19,752 research outputs found
Alleviating Sparsity of Open Knowledge Graphs with Ternary Contrastive Learning
Sparsity of formal knowledge and roughness of non-ontological construction
make sparsity problem particularly prominent in Open Knowledge Graphs
(OpenKGs). Due to sparse links, learning effective representation for few-shot
entities becomes difficult. We hypothesize that by introducing negative
samples, a contrastive learning (CL) formulation could be beneficial in such
scenarios. However, existing CL methods model KG triplets as binary objects of
entities ignoring the relation-guided ternary propagation patterns and they are
too generic, i.e., they ignore zero-shot, few-shot and synonymity problems that
appear in OpenKGs. To address this, we propose TernaryCL, a CL framework based
on ternary propagation patterns among head, relation and tail. TernaryCL
designs Contrastive Entity and Contrastive Relation to mine ternary
discriminative features with both negative entities and relations, introduces
Contrastive Self to help zero- and few-shot entities learn discriminative
features, Contrastive Synonym to model synonymous entities, and Contrastive
Fusion to aggregate graph features from multiple paths. Extensive experiments
on benchmarks demonstrate the superiority of TernaryCL over state-of-the-art
models
Living Knowledge
Diversity, especially manifested in language and knowledge, is a function of local goals, needs, competences, beliefs, culture, opinions and personal experience. The Living Knowledge project considers diversity as an asset rather than a problem. With the project, foundational ideas emerged from the synergic contribution of different disciplines, methodologies (with which many partners were previously unfamiliar) and technologies flowed in concrete diversity-aware applications such as the Future Predictor and the Media Content Analyser providing users with better structured information while coping with Web scale complexities. The key notions of diversity, fact, opinion and bias have been defined in relation to three methodologies: Media Content Analysis (MCA) which operates from a social sciences perspective; Multimodal Genre Analysis (MGA) which operates from a semiotic perspective and Facet Analysis (FA) which operates from a knowledge representation and organization perspective. A conceptual architecture that pulls all of them together has become the core of the tools for automatic extraction and the way they interact. In particular, the conceptual architecture has been implemented with the Media Content Analyser application. The scientific and technological results obtained are described in the following
Artificial intelligence (AI) in rare diseases: is the future brighter?
The amount of data collected and managed in (bio)medicine is ever-increasing. Thus, there is a need to rapidly and efficiently collect, analyze, and characterize all this information. Artificial intelligence (AI), with an emphasis on deep learning, holds great promise in this area and is already being successfully applied to basic research, diagnosis, drug discovery, and clinical trials. Rare diseases (RDs), which are severely underrepresented in basic and clinical research, can particularly benefit from AI technologies. Of the more than 7000 RDs described worldwide, only 5% have a treatment. The ability of AI technologies to integrate and analyze data from different sources (e.g., multi-omics, patient registries, and so on) can be used to overcome RDs' challenges (e.g., low diagnostic rates, reduced number of patients, geographical dispersion, and so on). Ultimately, RDs' AI-mediated knowledge could significantly boost therapy development. Presently, there are AI approaches being used in RDs and this review aims to collect and summarize these advances. A section dedicated to congenital disorders of glycosylation (CDG), a particular group of orphan RDs that can serve as a potential study model for other common diseases and RDs, has also been included.info:eu-repo/semantics/publishedVersio
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