30,365 research outputs found
The Information-Flow Approach to Ontology-Based Semantic Integration
In this article we argue for the lack of formal foundations for ontology-based semantic alignment. We analyse and formalise the basic notions of semantic matching and alignment and we situate them in the context of ontology-based alignment in open-ended and distributed environments, like the Web. We then use the mathematical notion of information flow in a distributed system to ground three hypotheses that enable semantic alignment. We draw our exemplar applications of this work from a variety of interoperability scenarios including ontology mapping, theory of semantic interoperability, progressive ontology alignment, and situated semantic alignment
How COVID-19 Is Changing Our Language : Detecting Semantic Shift in Twitter Word Embeddings
Words are malleable objects, influenced by events that are reflected in
written texts. Situated in the global outbreak of COVID-19, our research aims
at detecting semantic shifts in social media language triggered by the health
crisis. With COVID-19 related big data extracted from Twitter, we train
separate word embedding models for different time periods after the outbreak.
We employ an alignment-based approach to compare these embeddings with a
general-purpose Twitter embedding unrelated to COVID-19. We also compare our
trained embeddings among them to observe diachronic evolution. Carrying out
case studies on a set of words chosen by topic detection, we verify that our
alignment approach is valid. Finally, we quantify the size of global semantic
shift by a stability measure based on back-and-forth rotational alignment
Learning Semantic Correspondences in Technical Documentation
We consider the problem of translating high-level textual descriptions to
formal representations in technical documentation as part of an effort to model
the meaning of such documentation. We focus specifically on the problem of
learning translational correspondences between text descriptions and grounded
representations in the target documentation, such as formal representation of
functions or code templates. Our approach exploits the parallel nature of such
documentation, or the tight coupling between high-level text and the low-level
representations we aim to learn. Data is collected by mining technical
documents for such parallel text-representation pairs, which we use to train a
simple semantic parsing model. We report new baseline results on sixteen novel
datasets, including the standard library documentation for nine popular
programming languages across seven natural languages, and a small collection of
Unix utility manuals.Comment: accepted to ACL-201
Progressive Ontology Alignment for Meaning Coordination: an Information-Theoretic Foundation
We elaborate on the mathematical foundations of the meaning coordination problem that agents face in open environments. We investigate to which extend the Barwise-Seligman theory of information flow provides a faithful theoretical description of the partial semantic integration that two agents achieve as they progressively align their underlying ontologies through the sharing of tokens, such as instances. We also discuss the insights and practical implications of the Barwise-Seligman theory with respect to the general meaning coordination proble
Capturing lexical variation in MT evaluation using automatically built sense-cluster inventories
The strict character of most of the existing Machine Translation (MT) evaluation metrics does not permit them to capture lexical variation in translation. However, a central
issue in MT evaluation is the high correlation that the metrics should have with human judgments of translation quality. In order to achieve a higher correlation, the identification of sense correspondences between the compared translations becomes really important. Given
that most metrics are looking for exact correspondences, the evaluation results are often misleading concerning translation quality. Apart from that, existing metrics do not permit one to make a conclusive estimation of the impact of Word Sense Disambiguation techniques into
MT systems. In this paper, we show how information acquired by an unsupervised semantic analysis method can be used to render MT evaluation more sensitive to lexical semantics. The sense inventories built by this data-driven method are incorporated into METEOR: they replace WordNet for evaluation in English and render METEORās synonymy module operable in French. The evaluation results demonstrate that the use of these inventories gives rise to an increase in the number of matches and the correlation with human judgments of translation quality, compared to precision-based metrics
Institutionalising Ontology-Based Semantic Integration
We address what is still a scarcity of general mathematical foundations for ontology-based semantic integration underlying current knowledge engineering methodologies in decentralised and distributed environments. After recalling the first-order ontology-based approach to semantic integration and a formalisation of ontological commitment, we propose a general theory that uses a syntax-and interpretation-independent formulation of language, ontology, and ontological commitment in terms of institutions. We claim that our formalisation generalises the intuitive notion of ontology-based semantic integration while retaining its basic insight, and we apply it for eliciting and hence comparing various increasingly complex notions of semantic integration and ontological commitment based on differing understandings of semantics
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