20,336 research outputs found
Ontology and Formal Semantics - Integration Overdue
In this note we suggest that difficulties encountered in natural language semantics are, for the most part, due to the use of mere symbol manipulation systems that are devoid of any content. In such systems, where there is hardly any link with our common-sense view of the world, and it is quite difficult to envision how one can formally account for the considerable amount of content that is often implicit, but almost never explicitly stated in our everyday discourse. \ud
The solution, in our opinion, is a compositional semantics grounded in an ontology that reflects our commonsense view of the world and the way we talk about it in ordinary language. In the compositional logic we envision there are ontological (or first-intension) concepts, and logical (or second-intension) concepts, and where the ontological concepts include not only Davidsonian events, but other abstract objects as well (e.g., states, processes, properties, activities, attributes, etc.) \ud
It will be demonstrated here that in such a framework, a number of challenges in the semantics of natural language (e.g., metonymy, intensionality, metaphor, etc.) can be properly and uniformly addressed.\u
Experimental Biological Protocols with Formal Semantics
Both experimental and computational biology is becoming increasingly
automated. Laboratory experiments are now performed automatically on
high-throughput machinery, while computational models are synthesized or
inferred automatically from data. However, integration between automated tasks
in the process of biological discovery is still lacking, largely due to
incompatible or missing formal representations. While theories are expressed
formally as computational models, existing languages for encoding and
automating experimental protocols often lack formal semantics. This makes it
challenging to extract novel understanding by identifying when theory and
experimental evidence disagree due to errors in the models or the protocols
used to validate them. To address this, we formalize the syntax of a core
protocol language, which provides a unified description for the models of
biochemical systems being experimented on, together with the discrete events
representing the liquid-handling steps of biological protocols. We present both
a deterministic and a stochastic semantics to this language, both defined in
terms of hybrid processes. In particular, the stochastic semantics captures
uncertainties in equipment tolerances, making it a suitable tool for both
experimental and computational biologists. We illustrate how the proposed
protocol language can be used for automated verification and synthesis of
laboratory experiments on case studies from the fields of chemistry and
molecular programming
Toward a Formal Semantics for Autonomic Components
Autonomic management can improve the QoS provided by parallel/ distributed
applications. Within the CoreGRID Component Model, the autonomic management is
tailored to the automatic - monitoring-driven - alteration of the component
assembly and, therefore, is defined as the effect of (distributed) management
code. This work yields a semantics based on hypergraph rewriting suitable to
model the dynamic evolution and non-functional aspects of Service Oriented
Architectures and component-based autonomic applications. In this regard, our
main goal is to provide a formal description of adaptation operations that are
typically only informally specified. We contend that our approach makes easier
to raise the level of abstraction of management code in autonomic and adaptive
applications.Comment: 11 pages + cover pag
Distributional Formal Semantics
Natural language semantics has recently sought to combine the complementary
strengths of formal and distributional approaches to meaning. More
specifically, proposals have been put forward to augment formal semantic
machinery with distributional meaning representations, thereby introducing the
notion of semantic similarity into formal semantics, or to define
distributional systems that aim to incorporate formal notions such as
entailment and compositionality. However, given the fundamentally different
'representational currency' underlying formal and distributional approaches -
models of the world versus linguistic co-occurrence - their unification has
proven extremely difficult. Here, we define a Distributional Formal Semantics
that integrates distributionality into a formal semantic system on the level of
formal models. This approach offers probabilistic, distributed meaning
representations that are also inherently compositional, and that naturally
capture fundamental semantic notions such as quantification and entailment.
Furthermore, we show how the probabilistic nature of these representations
allows for probabilistic inference, and how the information-theoretic notion of
"information" (measured in terms of Entropy and Surprisal) naturally follows
from it. Finally, we illustrate how meaning representations can be derived
incrementally from linguistic input using a recurrent neural network model, and
how the resultant incremental semantic construction procedure intuitively
captures key semantic phenomena, including negation, presupposition, and
anaphoricity.Comment: To appear in: Information and Computation (WoLLIC 2019 Special Issue
ADS Formal Semantics
Abstract Database System (ADS) is a data model developed for an enduring medical information system where frequent changes in the conceptual schema are anticipated and multi-level abstraction is required. The mechanism of abstraction in ADS is based on the abstraction operator of the lamba calculus. The formal semantics of a subset of the ADS model is presented using the denotational specification method
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