Hierarchical Losses and New Resources for Fine-grained Entity Typing and Linking

Extraction from raw text to a knowledge base of entities and fine-grained types is often cast as prediction into a flat set of entity and type labels, neglecting the rich hierarchies over types and entities contained in curated ontologies. Previous attempts to incorporate hierarchical structure have yielded little benefit and are restricted to shallow ontologies. This paper presents new methods using real and complex bilinear mappings for integrating hierarchical information, yielding substantial improvement over flat predictions in entity linking and fine-grained entity typing, and achieving new state-of-the-art results for end-to-end models on the benchmark FIGER dataset. We also present two new human-annotated datasets containing wide and deep hierarchies which we will release to the community to encourage further research in this direction: MedMentions, a collection of PubMed abstracts in which 246k mentions have been mapped to the massive UMLS ontology; and TypeNet, which aligns Freebase types with the WordNet hierarchy to obtain nearly 2k entity types. In experiments on all three datasets we show substantial gains from hierarchy-aware training.Comment: ACL 201

Extracting and Representing Entities, Types, and Relations

Making complex decisions in areas like science, government policy, finance, and clinical treatments all require integrating and reasoning over disparate data sources. While some decisions can be made from a single source of information, others require considering multiple pieces of evidence and how they relate to one another. Knowledge graphs (KGs) provide a natural approach for addressing this type of problem: they can serve as long-term stores of abstracted knowledge organized around concepts and their relationships, and can be populated from heterogeneous sources including databases and text. KGs can facilitate higher level reasoning, influence the interpretation of new data, and serve as a scaffolding for knowledge that enhances the acquisition of new information. A symbolic graph over a fixed, human-defined schema encoding facts about entities and their relations is the predominant method for representing knowledge, but this approach is brittle, lacks specificity, and is inevitably highly incomplete. On the other extreme, recent work on purely text-based knowledge models lack abstractions necessary for complex reasoning. In this thesis I will present work incorporating neural models, rich structured ontologies, and unstructured raw text for representing knowledge. I will first discuss my work enhancing universal schema, a method for learning a latent schema over both existing structured resources and unstructured free text, embedding them jointly within a shared semantic space. Next, I inject additional hierarchical structure into the embedding space of concepts, resulting in more efficient statistical sharing among related concepts and improved accuracy in both fine-grained entity typing and linking. I then present initial work representing knowledge in context, including a single model for extracting all entities and long-range relations simultaneously over full paragraphs while jointly linking these entities to a KG. I will conclude by discussing possible future directions for representing knowledge in context

Evidence for a lack of a direct transcriptional suppression of the iron regulatory peptide hepcidin by hypoxia-inducible factors.

BACKGROUND: Hepcidin is a major regulator of iron metabolism and plays a key role in anemia of chronic disease, reducing intestinal iron uptake and release from body iron stores. Hypoxia and chemical stabilizers of the hypoxia-inducible transcription factor (HIF) have been shown to suppress hepcidin expression. We therefore investigated the role of HIF in hepcidin regulation. METHODOLOGY/PRINCIPAL FINDINGS: Hepcidin mRNA was down-regulated in hepatoma cells by chemical HIF stabilizers and iron chelators, respectively. In contrast, the response to hypoxia was variable. The decrease in hepcidin mRNA was not reversed by HIF-1alpha or HIF-2alpha knock-down or by depletion of the HIF and iron regulatory protein (IRP) target transferrin receptor 1 (TfR1). However, the response of hepcidin to hypoxia and chemical HIF inducers paralleled the regulation of transferrin receptor 2 (TfR2), one of the genes critical to hepcidin expression. Hepcidin expression was also markedly and rapidly decreased by serum deprivation, independent of transferrin-bound iron, and by the phosphatidylinositol 3 (PI3) kinase inhibitor LY294002, indicating that growth factors are required for hepcidin expression in vitro. Hepcidin promoter constructs mirrored the response of mRNA levels to interleukin-6 and bone morphogenetic proteins, but not consistently to hypoxia or HIF stabilizers, and deletion of the putative HIF binding motifs did not alter the response to different hypoxic stimuli. In mice exposed to carbon monoxide, hypoxia or the chemical HIF inducer N-oxalylglycine, liver hepcidin 1 mRNA was elevated rather than decreased. CONCLUSIONS/SIGNIFICANCE: Taken together, these data indicate that hepcidin is neither a direct target of HIF, nor indirectly regulated by HIF through induction of TfR1 expression. Hepcidin mRNA expression in vitro is highly sensitive to the presence of serum factors and PI3 kinase inhibition and parallels TfR2 expression

SCCO: Thermodiffusion for the Oil and Gas Industry

International audienc

From Romantic Gothic to Victorian Medievalism: 1817 and 1877

"The Cambridge History of the Gothic was conceived in 2015, when Linda Bree, then Editorial Director at Cambridge University Press, first suggested the idea to us