The Impact of Banking Sector Stability on the Real Economy

This article studies the relationship between the degree of banking sector stability and the subsequent evolution of real output growth and inflation. Adopting a panel VAR methodology for a sample of 18 OECD countries, we find a positive link between banking sector stability and real output growth. This finding is predominantly driven by periods of instability rather than by very stable periods. In addition, we show that an unstable banking sector increases uncertainty about future output growth. No clear link between banking sector stability and inflation seems to exist. We then argue that the link between banking stability and real output growth can be used to improve output growth forecasts. Using Fed forecast errors, we show that banking sector stability (instability) results in a significant underestimation (overestimation) of GDP growth in the subsequent quarters.Banking sector stability, real output growth, output growth forecasts

Relevant Entity Selection: Knowledge Graph Bootstrapping via Zero-Shot Analogical Pruning

Knowledge Graph Construction (KGC) can be seen as an iterative process starting from a high quality nucleus that is refined by knowledge extraction approaches in a virtuous loop. Such a nucleus can be obtained from knowledge existing in an open KG like Wikidata. However, due to the size of such generic KGs, integrating them as a whole may entail irrelevant content and scalability issues. We propose an analogy-based approach that starts from seed entities of interest in a generic KG, and keeps or prunes their neighboring entities. We evaluate our approach on Wikidata through two manually labeled datasets that contain either domain-homogeneous or -heterogeneous seed entities. We empirically show that our analogy-based approach outperforms LSTM, Random Forest, SVM, and MLP, with a drastically lower number of parameters. We also evaluate its generalization potential in a transfer learning setting. These results advocate for the further integration of analogy-based inference in tasks related to the KG lifecycle

Discovering and Comparing Relational Knowledge, the Example of Pharmacogenomics

Article in Proceedings of the EKAW Doctoral Consortium 2018 co-located with the 21st International Conference on Knowledge Engineering and Knowledge Management (EKAW 2018)Pharmacogenomics (PGx) studies the influence of the genome in drug response, with knowledge units of the form of ternary relationships genomic variation-drug-phenotype. State-of-the-art PGx knowledge is available in the biomedical literature as well as in specialized knowledge bases. Additionally, Electronic Health Records of hospitals can be mined to discover such knowledge units that can then be compared with the state of the art, in order to confirm or temper relationships lacking validation or clinical counterpart. However, both discovering and comparing PGx relationships face multiple challenges: heterogeneous descriptions of knowledge units (languages, vocabularies and granularities), missing values and importance of the time dimension. In this research, we aim at proposing a framework based on Semantic Web technologies and Formal Concept Analysis to discover, represent and compare PGx knowledge units. We present the first results, consisting of creating an integrated knowledge base of PGx knowledge units from various sources and defining comparison methods, as well as the remaining issues to tackle

Treat Different Negatives Differently: Enriching Loss Functions with Domain and Range Constraints for Link Prediction

Knowledge graph embedding models (KGEMs) are used for various tasks related to knowledge graphs (KGs), including link prediction. They are trained with loss functions that are computed considering a batch of scored triples and their corresponding labels. Traditional approaches consider the label of a triple to be either true or false. However, recent works suggest that all negative triples should not be valued equally. In line with this recent assumption, we posit that negative triples that are semantically valid w.r.t. domain and range constraints might be high-quality negative triples. As such, loss functions should treat them differently from semantically invalid negative ones. To this aim, we propose semantic-driven versions for the three main loss functions for link prediction. In an extensive and controlled experimental setting, we show that the proposed loss functions systematically provide satisfying results on three public benchmark KGs underpinned with different schemas, which demonstrates both the generality and superiority of our proposed approach. In fact, the proposed loss functions do (1) lead to better MRR and Hits@10 values, (2) drive KGEMs towards better semantic awareness as measured by the Sem@K metric. This highlights that semantic information globally improves KGEMs, and thus should be incorporated into loss functions. Domains and ranges of relations being largely available in schema-defined KGs, this makes our approach both beneficial and widely usable in practice

Knowledge-Based Matching of nn-ary Tuples

An increasing number of data and knowledge sources are accessible by human and software agents in the expanding Semantic Web. Sources may differ in granularity or completeness, and thus be complementary. Consequently, they should be reconciled in order to unlock the full potential of their conjoint knowledge. In particular, units should be matched within and across sources, and their level of relatedness should be classified into equivalent, more specific, or similar. This task is challenging since knowledge units can be heterogeneously represented in sources (e.g., in terms of vocabularies). In this paper, we focus on matching n-ary tuples in a knowledge base with a rule-based methodology. To alleviate heterogeneity issues, we rely on domain knowledge expressed by ontologies. We tested our method on the biomedical domain of pharmacogenomics by searching alignments among 50,435 n-ary tuples from four different real-world sources. Results highlight noteworthy agreements and particularities within and across sources

Matching Pharmacogenomic Knowledge: Particularities, Results, and Perspectives

International audienceKnowledge in pharmacogenomics (PGx) is scattered across several resources, e.g., reference databases and the biomedical literature. Matching their content would thus lead to a consolidated view of the available PGx knowledge that could, in turn, support multiple downstream applications, including knowledge curation and precision medicine. However, matching atomic units of PGx knowledge is challenging due to their peculiarities: they are of n-ary nature, represented with heterogeneous vocabularies, and with various levels of granularity. In this paper, we frame the matching of PGx knowledge units of various provenance as an instance matching problem. We summarize our work to represent such units within a knowledge graph named PGxLOD, and to match them with a rule-based and a graph embedding-based matching approaches. We then particularly discuss the remaining challenges and how our research artifacts opened to the community could foster new benchmarks and methods for structure-based instance matching

Climate‐related risks in financial assets

The financial risks and potential systemic impacts induced by climate change and the transition to a low-carbon economy have become a central issue for both financial investors and their regulators. In this article, we develop a critical review of the empirical and theoretical literature concerning the impact of climate-related risks on the price of financial assets. We first present the theoretical links between asset pricing and climate-related risks and develop a theory of how climate risk drivers transmit costs to firms and lead to asset price changes. We then discuss studies looking at past climate-related events, which show that both climate physical impacts and transition dynamics can trigger a revaluation of financial assets through multiple direct and indirect channels. Finally, we review the emerging literature that uses forward-looking methodologies to estimate future climate-related asset price changes, which suggests that climate financial risks can indeed have significant implications on financial stability

Étudier les mécanismes des effets indésirables des médicaments avec l'IA explicable : expériences avec la fouille de graphes de connaissances

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