Text Intelligence For ScienceMads Rydahlmads@unsilo.aiChief Visionary Officer, UNSILO

UNSILO

Artificial Intelligence Startupwith a small agile teamfocused on Scientific Publishing

Vision

Making it easy and fast to find relevant knowledge and discover new patterns

Automated. Because scientific language is constantly growing, evolving, and accelerating. Omniscient. Because important findings may not be apparent. Even to the author.Unbiased. Because existing solutions rank by popularity and cause filter bubbles.

The Problem

A request to find content on "inflammation":

"pathogens" OR "damaged cells" OR "irritants" OR "necrotic cells" OR "inflammatory" OR "inflammation" OR "hay fever" OR "periodontitis," OR "atherosclerosis" OR "rheumatoid arthritis" OR "gallbladder carcinoma" OR "leukocytes" OR "granulocytes" OR "urethritis" OR "type III hypersensitivity" OR "ischaemia" OR "parasitosis" OR "eosinophilia" OR "Appendicitis" OR "Bursitis" OR "Colitis" OR "Cystitis" OR "Dermatitis" OR "Phlebitis" OR "Rhinitis" OR "Tendonitis" OR "Tonsillitis" OR "Vasculitis"-

Searching is messy!

Key Challenges

Our Knowledge does Not Compute▪ The world moves too fast for data curators and ontology writers▪ Most Scientific Disciplines have no ontologies (or even controlled vocabularies)

▪ Dictionaries and Reference Works are too small and often out-of-date▪ New discoveries have no official names

People are too creative▪ There is a lot of variation in language▪ Researchers often add descriptive detail that obscure facts▪ There is no “right way” to describe most things

Some things seem obvious …but mostly to the author▪ The right Level-of-Detail depends both on the context and the reader▪ The most obvious facts are often omitted because they are implicitly included▪ Editors think in themes and topics, researchers in methods, properties, and facts

Core Technology

Finds key phrases in any textand uses Machine Learning to identify novel ideas

Open Languages, libraries, and frameworksApache UIMA, Apache Ruta, Stanford NLP tools, DKPRo, Hadoop, Spark, TensorFlow, Mahout, Vowpal Wabbit, GenSim, LevelDb, Elasticsearch, Docker, Cloudsigma, AWS

Full Text SearchPseudohyponatremia: Does It Matter in Current Clinical Practice?http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3894530/doi: 10.5049/EBP.2006.4.2.77

Serum consists of water (93% of serum volume) and nonaqueous components, mainly lipids and proteins (7% of serum volume). Sodium is restricted to serum water. In states of hyperproteinemia or hyperlipidemia, there is an increased mass of the nonaqueous components of serum and a concomitant decrease in the proportion of serum composed of water. Thus, pseudohyponatremia results because the flame photometry method measures sodium concentration in whole plasma. A sodium-selective electrode gives the true, physiologically pertinent sodium concentration because it measures sodium activity in serum water. Whereas the serum sample is diluted in indirect potentiometry, the sample is not diluted in direct potentiometry. Because only direct reading gives an accurate concentration, we suspect that indirect potentiometry which many hospital laboratories are now using may mislead us to confusion in interpreting the serum sodium data. However, it seems that indirect potentiometry very rarely gives us discernibly low serum sodium levels in cases with hyperproteinemia and hyperlipidemia. As long as small margins of errors are kept in mind of clinicians when serum sodium is measured from the patients with hyperproteinemia or hyperlipidemia, the present methods for measuring sodium concentration in serum by indirect sodium-selective electrode potentiometry could be maintained in the clinical practice.

Using Dictionaries and OntologiesPseudohyponatremia: Does It Matter in Current Clinical Practice?http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3894530/doi: 10.5049/EBP.2006.4.2.77

Key: Chemical Technique Anatomy Disease Species

Serum consists of water (93% of serum volume) and nonaqueous components, mainly lipids and proteins (7% of serum volume). Sodium is restricted to serum water. In states of hyperproteinemia or hyperlipidemia, there is an increased mass of the nonaqueous components of serum and a concomitant decrease in the proportion of serum composed of water. Thus, pseudohyponatremia results because the flame photometry method measures sodium concentration in whole plasma. A sodium-selective electrode gives the true, physiologically pertinent sodium concentration because it measures sodium activity in serum water. Whereas the serum sample is diluted in indirect potentiometry, the sample is not diluted in direct potentiometry. Because only direct reading gives an accurate concentration, we suspect that indirect potentiometry which many hospital laboratories are now using may mislead us to confusion in interpreting the serum sodium data. However, it seems that indirect potentiometry very rarely gives us discernibly low serum sodium levels in cases with hyperproteinemia and hyperlipidemia. As long as small margins of errors are kept in mind of clinicians when serum sodium is measured from the patients with hyperproteinemia or hyperlipidemia, the present methods for measuring sodium concentration in serum by indirect sodium-selective electrode potentiometry could be maintained in the clinical practice.

UNSILO Concept ExtractionPseudohyponatremia: Does It Matter in Current Clinical Practice?http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3894530/doi: 10.5049/EBP.2006.4.2.77

Key: Chemical Technique Anatomy Disease Species

Serum consists of water (93% of serum volume) and nonaqueous components, mainly lipids and proteins (7% of serum volume). Sodium is restricted to serum water. In states of hyperproteinemia or hyperlipidemia, there is an increased mass of the nonaqueous components of serum and a concomitant decrease in the proportion of serum composed of water. Thus, pseudohyponatremia results because the flame photometry method measures sodium concentration in whole plasma. A sodium-selective electrode gives the true, physiologically pertinent sodium concentration because it measures sodium activity in serum water. Whereas the serum sample is diluted in indirect potentiometry, the sample is not diluted in direct potentiometry. Because only direct reading gives an accurate concentration, we suspect that indirect potentiometry which many hospital laboratories are now using may mislead us to confusion in interpreting the serum sodium data. However, it seems that indirect potentiometry very rarely gives us discernibly low serum sodium levels in cases with hyperproteinemia and hyperlipidemia. As long as small margins of errors are kept in mind of clinicians when serum sodium is measured from the patients with hyperproteinemia or hyperlipidemia, the present methods for measuring sodium concentration in serum by indirect sodium-selective electrode potentiometry could be maintained in the clinical practice.

UNSILO Semantic MappingPseudohyponatremia: Does It Matter in Current Clinical Practice?http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3894530/doi: 10.5049/EBP.2006.4.2.77

Key: Action/Relation Chemical Technique Anatomy Disease Species

Serum consists of water (93% of serum volume) and nonaqueous components, mainly lipids and proteins (7% of serum volume). Sodium is restricted to serum water. In states of hyperproteinemia or hyperlipidemia, there is an increased mass of the nonaqueous components of serum and a concomitant decrease in the proportion of serum composed of water. Thus, pseudohyponatremia results because the flame photometry method measures sodium concentration in whole plasma. A sodium-selective electrode gives the true, physiologically pertinent sodium concentration because it measures sodium activity in serum water. Whereas the serum sample is diluted in indirect potentiometry, the sample is not diluted in direct potentiometry. Because only direct reading gives an accurate concentration, we suspect that indirect potentiometry which many hospital laboratories are now using may mislead us to confusion in interpreting the serum sodium data. However, it seems that indirect potentiometry very rarely gives us discernibly low serum sodium levels in cases with hyperproteinemia and hyperlipidemia. As long as small margins of errors are kept in mind of clinicians when serum sodium is measured from the patients with hyperproteinemia or hyperlipidemia, the present methods for measuring sodium concentration in serum by indirect sodium-selective electrode potentiometry could be maintained in the clinical practice.

■ Natural Language Processing Sentences are annotated with part-of-speech tags; noun, verb, adjective, and a dependency tree

methods for measuring sodium concentration in serum by indirect sodium-selective electrode potentiometry  [··thing··] [··action··] [···········thing··········] [·thing·] [····························· thing ······························]

■ Extract all “things”MethodSodium concentrationSerumIndirect Sodium-Selective Electrode Potentiometry

Phrase Extraction

■ Reduce Morphological and Syntactic variation (Grammar, form)■ Normalize adjectival modifiers, compound paraphrases, and expand coordinations

Concentration of Sodium >> Sodium ConcentrationThe Electrode Potentiometry was indirect >> Indirect Electrode PotentiometryMethodology >> Method

■ Reduce Lexical and Semantic variation (Synonyms, hypernyms, ontologies)■ Normalize semantic Level-of-Detail using ontologies and vector models

Serum Sample >> Blood SampleSodium Concentration >> Natrium ConcentrationIndirect Electrode Potentiometry >> Electroanalysis

■ Remove rare super-grams and hyponyms (C-level filtering, distribution metrics)■ E.g. “Clinically validated indirect sodium-selective potentiometry”

■ Snap to common fragments and forms (actual usage and Ontologies)■ Indirect Sodium Selective Potentiometry is-a-kind-of Indirect Potentiometry is-a-kind-of Electroanalysis

Boundary detection and Normalization

● We build high-dimensional vector-space representations of all concepts from the textual context

Word Embeddings and Word2Vec

Vasodilatation (finding)Peripheral vasodilation (finding)

Vasodilator (substance)Poisoning by vasodilator (disorder)

Vasodilating agent (product)Intra-cavernosal vasodilator (product)

Intra-arterial vasodilator (product)Coronary vasodilator (product)

Alpha blocking vasodilator (product)Nitrate-based vasodilating agent (product)

Human B-type natriuretic peptide (product)Endothelin receptor antagonist (product)

Pentaerythritol tetranitrate (product)Nitroglycerin (product)

Isosorbide mononitrate (product)Isosorbide dinitrate (product)

Measurement of blood pressure (procedure)Self-measurement devices (product)Systolic arterial pressure (observable entity)Non-invasive arterial pressure (observable entity)Blood pressure finding (finding)Blood pressure cuff, device (physical object)Blood pressure cuff inflator (physical object)Lying blood pressure (observable entity)Abnormal blood pressure (finding)Lower tourniquet cuff inflation (procedure)Cuff inflated (attribute)

principle.n.01generalizationbasic truthassumptionlaw

receptor.n01Plasma membrane moleculeG protein-coupled receptorligand-gated ion channelP2X receptorP2Y receptor

● We build high-dimensional vector-space representations of all concepts from the textual context● We apply ontologies and dictionaries to improve occurrence counts of on rare, complex, or novel concepts● We use these normalized concepts to improve recall and precision for rare, complex, or novel concepts● We use this high-dimensional vector model to build real-time semantic indexes with unprecedented precision

Ontology Augmented Vector-space

Synsets built from Vector Cosine Similarity

Human-readable Fingerprints

We have built a Corpus-based Recommender that use our novel and flexible approach to document fingerprinting and similarity

▪ Traditional Document Similarity ▪ Document vectors based on TF-IDF and Naïve BOW▪ Slow moving ontologies (snomed, doid, dron)▪ Simple concepts (“insulin” and “obesity”)▪ Limited recognition (only lemmatization/stemming)

▪ UNSILO▪ Dynamic corpus-driven concept similarity▪ Captures novel significant phrases (“insulin insensitivity”)▪ Links concepts across terminology variations (“reduced hormone response”)

Products for Science

Springer.com“Using UNSILO’s fully automated content enrichment technology, we can identify the most descriptive concepts and phrases within any document in our content portfolio, and provide more valuable reading suggestions, even across domains with a highly variable terminology.”

Jan-Erik de BoerChief Information OfficerSpringer Nature

“Our goal with this new feature is to make it easy for our users to drill down on what they find important in an article, and use that insight as a departure point for their discovery process.”

Stephen CorneliusProduct OwnerIT Platform DevelopmentSpringer Nature

UNSILO technology vendor for Springer Nature9M scientific articles and book chapters22M monthly users Significant increase in traffic and user engagementDisplaced leading competitor

■ Normalize Actions and RelationshipsSample linguistic variations of common relationships from re-statements of known facts, Then apply what we learn to less well understood domains:

■ Serum consists of water■ Serum amounts to 93% water■ Serum contains water■ Serum is composed of water■ Serum is mostly water

■ Providing hooks into Unstructured TextImprove training and prediction capabilities of larger AI initiatives by improving access to consumer feedback, corporate data lakes, or conversations within large communities of practice.

■ Reasoning at ScaleQuestion answering, uncover hidden causal chains, invalidate futile research projects

■ Augment Researcher’s cognitive abilities■ Accelerate the pace of Research■ Improve the return on R&D investments■ Helping 10M Researchers across the globe “Finding the Cure for Cancer”

Future Directions

■ Thin film Coated Gold Nano Particles■ Coating of Iron nano-particles with thin Gold film■ Fe Nanoparticles thin-film Gold coat■ Evaporation-coating of nanoparticles with gold■ Gold-coated magnetic nanoparticles

Yes! We are hiring!

jobs@unsilo.com