63 research outputs found
A Boxology of Design Patterns for Hybrid Learning and Reasoning Systems
We propose a set of compositional design patterns to describe a large variety
of systems that combine statistical techniques from machine learning with
symbolic techniques from knowledge representation. As in other areas of
computer science (knowledge engineering, software engineering, ontology
engineering, process mining and others), such design patterns help to
systematize the literature, clarify which combinations of techniques serve
which purposes, and encourage re-use of software components. We have validated
our set of compositional design patterns against a large body of recent
literature.Comment: 12 pages,55 reference
Modular Design Patterns for Hybrid Learning and Reasoning Systems: a taxonomy, patterns and use cases
The unification of statistical (data-driven) and symbolic (knowledge-driven)
methods is widely recognised as one of the key challenges of modern AI. Recent
years have seen large number of publications on such hybrid neuro-symbolic AI
systems. That rapidly growing literature is highly diverse and mostly
empirical, and is lacking a unifying view of the large variety of these hybrid
systems. In this paper we analyse a large body of recent literature and we
propose a set of modular design patterns for such hybrid, neuro-symbolic
systems. We are able to describe the architecture of a very large number of
hybrid systems by composing only a small set of elementary patterns as building
blocks.
The main contributions of this paper are: 1) a taxonomically organised
vocabulary to describe both processes and data structures used in hybrid
systems; 2) a set of 15+ design patterns for hybrid AI systems, organised in a
set of elementary patterns and a set of compositional patterns; 3) an
application of these design patterns in two realistic use-cases for hybrid AI
systems. Our patterns reveal similarities between systems that were not
recognised until now. Finally, our design patterns extend and refine Kautz'
earlier attempt at categorising neuro-symbolic architectures.Comment: 20 pages, 22 figures, accepted for publication in the International
Journal of Applied Intelligenc
Combining machine learning and semantic web: A systematic mapping study
In line with the general trend in artificial intelligence research to create intelligent systems that combine learning and symbolic components, a new sub-area has emerged that focuses on combining Machine Learning components with techniques developed by the Semantic Web community - Semantic Web Machine Learning (SWeML). Due to its rapid growth and impact on several communities in thepast two decades, there is a need to better understand the space of these SWeML Systems, their characteristics, and trends. Yet, surveys that adopt principled and unbiased approaches are missing. To fill this gap, we performed a systematic study and analyzed nearly 500 papers published in the past decade in this area, where we focused on evaluating architectural and application-specific features. Our analysis identified a rapidly growing interest in SWeML Systems, with a high impact on several application domains and tasks. Catalysts for this rapid growth are the increased application of deep learning and knowledge graph technologies. By leveraging the in-depth understanding of this area acquired through this study, a further key contribution of this article is a classification system for SWeML Systems that we publish as ontology.</p
Describing and Organizing Semantic Web and Machine Learning Systems in the SWeMLS-KG
In line with the general trend in artificial intelligence research to create
intelligent systems that combine learning and symbolic components, a new
sub-area has emerged that focuses on combining machine learning (ML) components
with techniques developed by the Semantic Web (SW) community - Semantic Web
Machine Learning (SWeML for short). Due to its rapid growth and impact on
several communities in the last two decades, there is a need to better
understand the space of these SWeML Systems, their characteristics, and trends.
Yet, surveys that adopt principled and unbiased approaches are missing. To fill
this gap, we performed a systematic study and analyzed nearly 500 papers
published in the last decade in this area, where we focused on evaluating
architectural, and application-specific features. Our analysis identified a
rapidly growing interest in SWeML Systems, with a high impact on several
application domains and tasks. Catalysts for this rapid growth are the
increased application of deep learning and knowledge graph technologies. By
leveraging the in-depth understanding of this area acquired through this study,
a further key contribution of this paper is a classification system for SWeML
Systems which we publish as ontology.Comment: Preprint of a paper in the resource track of the 20th Extended
Semantic Web Conference (ESWC'23
Graph Neural Networks Meet Neural-Symbolic Computing: A Survey and Perspective
Neural-symbolic computing has now become the subject of interest of both
academic and industry research laboratories. Graph Neural Networks (GNN) have
been widely used in relational and symbolic domains, with widespread
application of GNNs in combinatorial optimization, constraint satisfaction,
relational reasoning and other scientific domains. The need for improved
explainability, interpretability and trust of AI systems in general demands
principled methodologies, as suggested by neural-symbolic computing. In this
paper, we review the state-of-the-art on the use of GNNs as a model of
neural-symbolic computing. This includes the application of GNNs in several
domains as well as its relationship to current developments in neural-symbolic
computing.Comment: Updated version, draft of accepted IJCAI2020 Survey Pape
Software design patterns for ai-systems
Well-established design patterns offer the possibility of standardized construction of software systems and can be used in various ways. The systematic use of design patterns in the field of Artificial Intelligence (AI) Systems however, has received little attention so far, despite AI being a popular research area in recent years. AI systems can be used for a wide variety of applications and play an increasingly important role in business and everyday life. AI systems are becoming more complex however, the actual machine learning (ML) task comprises only a small part of the total source code of a system. In order to maintain a clear and structured architecture for such systems and to allow easy maintenance, standardized elements should be reused in the design. This paper describes possible applications of well-known design patterns in AI systems to improve traceability of the system design
Is Neuro-Symbolic AI Meeting its Promise in Natural Language Processing? A Structured Review
Advocates for Neuro-Symbolic Artificial Intelligence (NeSy) assert that
combining deep learning with symbolic reasoning will lead to stronger AI than
either paradigm on its own. As successful as deep learning has been, it is
generally accepted that even our best deep learning systems are not very good
at abstract reasoning. And since reasoning is inextricably linked to language,
it makes intuitive sense that Natural Language Processing (NLP), would be a
particularly well-suited candidate for NeSy. We conduct a structured review of
studies implementing NeSy for NLP, with the aim of answering the question of
whether NeSy is indeed meeting its promises: reasoning, out-of-distribution
generalization, interpretability, learning and reasoning from small data, and
transferability to new domains. We examine the impact of knowledge
representation, such as rules and semantic networks, language structure and
relational structure, and whether implicit or explicit reasoning contributes to
higher promise scores. We find that systems where logic is compiled into the
neural network lead to the most NeSy goals being satisfied, while other factors
such as knowledge representation, or type of neural architecture do not exhibit
a clear correlation with goals being met. We find many discrepancies in how
reasoning is defined, specifically in relation to human level reasoning, which
impact decisions about model architectures and drive conclusions which are not
always consistent across studies. Hence we advocate for a more methodical
approach to the application of theories of human reasoning as well as the
development of appropriate benchmarks, which we hope can lead to a better
understanding of progress in the field. We make our data and code available on
github for further analysis.Comment: Surve
A Research Agenda for Hybrid Intelligence:Augmenting Human Intellect With Collaborative, Adaptive, Responsible, and Explainable Artificial Intelligence
We define hybrid intelligence (HI) as the combination of human and machine intelligence, augmenting human intellect and capabilities instead of replacing them and achieving goals that were unreachable by either humans or machines. HI is an important new research focus for artificial intelligence, and we set a research agenda for HI by formulating four challenges
Hybrid Learning as an Optional Language Learning Model in the Post Pandemic Era: A Systematic Literature Review
In the post-pandemic era, several technologies are recommended to address some of the issues faced by English language learning. The hybrid learning model comes to answer this challenge by utilizing technology through integrated online and offline learning in the classroom at the same time. Therefore, the paper aims to present a better understanding of hybrid learning in the English teaching and learning process during the post-pandemic era. The systematic searching method was applied, and 23 articles published from 2020 until 2023 were selected for this paper. Most of them were written in Asian nations. The finding shows that hybrid learning was well received by both teachers and students. Since this teaching model paved the way and provided a new window for English language practice, it has also been underlined by many academics and is being applied in English teaching and learning. In conclusion, the hybrid learning model can be an optional language learning model in the post-pandemic era
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