243 research outputs found
Ontology Reasoning with Deep Neural Networks
The ability to conduct logical reasoning is a fundamental aspect of
intelligent behavior, and thus an important problem along the way to
human-level artificial intelligence. Traditionally, symbolic logic-based
methods from the field of knowledge representation and reasoning have been used
to equip agents with capabilities that resemble human logical reasoning
qualities. More recently, however, there has been an increasing interest in
using machine learning rather than symbolic logic-based formalisms to tackle
these tasks. In this paper, we employ state-of-the-art methods for training
deep neural networks to devise a novel model that is able to learn how to
effectively perform logical reasoning in the form of basic ontology reasoning.
This is an important and at the same time very natural logical reasoning task,
which is why the presented approach is applicable to a plethora of important
real-world problems. We present the outcomes of several experiments, which show
that our model learned to perform precise ontology reasoning on diverse and
challenging tasks. Furthermore, it turned out that the suggested approach
suffers much less from different obstacles that prohibit logic-based symbolic
reasoning, and, at the same time, is surprisingly plausible from a biological
point of view
LOGICSEG: Parsing Visual Semantics with Neural Logic Learning and Reasoning
Current high-performance semantic segmentation models are purely data-driven
sub-symbolic approaches and blind to the structured nature of the visual world.
This is in stark contrast to human cognition which abstracts visual perceptions
at multiple levels and conducts symbolic reasoning with such structured
abstraction. To fill these fundamental gaps, we devise LOGICSEG, a holistic
visual semantic parser that integrates neural inductive learning and logic
reasoning with both rich data and symbolic knowledge. In particular, the
semantic concepts of interest are structured as a hierarchy, from which a set
of constraints are derived for describing the symbolic relations and formalized
as first-order logic rules. After fuzzy logic-based continuous relaxation,
logical formulae are grounded onto data and neural computational graphs, hence
enabling logic-induced network training. During inference, logical constraints
are packaged into an iterative process and injected into the network in a form
of several matrix multiplications, so as to achieve hierarchy-coherent
prediction with logic reasoning. These designs together make LOGICSEG a general
and compact neural-logic machine that is readily integrated into existing
segmentation models. Extensive experiments over four datasets with various
segmentation models and backbones verify the effectiveness and generality of
LOGICSEG. We believe this study opens a new avenue for visual semantic parsing.Comment: ICCV 2023 (Oral). Code: https://github.com/lingorX/LogicSeg
Saliency Map Verbalization: Comparing Feature Importance Representations from Model-free and Instruction-based Methods
Saliency maps can explain a neural model's predictions by identifying
important input features. They are difficult to interpret for laypeople,
especially for instances with many features. In order to make them more
accessible, we formalize the underexplored task of translating saliency maps
into natural language and compare methods that address two key challenges of
this approach -- what and how to verbalize. In both automatic and human
evaluation setups, using token-level attributions from text classification
tasks, we compare two novel methods (search-based and instruction-based
verbalizations) against conventional feature importance representations
(heatmap visualizations and extractive rationales), measuring simulatability,
faithfulness, helpfulness and ease of understanding. Instructing GPT-3.5 to
generate saliency map verbalizations yields plausible explanations which
include associations, abstractive summarization and commonsense reasoning,
achieving by far the highest human ratings, but they are not faithfully
capturing numeric information and are inconsistent in their interpretation of
the task. In comparison, our search-based, model-free verbalization approach
efficiently completes templated verbalizations, is faithful by design, but
falls short in helpfulness and simulatability. Our results suggest that
saliency map verbalization makes feature attribution explanations more
comprehensible and less cognitively challenging to humans than conventional
representations.Comment: ACL 2023 Workshop on Natural Language Reasoning and Structured
Explanations (NLRSE
From fuzzy to annotated semantic web languages
The aim of this chapter is to present a detailed, selfcontained and comprehensive account of the state of the art in representing and reasoning with fuzzy knowledge in Semantic Web Languages such as triple languages RDF/RDFS, conceptual languages of the OWL 2 family and rule languages. We further show how one may generalise them to so-called annotation domains, that cover also e.g. temporal and provenance extensions
Current and Future Challenges in Knowledge Representation and Reasoning
Knowledge Representation and Reasoning is a central, longstanding, and active
area of Artificial Intelligence. Over the years it has evolved significantly;
more recently it has been challenged and complemented by research in areas such
as machine learning and reasoning under uncertainty. In July 2022 a Dagstuhl
Perspectives workshop was held on Knowledge Representation and Reasoning. The
goal of the workshop was to describe the state of the art in the field,
including its relation with other areas, its shortcomings and strengths,
together with recommendations for future progress. We developed this manifesto
based on the presentations, panels, working groups, and discussions that took
place at the Dagstuhl Workshop. It is a declaration of our views on Knowledge
Representation: its origins, goals, milestones, and current foci; its relation
to other disciplines, especially to Artificial Intelligence; and on its
challenges, along with key priorities for the next decade
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