資料探勘 走向決策支援 - library.tmu.edu.tw

資料探勘 走向決策支援 - library.tmu.edu.tw

Search Problem Search Query: Jaguar Jaguar(Animal) Jaguar(Automobile) Jaguar(Watch) Jaguar(OS) Monika Henzinger, Search Technologies for the Internet Science, Vol. 317. no. 5837, 468 471, 27 July 2007 2 records are recognized as agency assets used to underpin current business and legal needs, as well as the basis for a knowledge management system to meet future goals. HOWARD P. LOWELL Director Modern Records Programs NARA

KDD Process Interpretation/ Evaluation Data Mining Transformation Preprocessing Pattern Selection Transformed Data Preprocessed Data Target Data Data Warehouse Knowledge

BI metadata Other sources Operational DBs Data Sources Monitor & Integrator Extract Transform Load Refresh Complete Data Warehouse Data Marts OLAP Server

Server 1. Comprehensive Performance Management 2. Analysis 3. Query 4. Reports 5. Data mining Tools Business Intelligence 8 Gaining market intelligence from news feeds 9 Sreekumar Sukumaran and Ashish Sureka Signal Dr. Bhandari said, I first noticed this when the New York Times did an analysis after the fact showing that early indications of the FordExplorer-Firestone-tire problem went undetected in a federal database. Recently, a similar analysis by CNN showed that early

indications of security problems at Logan, Dulles, and Newark airports, went undetected in a federal database well before the September 11 tragedy. It is clear that the cost of missing these patterns is too high to be ignored. Mining target: individual text Mining unit: >texts >category labeled items extracted from text using NLP Original Data Structured Data Call Taker: James Date: Aug. 30, 2002 Duration: 10 min. CustomerID: ADC00123 Q: cust sys has stopped working. A: checked cust bios and it need updated. Category Meta Data Category Dictionary

Synonym Dictionary Linguistic Analysis Unstructured Data Tagging Dependency Analysis Named Entity Extraction Intention Analysis Item Visualization & Interactive Mining [Call Taker] James [Date] 2002/08/30 [Duration] 10 min. [CustomerID] ADC00123 Mining [Noun] Customer [Software] BIOS

[Subj...Verb] customer system..stop [SW..Problem] BIOS..need IBM TAKMI (Nasukawa, Nagano,1999) Medline 1. Swanson DR. Searching natural language text by computer. Machine indexing and text searching offer an approach to the basic problems of library automation. Science. 132:10991104, 21 Oct. 1960. 2. Swanson DR. Fish oil, Raynaud's syndrome, and undiscovered public knowledge. Perspect Biol Med. 30(1):718, 1986. 3. Swanson, D.R., Complementary structures in disjoint science literatures. In A. Bookstein, et al (Eds.), SIGIR91: Proceedings of the Fourteenth Annual International ACM/SIGIR Conference on Research and Development in Information Retrieval Chicago, Oct 13-16, 280-289, 1991. ? Stress is associated with migraines Stress can lead to loss of magnesium Calcium channel blockers prevent some

migraines Magnesium is a natural calcium channel blocker Spreading cortical depression (SCD) is implicated in some migraines High levels of magnesium inhibit SCD Migraine patients have high platelet aggregability Magnesium can suppress platelet aggregability Smalheiser, N.R. & Swanson, D.R.. Assessing a gap in the biomedical literature: Magnesium deficiency and neurologic disease. Neuroscience Research Communications, 15, 1-9, 1994. All Migraine Research migraine CCB PA SCD stress All Nutrition Research magnesium

Raynauds Hypothesis generation Fish oils vasoconstrictions platelet aggregation blood viscosity Intermediate concepts Swanson, D.R. (1994). Fish oil, Raynaud's syndrome, and undiscovered public knowledge. Perspect Biol Med. Autumn;30(1):7-18, 1986 . - NLP 1948 Birkbeck College 1949- Warren Weaver American Interest 1950- (German to English, Russian to English)

1966~ (Dr. Eye?)) NLP brought the first hostility of research funding agencies. NLP gave AI a bad name before AI had a name. 2006 1,610 GB( 161 Exabytes) IDC 2006 2010 2010 70% 85% The Expanding Digital Universe, http://www.emc.com/leadership/digital-universe/ expanding-digital-universe.htm 100

90 80 70 60 50 40 30 20 10 Oracle 0 Search Engine Roadmap Exploratory Search Affiliation (Topic Relevance

Analysis) Dictionary/Ontology Wikis Full Text Search Including complex Boolean search Clustering/Categorize Synonym/Anatomy Document Abstraction Custom Search Knowledge collaborative search Filtering Crawler Integrate other search engines Summarization (mobile) Multiple abstracts

organization Search log recorder Personal tagging Sharing Forum/Blogger Customized meta-search Taxonomy search Natural language processing/ understanding Web Page Features Extraction (semi- and un-structure) Feature Ranking Feature Mapping Recommendation Taxonomy Search Visual Technology Ajax Topological Graphics Web 2.0 or upper

Collaborative Filting Visualization ( inverted) Monika Henzinger, Search Technologies for the Internet Science, Vol. 317. no. 5837, 468 471, 27 July 2007 Search Engine Problems Index Comprehensiveness Relevance Deterministic Search Search Query Jaguar(Animal) Jaguar(Automobile) Jaguar(Watch) Jaguar(OS) Problem: Scalable J, Beall, The Weaknesses of Full-Text Searching. The

Journal of Academic Librianship, 34(5):438-444, 2008. , -- , 1995-1997 AV, Excite, Lycos, etc From 1998. Made popular by Google but everyone now (What results people click on) (Hyperlinks, How people refer to this page) -- what is this about?) ,

Still experimental , , Trailblazer Car Basketball team Monika Henzinger, Search Technologies for the Internet Science, Vol. 317. no. 5837, 468 471, 27 July 2007 Expand Crawler Basic Crawler

Wrapper/Clipper XHTML, DHTML Parser Feature Transformation XML Parser Structural Features Extraction HTML Parser Scheduling Clipper Windows Specify Hertrix Crawler Unstructured Document Features Extractions (NLP) Feature Mapping Ontological Organization Specific Feature Parse

Filtering Ontology Machine Learning Approach Semantic Crawler P2P Knowledge Sharing Crawler Crawler Classes Annotated Crawler Craw with specific terms/phases Crawler Supporting Information from original sources & Reference contents Filter Outside Search

Engine Data Sources Learner Relevant Information Feed into Reference List Authoring User process Filtering NE records Web Crawler Classes Page/Section/Block/Item Specify GUI Specification System Scheduler Crawler Notify for manually tune Adaptor Logger

Log Named Entities Recognition Comparator Compare the extracted structure between two stages Feature Extractor Repository 1. 2. Walter Warnick, Problems of Searching in Web Databases. Science . Vol. 316. no. 5829, 1284, June 2007. I-Jen Chiang, Discover the Semantic Topology in High-Dimensional Data, Expert Systems with Applications, 33 (1), September, 2007. d1

d2 dm t1 t 2 tn w11 w12 w1n w21 w22 w2n wm1 wm2 wmn Term similarity Doc similarity Term Weighting Tokenized text Stemming & Stop words Sentence selection t t tttt tt t t tt

ddddd d dd dd d dd d Vector centroid d Raw text META-DATA/ ANNOTATION / Saltons Vector Space Model (Bag of Words)Bag of Words) A Cosine Similarity

Jaccard index B Jaccard similarity coefficient Tanimoto coefficient G. Salton, A. Wong, and C. S. Yang, "A Vector Space Model for Automatic Indexing," Communications of the ACM, vol. 18, nr. 11, 613620, 1975. Curse of Dimensions 1 : I saw the man on the hill with telescope Using a telescope, I saw a man who was on a hill. I saw the man on the hill with telescope I saw a man who was on a hill and who had a telescope.

I saw the man on the hill with telescope I saw a man who was on the hill that has a telescope on it. I saw the man on the hill with telescope The delegation, which included the commander of the U.N. troops in Bosnia, Lt. Gen. Sir Michael Rose, went to the Serb stronghold of Pale, near Sarajevo, for talks with Bosnian Serb leader Radovan Karadzic. training sentences

Training Program answers NE Models Speech Speech Recognition Entities Extractor Text Prior to 1997 - no learning approach competitive with hand-built rule systems Since 1997 - Statistical approaches (BBN (Bikel et al. 1997), NYU, MITRE, CMU/JustSystems) achieve state-ofthe-art performance 1. 2. 3. 4.

The delegation, which included the commander of the U.N. troops in Bosnia, Lt. Gen. Sir Michael Rose, went to the Serb stronghold of Pale, near Sarajevo, for talks with Bosnian Serb leader Radovan Karadzic. M. Marcus. New trends in natural language processing: Statistical natural language processing. PNAS. 92. 10052-10059, 1995. Current Trends in Biomedical Natural Language Processing, Ohio State University, June 2008 Tanveer Siddiqui. National Language Processing and Information Retrieval. Oxford Univ Press, 2008. Yorick Wilks. Natural Language Processing as a Foundation of the Semantic Web. Foundations and Trends in Web Science, 1(3-4). 199-327, 2009. I-Jen Chiang

( )

Integrated BI Systems ETL Complete Data Warehouse RDBMS Structural Data File System XML XML Text tagger & Annotator ETL DBMS Intermedia Data

EA Unstructured Data Legacy CMS Scanned Documents Email Sreekumar Sukumaran and Ashish Sureka Date Acquiring Organization Acquisition Event Acquired Organization

On November 16, 2005, IBM announced it had acquired Collation, a privately held company based in Redwood City, California for undisclosed amount. Place Amount Output to RDBMS Text Annotator Date Organization Place Amount Nov. 16 IBM Redwood City, CA Undisclosed

XML output On November 16, 2005, IBM announced it had acquired Collation, a privately held company based in Redwood City, California for undisclosed amount. McIlraith, S.A., Son, T.C., Zeng, H.: Semantic web services. IEEE Intelligent Systems 16, 4653, 2001 BI Intermedia Data ETL Complete Data Warehouse RDBMS Text tagger & Annotator ETL Structural Data DBMS File System

XML XML EA Unstructured Data Legacy CMS Sreekumar Sukumaran and Ashish Sureka Scanned Documents Email Knowledge-based Persistent Archives Knowledge Repository for Rules Access Rules - KQL Knowledge

Relationships Between Concepts Manage XTM DTD Ingest Knowledge or Topic-Based Query Attributes Semantics Information Repository EMCAT / MIX Information XML DTD

(Topic Maps / Model-based Access) Attribute- based Query Fields Containers Folders Storage (Replicas, Persistent IDs) GRIDS Data MCAT/HDF (Data Handling System - Storage Resource Broker) Feature-based Query NExIOM Ontology Models

Mapping Translation Models Discipline Ontology Models Mapping Electrical Power Electrical Power Analysis Analysis W S Structure and Connectivity W S W S Trade-Offs

Analysis Risk Modeling W S W S W S Interaction Logic Application Logic Semantic Interface W S Cost Modeling Semantic Application Performance Modeling NASA iLoC SBA Workspace

TopSCAPE Mapping Mapping WS WS WS Ontology Authoring WS COVE SI SI IL AL BL SI

IL AL BL SI IDT DB T1 RFx DB T2 Text Mining for Hypertext Creation A general topic Concept map Subtopic 1 Subtopic i Subtopic M ... Doc 1

Doc 2 Hypertext Doc N Type of Links Term Term Links DocTerm Links A general topic TermDoc Links Subtopic 1 Subtopic i Subtopic M ... Doc 1 Doc 2 Doc N Doc Doc Links

Example from an Enterprise Architecture Process Ontology Agent Role Process Task Measur e Goal fea: Mission fea: intentOf prm: GenericMeasurementIndicator fea: Agency brm: provides fea: hasIntent brm: SubFunction brm: hasProcess

brm: Process brm: usesResource brm: Resource prm: PerformanceMeasure prm:hasSpecializati on prm:hasIndicator brm: hasPerformance brm: realizedWith brm: hasCustomer fea: Customer prm: OperationalizedMeasurementIndicato r srm: Service ROYAL MARSDEN NHS TRUST - PATIENT CASE NOTE ######:MRS ##### ####### 27 Aug 1998 Seen in the Follow Up Staging Clinic

This 65 year old lady has been reviewed in the Breast staging clinic. As you know, she was originally diagnosed with a carcinoma of the left ROYAL MARSDEN NHS TRUST - PATIENT CASE NOTE breast in 1974 and treated with a total mastectomy. This was followed ######:MRS ##### ####### with MEFUP chemotherapy. In 1982 she noticed a lump in the infraclavicular region which was excised and this was followed by 15 Dec 1993 ROYAL MARSDEN NHS TRUST - DIAGNOSTIC RADIOLOGY - CT REPORT radiotherapy. In 1994 she developed a tumour in the chest cavity that General Surgical I reviewed this patient in clinic today. She has been followed ROYAL MARSDEN NHS TRUST - PATIENT CASE NOTE

Exam up for a left breast carcinoma for which she was treated with a ######:MRS ##### ####### mastectomy. She had a prosthesis removed last year and has had some improvement in the symptoms of chest wall discomfort since 24 Jan 1997 Seen was diagnosed with a CT guided biopsy and this was treated with VAC ######:#######,MRS ##### chemotherapy and radiotherapy to the mediastinum. Since 1994 she had 18 Dec Examination LIVER/THORAX/ABDOMEN/PELVIS noticed a slight deterioration and earlier this year she had problems Exam Number [NUM] with occasional episodes of vomiting, nausea and general lethargy. She Date of Birth 17 May 1933 Ref in the Chemotherapy Clinic (TPFRIDAY)

[HCA1] Clinical then although she still gets quite sharp pains intermittently. I saw ##### today in clinic. I am very pleased to say that she has BRhad Verified by She has been reviewed in the pain clinic local to where she was found to have lymphadenopathy in the right supraclavicular fossa and was treated with Arimidex. Since being on Arimidex there was OUTPATIENT originally stablisation of her disease but recently it appears that the node has started to enlarge. [HCA2] On examination today, she has a 1.5x1cm lymph node in the right supraclavicular fossa and an essence of thickening probably due to a complete response in her superior mediastinum and rightDIAGNOSIS: Carcinoma of breast. lives but has not had much relief of her symptoms. She feels supraclavicular

though that she can bear with these and does not want any previous therapy in the left supraclavicular fossa. She also has CT scans have been obtained through chest, abdomen and pelvis with oral fossa lymphadenopathy. There is some minimal thickening radiation changes in the lung which produced some physical sign at both contrast only. further intervention at present. remaining in the soft tissues around the superior mediastinum and in On examination today there is no sign of recurrence of her fact it is felt that this might now be related to previous disease. Chest and abdominal examination were unremarkable. We radiotherapy. To be will see her again in a year's time. bases and there was no evidence of abdominal organomegaly.

Her recent staging investigations show that she has C5 carcinoma cells There is thickening in the left clavicular fossa and smallpresent in the lymph node fine needle aspirate. A right mammogram is volume residual abnormalities in unremarkable. the mediastinum. Comparison is made An ultrasound of the liver was normal and a chest x-ray honest, however, symptomatically there withhas thebeen most recent scan (21.7.95) andsome there no discernible change showed softistissue thickening present in the left axilla due to little in the way of benefit with overall palliative response by of CT no criteria.

28/03/2003, 10:35:26 previous therapy. There is also some loss of volume in the left upper zone but no lung nodules seen. A bone scan shows evidence of Lung changes, which may have been related to radiotherapy, are now less degenerative changes but no specific evidence of bony metastases. Her change. She is tolerating the treatment fairly well. Interestingly she extensive. thyroid function tests show that the TSH is 0.12 and her free T3 are 4 has had virtually complete alopecia with the treatment. SheThere has are been on which indicates that the TSH is slightly low. This does not amount to no abnormally-enlarged nodes in the retroperitoneum primary hypothyroidism but it would be worth repeating the thyroid warfarin for about the same amount of time and I wonder whether this are no focal hepatic or pelvis. There masses.

function tests in three months time. CONCLUSION: No CT evidenceOverall, of disease progression. it appears that the patient has stable disease on Arimidex may be partly responsible. We have given her a fourth cycle of apart from in the right supraclavicular fossa. The Arimidex is not treatment today and we will see her in three weeks for consideration of 28/03/2003, 12:35:06 holding the disease completely and we feel that the best approach to management would be to consider some radiotherapy to the right her fifth. supraclavicular fossa. She has previously had radiation therapy to the 28/03/2003, 10:44:20 left clavicular region and mediastinum. We have discussed performing a CT scan of the thorax but she was unable to lie flat for the duration

of the investigation some months ago. We shall ask our radiotherapy colleagues to review her and consider her for therapy. We shall review her again in the follow up clinic in six weeks time. 28/03/2003, 10:50:25 Consider a 62-year-old man with 3 months history of severe back pain. His weight remained stable. CBC and routine biochemistry were normal. ESR was 52 mm / hour. An x-ray of the lumbar and thoracic spine was reported to showing degenerative changes. Cancer Low back pain History and physical examination History of Previous cancer Age > 50 years or Failure of treatment or weight loss

ESR,spine Films, 9% with cancer No significant finding ESR ESR < 20 and only one clinical Finding No cancer ESR > 20 or more than one clinical finding X-ray 2.3% cancer What was done What happened And why Human:1382 Pain:5735

locus attends reason locus Breast:1492 Clinic:4096 reason plans Clinic:1024 plans plans reason Biopsy:1066 locus target

plans finding finding Clinic:2010 reason Radio:1812 plans Chemo:6502 treats reason time reason attends

attends Mass:1666 Ulcer:1945 locus treats locus time Cancer:1914 time time time time time time Concept Lattice

Given the context (D1,T1) where D1 = {d1,d2,d3,d4} & T1 = {t1,t2,t3,t4,t5,t6} Hasse Diagram C1:(D1,) R t1 t2 t3 t4 t5 t6 d1 1 0 1 0 1 1 C2:({d1,d2,d4},{t1,t6}) C3:({d3,d4},{t4}) d2 1 0 1 0 1 1 d3 0 1 0 1 0 0 d4 1 0 0 1 0 1 C5:({d4},{t1,t4,t6}) C4:({d1,d2},{t1,t3,t5,t6}) C6:({d3},{t2,t4}) Table: The input relation R = documents keywords C7:(, T1) The formal concept C4 has two own terms {t3,t5} and two inherited terms {t1,t6} Text Analysis Spectrum Classification

Concept Identification Targeted Facts and Events Entity Extraction Clustering What is this document about? Who did what to whom when where, etc. Why is getting dimensional data so hard? Hank bought plastic explosives from Henry in Tucson yesterday. Named Entity Extraction People, Weapons, Vehicles, Dates

NER Engine Hank Henry Plastic explosives 11/01/07 Tucson Automatic PatternLearning Systems Language Input Answers Pros: Portable across domains Tend to have broad coverage Trainer Language Robust in the face of degraded input. Input

Model Decoder Answers Automatically find appropriate statistical patterns System knowledge not needed by those who supply the domain knowledge. Cons: Annotated training data, and lots of it, is needed. Isnt necessarily better or cheaper than hand-built soln Examples: Riloff et al., AutoSlog, Soderland WHISK (UMass); Mooney et al. Rapier (UTexas); Ciravegna (Sheffield) Learn lexico-syntactic patterns from templates Explicit Events, Object Identity, Symmetry E52 Time-Span E39 Actor E53 Place

7012124 February 1945 P82 at some P11 par tici time within pat ed in E7 Activity P7 took place at Crimea Conference E39 Actor E38 Image P6 by 7 is r P86 falls efe within

rre dt o E65 Creation Event * E39 Actor P14 ed m r fo per P81 ongoing throughout E52 Time-Span 11-2-1945 P9 cre 4 ha ate s d E31 Document

Yalta Agreement Rules Extraction The formal concept C4 makes it possible the following rules R1 : t3 t1 t6 R2 : t5 t1 t6 R3 : t3 t5 The interpretation of the R1 and R2: The use of terms t3 or t5 is always associated with that of terms t1 and t6 The rule R3 express mutual equivalence of the terms {t3,t5}: All the documents which have the term t3 also have the t5 term. --

NER Example 2

[Nb] 2 [Nb]

1 [Nb] 1 [Nb] 1 [Nb] 1 [Nb] 1

[Nb] 1 [Nb] 2 [Nb] 1 [Nb] 1 [Nb] 1

[Nb] 1 [Nb] 3 [Nb] 1 [Nb] 1

[Nb] 1 [Nb] 2 [Nb] 3 [Nb] 4 [Nb]

2 [Nb] 1 [Nb] 1 [Nb] 1 [Nb] 1

[Nb] 2 [Nb] 2 [Nb] 1 [Nb] 1

[Nc] 1 [Nc] 1 [Nc] 1 [Nc] 1

[Nc] 1 [Nd] 4 [Nd] 1

[Nd] 1 [Nd] 1 [Nd] 2 [Nd] 1 [Nd]

6 [Nd] 1 [LN] 2 [LN] 1

Generative Discriminative Generalize Object: attribute Object: Attribute (condition) method object Object: attribute

Object: attribute Object: attribute Object: attribute Specify Object: condition

99 99

80 81 for Malignancy DSS Patient (Patient ID) ESR Screening (Positive) Symptom (Positive Indication) Cancer Bag of Words extraction Expressions extraction Decision Making Patient ID Named Entities malignancy

ESR extraction Treatment severe Patient ID Events/Sentiment ESR back Extraction severe back pain pain x-ray x-ray lumbar Patient ID Diagnostic term lumbar spine malignancy? malignancy spine degenerative changes ESR screening test degenerative Lumber, Spine Anatomy Term Combined changes degenerative changes Symptom With structured data

Information Retrieval Information Extraction Knowledge Inference ( ) Clustering Association Rules Visualization Processing Guideline Local data FTP Gopher HTML More structure Indexing

Search Relevance Ranking Latent Semantic Topology Crawling WebSQL Social Network of Hyperlinks WebL XML Clustering Collaborative Filtering ScatterGather Topic Directories Semi-supervised Automatic Learning Classification

Web Communities Web Servers Topic Distillation Focused Crawling Monitor Mine Modify User Profiling Web Browsers

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