a target-object location. By contrast, the performance of the two groups in a true belief condition did not differ significantly. These findings suggest for the first time that access to language and conversational input contributes to early ToM reasoning

SoTL as Generative Heuristic Methodology for Building Learning Communities

Excerpt: A variety of sources have expounded on the exponential growth of knowledge, and current projections estimate knowledge doubling every one to two years. I would argue that what has exponentially increased is the availability of and our virtually immediate access to larger sets of data and information; however, this access to data or information does not automatically correspond to an increase in knowledge, and even less so to informed judgments with knowledge or “practical reasoning” (Sullivan & Rosin, 2008). We have only to look at our educational systems, colleges or universities, or even our classrooms for evidence of a wealth of accessible information with no corresponding richness of knowledge. Specifically, it is our nascent knowledge of how our students are acquiring and applying their knowledge that has drawn unwanted and in some cases unwarranted criticism of higher education (cf. Bloom, 1987; Bok, 2004; Hacker & Dreifus, 2010; Brandon, 2010: Arum & Roksa, 2011). In sum, many perceive a lack of knowledge about what transpires in our classrooms and the qualifications of our graduates; as a result, higher education, faculty teaching, and student learning are in a national spotlight

ForecastTKGQuestions: A Benchmark for Temporal Question Answering and Forecasting over Temporal Knowledge Graphs

Question answering over temporal knowledge graphs (TKGQA) has recently found increasing interest. TKGQA requires temporal reasoning techniques to extract the relevant information from temporal knowledge bases. The only existing TKGQA dataset, i.e., CronQuestions, consists of temporal questions based on the facts from a fixed time period, where a temporal knowledge graph (TKG) spanning the same period can be fully used for answer inference, allowing the TKGQA models to use even the future knowledge to answer the questions based on the past facts. In real-world scenarios, however, it is also common that given the knowledge until now, we wish the TKGQA systems to answer the questions asking about the future. As humans constantly seek plans for the future, building TKGQA systems for answering such forecasting questions is important. Nevertheless, this has still been unexplored in previous research. In this paper, we propose a novel task: forecasting question answering over temporal knowledge graphs. We also propose a large-scale TKGQA benchmark dataset, i.e., ForecastTKGQuestions, for this task. It includes three types of questions, i.e., entity prediction, yes-no, and fact reasoning questions. For every forecasting question in our dataset, QA models can only have access to the TKG information before the timestamp annotated in the given question for answer inference. We find that the state-of-the-art TKGQA methods perform poorly on forecasting questions, and they are unable to answer yes-no questions and fact reasoning questions. To this end, we propose ForecastTKGQA, a TKGQA model that employs a TKG forecasting module for future inference, to answer all three types of questions. Experimental results show that ForecastTKGQA outperforms recent TKGQA methods on the entity prediction questions, and it also shows great effectiveness in answering the other two types of questions.Comment: Accepted to ISWC 202

BERT Knows Punta Cana is not just Beautiful, it's Gorgeous : Ranking Scalar Adjectives with Contextualised Representations

Adjectives like pretty, beautiful and gorgeous describe positive properties of the nouns they modify but with different intensity. These differences are important for natural language understanding and reasoning. We propose a novel BERT-based approach to intensity detection for scalar adjectives. We model intensity by vectors directly derived from contextualised representations and show they can successfully rank scalar adjectives. We evaluate our models both intrinsically, on gold standard datasets, and on an Indirect Question Answering task. Our results demonstrate that BERT encodes rich knowledge about the semantics of scalar adjectives, and is able to provide better quality intensity rankings than static embeddings and previous models with access to dedicated resources.Peer reviewe

INFRAWEBS semantic web service development on the base of knowledge management layer

The paper gives an overview about the ongoing FP6-IST INFRAWEBS project and describes the main layers and software components embedded in an application oriented realisation framework. An important part of INFRAWEBS is a Semantic Web Unit (SWU) – a collaboration platform and interoperable middleware for ontology-based handling and maintaining of SWS. The framework provides knowledge about a specific domain and relies on ontologies to structure and exchange this knowledge to semantic service development modules. INFRAWEBS Designer and Composer are sub-modules of SWU responsible for creating Semantic Web Services using Case-Based Reasoning approach. The Service Access Middleware (SAM) is responsible for building up the communication channels between users and various other modules. It serves as a generic middleware for deployment of Semantic Web Services. This software toolset provides a development framework for creating and maintaining the full-life-cycle of Semantic Web Services with specific application support