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Presentation by Costas Bekas for IBM Cognitive Systems Institute Group Speaker Series Call

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- 1. IBM Research - Zurich Knowledge Graph Creation & Analytics for Cognitive Systems Costas Bekas Foundations of Cognitive Computing, IBM Research - Zurich
- 2. IBM Research - Zurich Data & Computation trends towards Cognitive Computing TOWARDS COGNITIVE COMPUTING COMP Cost O(N) O(N3) O(N2) Graph Analytics Knowledge Graph Creation Clustering Dim. Reduction Simple DB queries Information Retrieval Uncertainty Quantification HADOOPHPC DATA Volume PB TB GB MB
- 3. IBM Research - Zurich Data driven knowledge discovery pipeline 3 SQL NoSQL Informa(on Knowledge Intelligence ESB R E A S O N C O N N E C T A D A P T G A T H E R Data Context Decisions & Ac(ons Collect all relevant data from a variety of sources: Publications, RSS, APIs, DBs, etc Extract features and build context using multiple diverse data sources, new sources added at run-time: User defined Analyze data in context to uncover hidden information and find new relationships. Analytics both add to context via metadata extraction, and use context to broader information exploited Compose recommended interactions, use context to deliver to point of action. Gather Connect Reason Adapt NEW GRAPH ANALYTICS WORK
- 4. IBM Research - Zurich 4 Research directions and projects engagement Focus on Knowledge Graph creation and analytics Ø Representation of knowledge. Basic foundation of Cognitive Computing Strategic Projects: Support roadmap for competitive edge Ø Materials Analytics: Focus on materials knowledge graphs § MARVEL: SWISS NSF. Data driven material discovery § Direct client projects and Watson engagement Ø Acceleration and Cloud deployment of graph analytics § Nanostreams (EU FP7): Focus on accelerators § HPC Java (EU FP7), XDATA (DARPA): Focus on Java and algorithms
- 5. IBM Research - Zurich Novel graph analytics tools Ø Node importance: Sub-graph node centralities § Previous art: O(N3) cost. Graphs in the millions of nodes require Exascale. Not possible on the Cloud. § Our method: close to O(N). Runs on the Cloud and HPC. Cuts time down to minutes from several hours (or days) Ø Graph Comparisons: Spectrograms § Completely new method to compare graphs § Standard methods: Combinatorial heuristics, limited to very small graphs § Our method: close to O(N) cost, runs on the Cloud & HPC 5
- 6. IBM Research - Zurich 6 Knowledge Graphs: Hold data + relationships = Knowledge Best Friend Adam Distrust In love Cohabitation 1950 Alcohol Abuse Heart Disease Judy M 1988M 1977 Sophie 1982 1956-1996 1950 Joan M 1973 1952 Carol Richards Kimberly 1964 1989 M 2009 Donald Alice 1988 Abuse 1978 Mike 3 Heart Disease Brittany 2008 John Sibling Data examples: • Birthdays, death dates, family lineage, medical events Relationship examples: • Mike is in love with Sophie, they live together • Carol is the best friend of Sophie Advanced relationships examples: • Sophie’s mother died of heart disease. Sophie’s daughter suffers of heart disease Query Examples: • Why does Brittany has heart disease? Is there a chance that she is abused? Who should we call if Sophie goes missing?
- 7. IBM Research - Zurich 7 Deeper dive: Knowledge graphs for materials Citation graphs on documents: papers/patents • Nodes are documents • Two nodes are connected (there exists and edge between them) if there is citation pointer between the documents What kind of analytics can we do with our tools? • Find influential patents (obviously not simply the most cited ones) • Find groups of similar patents (wrt impact/ influence) • Find similar patent categories
- 8. IBM Research - Zurich 8 Knowledge graph creation example: focus on similarities An example from materials science Connections (edges) have weights to indicate degree of similarity • Multiple similarities can be expressed at the same time and can be chosen on the fly • There is a learning phase for knowledge graph creation. Similarities and connectivity needs to represent ground truth and empirical knowledge • This calibration phase requires the systematic comparison of graphs Material i • Composition • Crystal Structure • Chemical Stability • Ionic conductivity • … Material i • Composition • Crystal Structure • Chemical Stability • Ionic conductivity • … 1/similarity distance
- 9. IBM Research - Zurich 9 A Metallurgical Knowledge Graph Data model We have various node types • Alloy node type: according to standard numbering/classification - Node properties: composition, form, etc • Basic doping element type • Processing type - Node properties: basic forming steps and order • Document node type: - Node properties: extracted text based on industry provided ontologies
- 10. IBM Research - Zurich 1 0 The Knowledge Graph Data model Let us consider 2 alloys: Alloy_1, Alloy_2 • We conduct text extraction from a set of documents and get the document type nodes Consider the following cases: 1. A document node refers to both alloys: Then the alloys nodes are connected 2. Two documents refer to alloys separately but the documents are linked by citation: Then the alloy nodes are connected ALLOY_1 ALLOY_2DOC ALLOY_1 ALLOY_2 DOC_1 DOC_2
- 11. IBM Research - Zurich 1 1 The Knowledge Graph Data model Let us consider 2 alloys: Alloy_1, Alloy_2 • Can we extract similarities beyond extracted text? Chemical composition similarity estimator ALLOY_2: Chem. composition ALLOY_1: Chem. composition Threshold YES NO CONT. VALUE VALUE
- 12. IBM Research - Zurich 1 2 The Knowledge Graph Data model Let us consider 2 alloys: Alloy_1, Alloy_2 and a Process Proc_1 • We conduct text extraction from a set of documents and get the document type nodes. The document nodes link process Proc_1 to each alloy separately: ALLOY_1 ALLOY_2 PROC_1 Query: “Find all alloys for which Proc_1 is used and certain properties need to hold for the alloys” Action on graph: • Start from the node Proc_1 and visit its neighbors • Those nodes you find that are of the alloy type that fulfill the user defined criteria are your answers
- 13. IBM Research - Zurich 1 3 The Knowledge Graph Data model These processes create a complex Knowledge Graph that captures all the knowledge in the text, in the practical experience & from physics/chemistry principles. ALLOY NODE TYPE PROCESS NODE TYPE DOCUMENT NODE TYPE ELEMENT NODE TYPE KNOWLEDGE GRAPH
- 14. IBM Research - Zurich System Architecture WDA front end system Back end system COMPUTE GRAPH DB
- 15. IBM Research - Zurich Query work flow TRANSLATE QUERY TO SUBGRAPH SELECTION COMPUTE RANK (IMPORTANCE) OF NODES: NODE CENTRALITIES VISUALIZE AND EXPLORE
- 16. IBM Research - Zurich 16 Node importance: Sub-graph node centralities The subgraph centrality measures the participation of each node in all subgraphs in a network, where smaller subgraphs (with same start and end point) carry more weight than larger ones. • In a star graph the center participates in all (8) subgraphs of size 2 (a line) • Each other nodes participate in all (8) subgraphs of size 4, where the center participates in 64 subgraphs of size 4
- 17. IBM Research - Zurich 17 Computing graph centralities Counting the number of paths in a graph boils down to simple algebra with the adjacency matrix: C = I + A + A2/2! + A3/3! + A4/4! + … § The power k counts how many paths of length k there are between nodes i-j • We would like to penalize (weight) very long paths over shorter ones. Thus, we use the factorial scaling (Estrada index) • Thus the diagonal of matrix C is exactly what we need. • But: basic matrix algebra shows that C=expm(A), i.e. the matrix exponential • Problem: This is a O(N3) problem using standard techniques. Exascale needed for graphs of size 1M • We developed an O(N) method to compute graph centralities. (patent pending)
- 18. IBM Research - Zurich 18 How do we compute node centralities at O(N) cost Remember: we are looking for the diagonal of the matrix exponential function Key point: we do not need all of the matrix function. Just selected elements of it! Solution: ü Stochastic estimation: Use s<<n carefully designed vectors vi and estimate o where ⊗ is Hadamard (entry-wise) multiplication and ∅ is Hadamard division ü P(A,vi) is approximating the multiplication of matrix exponential with a vector (Lanczos) ü Since s<<n and P(A,vi) costs O(N) (Lanczos). Total cost: O(sn). Memory: O(N)
- 19. IBM Research - Zurich 19 Scalability of node importance calculator: Speedup Scaling to HPC resources 22 secs 3.6 secs Road network of Europe. 50M nodes, 150M edges.
- 20. IBM Research - Zurich GRAPH SPECTROGRAMS 20 Graph Similarities 1D VECTOR CORR.
- 21. IBM Research - Zurich 21 O(N) Method for Graph Spectrograms § Then P(A,v)’s is nothing but filtering functions that map all eigenvalues less than the inflection points to 1 and the rest to 0. § Thus the diagonal of this function is nothing but it’s trace…which counts how many eigenvalues there are less than the inflection points…then it is a matter of simple subtraction to get the spectrogram.
- 22. IBM Research - Zurich 22 Applying the filters in the diagonal stochastic estimator, we count how many eigenvalues are less than -0.5, 0 and 0.5. Thus it is trivial to get how many are in the intervals [-0.5, 0] and [0, 0.5] O(N) Method for Graph Spectrograms
- 23. IBM Research - Zurich Scalability of graph spectrogram calculator: Speedup 23 4 secs 25 secs Road network of Europe. 50M nodes, 150M edges.
- 24. IBM Research - Zurich Conclusions 24 We focus on knowledge graph analytics and develop: § O(N) cost methods that allow new powerful analytics § Cloud deployable and scalable to HPC resources as well Ø Node importance: Sub-graph node centralities Ø Graph Comparisons: Spectrograms We employ this work on key projects that drive impact and allow us to develop new functionality This is part of our roadmap to systematically attack the complexity of advanced ML and Cognitive algorithms
- 25. IBM Research - Zurich 25 Analyzing complex networks Using single server resources: Road network of Europe. 50M nodes, 150M edges. § 14 minutes on 16 cores § Real traffic monitoring at very large scale is thus now possible

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