Signals from outer space
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- NASA has a large database of documents and lessons learned from past programs and projects dating back to the 1950s. - Graph databases can be used to connect related information across different topics, enabling more efficient search and pattern recognition compared to isolated data silos. - Natural language processing techniques like named entity recognition, parsing, and keyword extraction can be applied to NASA's text data and combined with a graph database to create a knowledge graph for exploring relationships in the data.
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Signals from outer space
- 1. GraphAware® SIGNALS FROM OUTER SPACE Vlasta Kůs, Data Scientist @ GraphAware graphaware.com @graph_aware, @VlastaKus How NASA Benefits from Graph-Powered NLP
- 2. ‣ Database of learned knowledge across NASA’s programs & projects ‣ Unstructured text with basic metadata ‣ Collected since late 1950s (100s of millions of documents) ‣ Public dataset: ~1600 documents NASA’s Lessons Learned GraphAware®
- 3. "1406",420,"Roberts, J “, "VO'75 Pressure Regulator Leakage and Work-Around Procedures (~1976)”, "The pressure regulator in the Viking Orbiter Propulsion Subsystem started leaking following a pyro firing that occurred prior to the near-Mars TCM. Likely causes were corrosion or residue from propellant migration or pyro valve blowby, or particulate contamination. Recommendations included using separate regulators for the fuel and oxidizer sides, incorporating a bellows in the pyro valve to eliminate blowby, and adding a isolation valve between the regulator and propellant tank.“, " The micro-scale effects of long-term propellant exposure should be investigated in order to better critique regulator design. “, "JPL",1996-07-08,"",TRUE,"",1460,7,NA,"https:// nen.nasa.gov/web/11/viewall/-/viewall/420" NASA’s Lessons Learned Database GraphAware®
- 4. “673",1326,"Relvini, Kristine “, "Lessons Learned Not Being Inputted Into Lessons Learned Information System (LLIS) Database”, “", "If you don't document the lessons learned, you loose knowledgeable, shared information and tracking capacity across programs.“, "KSC",2002-10-11,"Aeronautics Research, Science, Exploration Systems, Space Operations, ",FALSE,"", 702,6,NA,"https://nen.nasa.gov/web/11/viewall/-/ viewall/1326" NASA’s Lessons Learned GraphAware®
- 5. Graph database = isolated data silos -> connected knowledge ‣ Efficient search ‣ Relationships among various areas Apollo, Space Shuttle, Orion, … ‣ Pattern recognition (clusters, communities, correlations, …) Example: correlation between corrosion of valves & topics involving batteries ‣ Useful for planning future projects and preventing/solving issues NASA’s Lessons Learned GraphAware®
- 6. What is a Graph? GraphAware® G = (V, E)
- 7. WHY NEO4J? GraphAware® It is a proper graph database It is a proper database
- 8. Graph-Based Architecture: Knowledge Graph GraphAware®
- 10. ‣ NLP = machine learning tools allowing computers to process - and perhaps understand - human languages ‣ Basic steps Sentence segmentation Tokenisation Lemmatisation Part of Speech (POS) tagging Parsing Named Entities Recognition (NER) Sentiment analysis … Natural Language Processing GraphAware®
- 11. Currently supported toolkits for human language processing ‣ Stanford CoreNLP ‣ developed at Stanford University ‣ fast, robust, production ready ‣ many pre-built models ‣ license: GPL v3+ ‣ Apache OpenNLP ‣ developed by volunteers ‣ many pre-built models ‣ license: Apache License v2.0 NLP: Text Processors GraphAware®
- 12. ‣ Named Entity Recognition (NER) = classification of words into predefined classes ‣ Examples: Dr. Who -> Person, May 2018 -> Date, EU -> Country … ‣ Stanford NLP default entities: Person, Location, Date, Organisation, Number, Money, Percentage ‣ Custom NE classes -> training on large tokenised & labeled corpus ‣ Wikipedia, Wikidata - rich sources of multilingual training data that can be extracted automatically Named Entity Recognition GraphAware®
- 13. Custom Named Entities based on Wikipedia GraphAware® NASA use case: identify names of space missions Training - crawling Wikipedia & identifying relevant information
- 15. Universal Dependencies: cross-linguistically consistent grammatical relations among words in a sentence Examples: ‣ amod (adjectival modifier) Matt likes red wine. ‣ appos (appositional modifier) Mars Global Surveyor (MGS) was an American robotic spacecraft … ‣ conj (conjunct) It failed to respond to messages and commands. ‣ … Universal Dependencies GraphAware®
- 16. ‣ Stanford CoreNLP: Dependency & Part of Speech analysis of a single sentence Source: http://nlp.stanford.edu:8080/corenlp/process Either find an efficient representation in some traditional database, or … Graph-Powered NLP GraphAware®
- 17. Graph-Powered NLP GraphAware® NLP and property graphs: natural fit … use a property graph!
- 19. Unsupervised techniques tend to be underestimated, while … ‣ No need for time & money to get massive labeled training datasets ‣ Often faster to train & faster to predict ‣ Unsupervised deep learning Unsupervised ML Algorithms GraphAware®
- 20. PageRank GraphAware® PageRank = a measure of importance of a web page based on the quality of links from other pages The formula reflects a model of a random surfer. Source: https://en.wikipedia.org/wiki/PageRank
- 21. Keyword Extraction: TextRank GraphAware® Keywords = words/phrases that capture the semantic essence of a text Graph-Based Unsupervised Algorithm: ‣ Construct a graph of word co-occurrences ‣ Asses the importance of words by PageRank algorithm ‣ Use top 1/3 of words as keyword candidates ‣ Use universal dependencies to construct key phrases
- 22. GraphAware® Rada Mihalcea, Paul Tarau. TextRank: Bringing Order into Texts. Proceedings of EMNLP 2004, pages 404–411, Barcelona, Spain. Association for Computational Linguistics. http://www.aclweb.org/anthology/W04-3252. Keyword Extraction: TextRank Despite its simplicity, TextRank provides state of the art results on wide range of unstructured texts. Leveraging universal dependencies allowed us to surpass precision & recall of the original TextRank paper. NASA examples: “space shuttle”, “flight hardware”, “launch vehicle”, …
- 23. Automatic text summarisation ‣ Abstractive ‣ Extractive TextRank can be adapted for efficient sentence ranking for extractive summarisation. Summarisation: TextRank GraphAware®
- 25. ConceptNet 5 = semantic network for understanding the meaning of words ‣ Relational knowledge from MIT’s Open Mind Common Sense project ‣ DBPedia (information from Wikipedia info-boxes) ‣ Wiktionary (free multilingual dictionary) ‣ … Knowledge Enrichment: ConceptNet 5 GraphAware® Microsoft Concept Graph = semantic network introducing knowledge about concepts ‣ harnessed from billions of web pages and years’ worth of search logs
- 26. Expand the knowledge from external or other internal sources. Knowlege Enrichment GraphAware®
- 27. ‣ Latent Dirichlet Allocation (LDA) - generative statistical model that describes documents as a probabilistic mixture of a small number of topics ‣ Each topic described by a list of most relevant words ‣ Sample of topics from the NASA dataset [“design”, "failure", "test", "result", "flight", "hardware", "mission", “testing”, “system”, “due”] [“pressure", "system", "cause", "valve", "propellant", "leak", "operation", “shuttle”, “space”, “gas”] [“space”, "shuttle", "NASA", "operation", "safety", "iss", "crew", "ISS", "astronaut", "progr am"] Topic Extraction: Latent Dirichlet Allocation GraphAware®
- 29. ‣ Word embeddings = representation of words as multi-dimensional semantic vectors which encode linguistic patterns ‣ Use cases: word sense disambiguation, new distance functions between documents, starting point for further ML (e.g. NN classification) ‣ Word2vec = shallow two-layer neural network model for producing word embeddings ‣ ConceptNet Numberbatch - consists of state-of-the-art word embeddings Word Embeddings GraphAware®
- 30. Word Embeddings: word2vec GraphAware® Tomas Mikolov et al.: https://arxiv.org/abs/1301.3781
- 31. Word Embeddings: word2vec GraphAware® Kusner et al.: http://mkusner.github.io/publications/WMD.pdf Document distance: min. cumulative distance that all words need to travel Semantic patterns representable as linear translations: distance(Oslo -> Norway) similar to distance(Berlin -> Germany) vec(Germany) - vec(Berlin) + vec(Oslo) = vec(Norway)
- 32. Document Embeddings GraphAware® Q. Le, T. Mikolov: Distributed representations of sentences and documents, arXiv:1405.4053v2 Paragraph Vector (doc2vec): extension of word2vec The additional paragraph node represents context (topic) of the current document. Paragraph vectors have the same behaviour towards linear vector translations as word vectors.
- 33. Document Embeddings GraphAware® doc2vec vectors of dimension 300, NASA sentences -> dimensionality reduction (PCA + t-SNE)
- 34. Document Embeddings GraphAware® doc2vec vectors of dimension 2000, 30k Wikipedia pages -> dimensionality reduction (PCA + t-SNE)
- 35. Some of the neural networks applicable to text processing ‣ Shallow networks (word & document embeddings) ‣ Deep Auto-Encoders ‣ Convolutional Neural Networks ‣ Recurrent Neural Networks (LSTMs) Deep Learning for Text Processing GraphAware®
- 36. Self-supervised Auto-Encoders: useful for vector embeddings (images, texts) DeepLearning4J - Java-based deep learning library Example of auto-encoder (e.g. stacked RBMs) … Deep Learning: Auto-encoders GraphAware® Works well for images, but problematic for texts (sparsity).
- 37. Convolutional Neural Networks GraphAware® Y. Zhang, B. Wallace: arXiv:1510.03820 Classification of documents based on word embeddings and CNN
- 38. Deep Learning: Summarisation GraphAware® S. Narayan et al.: Ranking Sentences for Extractive Summarisation with Reinforcement learning, arXiv:1802.08636
- 39. Deep Learning: Summarisation GraphAware® Extractive summarisation (sentence ranking) notably outperforms abstractive. S. Narayan et al.: Ranking Sentences for Extractive Summarisation with Reinforcement learning, arXiv:1802.08636
- 40. Knowledge Graphs are a powerful problem-solving tool ‣ Augmented search ‣ Actionable knowledge ‣ Machine Learning ‣ Chatbots and Question answering systems ‣ Foundational to AI Conclusion GraphAware®