83 research outputs found
Hybrid Query Answering Over OWL Ontologies
Abstract. Query answering over OWL 2 DL ontologies is an important reasoning task for many modern applications. Unfortunately, due to its high computational complexity, OWL 2 DL systems are still not able to cope with datasets containing billions of data. Consequently, application developers often employ provably scalable systems which only support a fragment of OWL 2 DL and which are, hence, most likely incomplete for the given input. However, this notion of completeness is too coarse since it implies that there exists some query and some dataset for which these systems would miss answers. Nevertheless, there might still be a large number of user queries for which they can compute all the right answers even over OWL 2 DL ontologies. In the current paper, we investigate whether, given a query Q with only distinguished variables over an OWL 2 DL ontology T and a system ans, it is possible to identify in an efficient way if ans is complete for Q, T and every dataset. We give sufficient conditions for (in)completeness and present a hybrid query answering algorithm which uses ans when it is complete, otherwise it falls back to a fully-fledged OWL 2 DL reasoner. However, even in the latter case, our algorithm still exploits ans as much as possible in order to reduce the search space of the OWL 2 DL reasoner. Finally, we have implemented our approach using a concrete system ans and OWL 2 DL reasoner obtaining encouraging results.
A survey on knowledge-enhanced multimodal learning
Multimodal learning has been a field of increasing interest, aiming to
combine various modalities in a single joint representation. Especially in the
area of visiolinguistic (VL) learning multiple models and techniques have been
developed, targeting a variety of tasks that involve images and text. VL models
have reached unprecedented performances by extending the idea of Transformers,
so that both modalities can learn from each other. Massive pre-training
procedures enable VL models to acquire a certain level of real-world
understanding, although many gaps can be identified: the limited comprehension
of commonsense, factual, temporal and other everyday knowledge aspects
questions the extendability of VL tasks. Knowledge graphs and other knowledge
sources can fill those gaps by explicitly providing missing information,
unlocking novel capabilities of VL models. In the same time, knowledge graphs
enhance explainability, fairness and validity of decision making, issues of
outermost importance for such complex implementations. The current survey aims
to unify the fields of VL representation learning and knowledge graphs, and
provides a taxonomy and analysis of knowledge-enhanced VL models
Choose your Data Wisely: A Framework for Semantic Counterfactuals
Counterfactual explanations have been argued to be one of the most intuitive
forms of explanation. They are typically defined as a minimal set of edits on a
given data sample that, when applied, changes the output of a model on that
sample. However, a minimal set of edits is not always clear and understandable
to an end-user, as it could, for instance, constitute an adversarial example
(which is indistinguishable from the original data sample to an end-user).
Instead, there are recent ideas that the notion of minimality in the context of
counterfactuals should refer to the semantics of the data sample, and not to
the feature space. In this work, we build on these ideas, and propose a
framework that provides counterfactual explanations in terms of knowledge
graphs. We provide an algorithm for computing such explanations (given some
assumptions about the underlying knowledge), and quantitatively evaluate the
framework with a user study.Comment: To appear at IJCAI 202
Connected Components and Disjunctive Existential Rules
In this paper, we explore conjunctive query rewriting, focusing on queries
containing universally quantified negation within the framework of disjunctive
existential rules. We address the undecidability of the existence of a finite
and complete UCQ-rewriting and the identification of finite unification sets
(fus) of rules. We introduce new rule classes, connected linear rules and
connected domain restricted rules, that exhibit the fus property for
existential rules. Additionally, we propose disconnected disjunction for
disjunctive existential rules to achieve the fus property when we extend the
introduced rule fragments to disjunctive existential rules. We present
ECOMPLETO, a system for efficient query rewriting with disjunctive existential
rules, capable of handling UCQs with universally quantified negation. Our
experiments demonstrate ECOMPLETO's consistent ability to produce finite
UCQ-rewritings and describe the performance on different ontologies and
queries.Comment: 23 pages, 4 figure
Visualising key information and communication technologies (ICT) indicators for children and young individuals in Europe
DGmap is an online interactive tool that visualises indicators drawn from large-scale European and international databases reflecting the use of information and communication technologies (ICT) amongst children and young individuals in Europe. A large number of indicators are estimated and visualised on an interactive map revealing convergences and divergences amongst European countries. Apart from its main feature, that of facilitating users to observe discrepancies between countries, the map offers the potentiality of downloading or customising country reports, information concerning the estimation of the indices and their values as spreadsheets, while covering a period from 2015 and onwards. DGmap also allows users to examine the evolution of each indicator through time for each country individually. Thus, the presented tool is a dynamic and constantly updated application that can serve as a major source of information for those interested in the use of digital technologies by children, adolescents, and young people in Europe
Large Language Models and Multimodal Retrieval for Visual Word Sense Disambiguation
Visual Word Sense Disambiguation (VWSD) is a novel challenging task with the
goal of retrieving an image among a set of candidates, which better represents
the meaning of an ambiguous word within a given context. In this paper, we make
a substantial step towards unveiling this interesting task by applying a
varying set of approaches. Since VWSD is primarily a text-image retrieval task,
we explore the latest transformer-based methods for multimodal retrieval.
Additionally, we utilize Large Language Models (LLMs) as knowledge bases to
enhance the given phrases and resolve ambiguity related to the target word. We
also study VWSD as a unimodal problem by converting to text-to-text and
image-to-image retrieval, as well as question-answering (QA), to fully explore
the capabilities of relevant models. To tap into the implicit knowledge of
LLMs, we experiment with Chain-of-Thought (CoT) prompting to guide explainable
answer generation. On top of all, we train a learn to rank (LTR) model in order
to combine our different modules, achieving competitive ranking results.
Extensive experiments on VWSD demonstrate valuable insights to effectively
drive future directions.Comment: Conference on Empirical Methods in Natural Language Processing
(EMNLP) 202
Deep Learning Monitoring of Woody Vegetation Density in a South African Savannah Region
Bush encroachment in African savannahs has been identified as a land degradation process, mainly due to the detrimental effect it has on small pastoralist communities. Mapping and monitoring the extent covered by the woody component in savannahs has therefore become the focus of recent remote sensing-based studies. This is mainly due to the large spatial scale that the process of woody vegetation encroachment is related with and the fact that appropriate remote sensing data are now available free of charge. However, due to the nature of savannahs and the mixture of land cover types that commonly make up the signal of a single pixel, simply mapping the presence/absence of woody vegetation is somewhat limiting: it is more important to know whether an area is undergoing an increase in woody cover, ever if it is not the dominant cover type. More recent efforts have, therefore, focused in mapping the fraction of woody vegetation, which, clearly, is much more challenging. This paper proposes a methodological framework for mapping savannah woody vegetation and monitoring its evolution though time, based on very high-resolution data and multi-temporal medium-scale satellite imagery. We tested our approach in a South African savannah region, the Northwest Province (>100,000 km2), 0.5m-pixel aerial photographs for sampling and validation and Landsat data
Towards explainable evaluation of language models on the semantic similarity of visual concepts
Recent breakthroughs in NLP research, such as the advent of Transformer
models have indisputably contributed to major advancements in several tasks.
However, few works research robustness and explainability issues of their
evaluation strategies. In this work, we examine the behavior of high-performing
pre-trained language models, focusing on the task of semantic similarity for
visual vocabularies. First, we address the need for explainable evaluation
metrics, necessary for understanding the conceptual quality of retrieved
instances. Our proposed metrics provide valuable insights in local and global
level, showcasing the inabilities of widely used approaches. Secondly,
adversarial interventions on salient query semantics expose vulnerabilities of
opaque metrics and highlight patterns in learned linguistic representations
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