KNOWLEDGE GRAPH: FROM METADATA TO INFORMATION VISUALIZATION AND BACK. Xia Lin College of Computing and Informatics Drexel University Philadelphia, PA

Transcription

1 KNOWLEDGE GRAPH: FROM METADATA TO INFORMATION VISUALIZATION AND BACK Xia Lin College of Computing and Informatics Drexel University Philadelphia, PA 1

2 A little background of me Teach at Drexel University since 1997 First at CIS College of Information Systems and Technology Then at ischool The Information Then at CCI College of Computing and Informatics The school moves with time ischool the intersection of people, technology, and information Informatics the science of information; the meaning of information, and the practice of information. 2

3 My research areas Information Visualization Self-organizing maps of information Visual interfaces for information exploration and discovery Information Retrieval and Knowledge Organization Knowledge structure mapping Metadata and knowledge organization systems Human-centered Computing Information seeking in the digital environment Information architecture 3

4 Outline of the talk Two big pictures and a little one Google s knowledge graphs IBM s Watson cognitive system My research projects on concept mapping and visualization The focus is on the little one Challenges on Interacting with concepts and languages Visualizing concepts and concept networks Representing concepts meaningfully 4

5 Google s Knowledge Graph Announced on May 16, 2012 Introducing the Knowledge Graph: things, not strings Known as a knowledge base and a semantic network Identify and connect facts about people, places, and things Understand relationships among topics and things Added to Google Search Engine to enhance search results and displays Understand search queries better Summarize relevant content and facts around the topics from the query go deeper and broader 5

6 Knowledge Graph Da Vinci 6

7 Knowledge Graph Display Box 7

8 SKKU 8

9 Knowledge Base for Knowledge Graph Freebase: Wikipedia/Dbpedia: Knowledge Vault Knowledge base by Google containing more than 1.6 billions factors Substantially bigger than any previous published structured knowledge repository Using supervised machine learning to fusing multiple information sources. 9

10 Knowledge Vault Knowledge base by Google containing more than 1.6 billions factors Substantially bigger than any previous published structured knowledge repository Using supervised machine learning to fusing multiple information sources. 10

11 From Search to Answers Knowledge Vault 11

12 IBM Watson s Knowledge Corpus From: NLP + Machine learning To: Deep understanding + Hypothesis testing 12

13 Cognitive Computing 13

14 Toward deep understanding: From text corpus to knowledge base The ability to understand To decompose expressions into entities and then combine them with context Semantic annotation Deep parsing To keep track of human behaviors The ability to learn To extract syntactic knowledge and convert it into semantic knowledge To generate and test hypothesis To interact with domain experts and improve its knowledge base 14

15 Toward deep understanding: From knowledge base to answers The ability to use/apply knowledge? Synthesized knowledge store Question and topic analysis Visual analytics The ability to collaborate with humans? Natural language interfaces Visual interfaces Display Box interfaces 15

16 My research: Toward deep understanding through concept mapping and visualization Knowledge Stores: Use existing KOS Knowledge Organization Systems Ontology/Classification Systems/Thesaurus/ Understanding & Learning Linking concepts in one ontology to another Converting statistics links to semantic links Human Computer Collaboration Combine expert s knowledge with automatic mapping Interactive mapping & visualization 16

17 Project 1: Digging into Metadata Goal: Enhance and unify metadata of three digital libraries through Dewey Decimal Classification (DDC) 17

18 Project 1: Digging into Metadata Analysis: Porter stemming and stop word removal Calculate term weights and term frequency (TF) Apply Threshold : > Mean(TF) + 1SD(TF) Identify and process noun phrases (Termine, Sum(TF)) Connect DDC nodes based on similarities of documents assigned to the DDC classes. 18

19 Project 1: Digging into Metadata Created DDC-based Interface: 000 Computer science, information & general works 100 Philosophy & psychology 200 Religion 300 Social sciences 400 Language 500 Science 600 Technology 700 Arts & recreation 800 Literature 900 History & geography 19

20 Interface: The Global View 20

21 Interface: The Interactive View The experimental interface: Demo video: Live: 21

22 Project 2: Semantic Annotation & Visualization Goal: Integrate ontologies and a brain model with a large collection of neuroscience literature 1 million Documents from Elsevier Large(Document(Collec4ons( Neuroscience Information Framework (NIF) Standard Ontology Annotation Integration Visualization Ontology(1( Ontology(2( Modeling( 22

23 System Technology Semantic Annotation Start Ontologies 1. Protégé is used to parse the OWL-formatted ontology files. 2. Lingpipe is then used to implement the term matching. The matching terms are saved into Trie, also called prefix tree, which is a data structure used to improve search efficiency. 3. Chunk is an interface in Lingpipe that specifies a slice of a character sequence and used to match the article with terms in Trie. 4. Levenshtein distance is used here to calculate the similarity of two strings. Lingpipe Article Chunk Levenshtein Distance Positions for Matched Terms Protégé API Trie Dictionary Highlighted Articles 23

24 System Technology Semantic Integration Semantic Integration: Linking concepts in one ontology to concepts in another ontology. A tool called SemIntegrator was created to annotate multiple ontologies to the same collection, and then use the collection as the bridge to link two or more ontologies. NIF-Dysfunction Ontology Brain Atlas Ontology Annotate Extract degeneration in parietal lobe Extract Alzheimer s Disease Semantic Integration frontal cortex cingulat e gyrus 24

25 System Technology Semantic Visualization The focus of semantic visualization in this project is to visualize knowledge structure of ontologies and document collections. Different types of semantic relationships Some of those relationships are explicitly defined. Some are derived from their semantic relationships or co-occurrence relationships. Some are learned or discovered through computational learning algorithms. 25

26 The Visualization Interface 26

27 The Visualization Interface Click Parietal lobe on the brain map The list of concepts is shown 27

28 The Visualization Interface Concept Network Shown The Nodes are clickable Click Superior parietal cortex in the concept list Document search with the concept 28

29 The Visualization Interface Concept Network Shown The Nodes are clickable Click Superior parietal cortex in the concept list Hierarchy is shown on another tab Document search with the concept 29

30 Project 3: From KOS to MCD Goals: Use Knowledge Organization Systems (KOS) for query expansion and searching filtering Develop Meaningful Concept Displays (MCD) to improve user s searching, browsing, and learning experience Collaboration with Dagobert Soergel, University of Buffalo Bill Ying, ARTstor Murtha Baca, Getty Research 30

31 Project 3: KOS MCD Architecture 31

32 Project 3: Challenges Knowledge Store: Create a unified database for multiple KOS Create mapping tables that link concepts and their subcomponents from one KOS to another Build standardized APIs Analysis and mapping: Semi-automatic mapping Manually identify string patterns and facets: oil on canvas à coating on surface Use regular expression to match and group matching patterns Apply the analysis to both query analysis and search process. 32

33 Project 3: Mapping details Mapping strategy Decompose queries (or terms) to elementary concepts whatever possible Identify facets of the elementary concepts Map the elementary concepts to KOS terms Let the users select indexing terms that match their needs Use the facets to limit search results Mapping efficiency Batch processing to map the whole KOS and save results in the Knowledge Store. 33

34 MCD facet-based Interface 34

35 MCD Librarians Interface 35

36 Summary Moving from metadata to Knowledge Graph and back to metadata: Building significant knowledge stores Knowledge graph or KOS stores Applying language analysis/concept mapping/machine learning/ for deep understanding Developing intelligent interfaces Meaningful Visual Useful 36