1. Manifestation of Research Specialty Processes in Collections of Journal Papers Steven A. Morris Oklahoma State University

2. Summary Discuss research specialties Model collections of papers as systems of coupled bipartite networks Discuss entities, links, and entity groups as manifestations of research specialties in collections of papers Discuss visual presentation to reveal structural and dynamic information about a specialty

3. Goals Visualize structure and dynamics of a research specialty through a collection of papers –Social organization –Knowledge organization Present to subject matter experts for technology forecasting

4. Research specialty definitions A research specialty is a self-organized social organization whose members tend to study a common research topic, attend the same conferences, publish in the same journals, cite each other's work, and belong to the same social networks that are known as invisible colleges. Specialties create their own literature, i.e., a body of journal papers and books that broadly focus on the specialty's research topic. Define a collection of papers as a list of journal papers that constitutes a comprehensive sample of a specialty's journal literature.

5. Model of a research specialty Kuhnian paradigm Researchers Body of knowledge Symbolic generalizations Metaphysical paradigms Validation standards Exemplars Researcher local organization (Researcher team processes) Researcher global self-organization (Research global communication processes) Researcher education & training (Researcher entrance processes) Researcher retirement/out-migration (Reseacher exit processes) Funding Technical communication through journal literature and conferences Journal literature Conference literature Educational theses & dissertations Institutional reports Books Base knowledge Research reports Generated knowledge adopted as base knowledge produces ‘ paradigm creep ’

6. Size of specialties Specialties are usually small, less than 100 core members according to Kuhn. Collections of papers usually less than 5000 papers. Scaling not a big problem.

7. Static information sought about a specialty Identification and ranking of individual entities –Experts –Productive researchers –“Rising stars” –Centers of excellence –Exemplar references –Key journals

8. Static information sought about a specialty Structural mapping (groups and their relations) –Terms (subtopic ‘vocabularies’) –Papers (‘research fronts’ – papers grouped by subtopic) –References (exemplar reference groups, ‘paradigms’) –Paper authors (‘research teams’) –Reference authors (‘schools of thought’) –Paper journals (research report ‘libraries’) –Reference journals (base knowledge ‘libraries’)

9. Dynamic information sought about a specialty Monitoring –Trends Growth/decline of the specialty Obsolescence of knowledge Geographic migration of research activity –Discontinuous events Discoveries External events Forecasting –Extrapolate trends –Predict risk of discontinuous events

10. Why use journal papers to investigate a specialty? Vetted through review process Public record of researcher communication Permanent record Formatted, structured information available (through abstract services)

11. Gathering collections of papers to ‘cover’ a specialty Gathered from Science Citation Index Using seed references: –Find all papers that cite a collection of key references in the specialty Query of terms. –Find all papers associated with keyword terms that are related to the specialty –Index terms, title terms, abstract terms Query of reference authors –Find all papers that reference key authors in the specialty.

12. Entity-Relation model of a collection of journal papers 6 direct bipartite networks 15 indirect bipartite networks formed from cascading bipartite networks authors papers index terms paper journalsreference journals references reference authors

13. PAPERS PAPER AUTHORS PAPER JOURNALS REFERENCES REFERENCE AUTHORS REFERENCE JOURNALS INDEX TERMS INSTITU- TIONS HAS MANY UNIQUE APPEARS ONCE IN ONE APPEARS ONCE IN MULTIPLE HAS MANY UNIQUE APPEARS ONCE IN MULTIPLE HAS ONE APPEARS ONCE IN MULTIPLE APPEARS IN ONE HAVE MANY UNIQUE APPEARS MULTIPLE TIMES IN MULTPLE CONTAINS MULTIPLE MULTPLE TIMES CONTAINS MULTIPLE UNIQUE APPEARS ONCE IN MULTIPLE Entity-relationship model of a collection of journal papers citing entitiescited entities Other entities: Paper year Reference year

14. Bibliometric entities vs. physical entities Paper author H. G. Small Reference author Small HG Reference author Small H Physical author Henry Small Bibliometric entities are objects in the paper collection and acquire separate meaning. Physical entities are objects in the ‘real world’ that correspond to bibliometric entities.

15. PAPERS PAPER AUTHORS PAPER JOURNALS REFERENCES REFERENCE AUTHORS REFERENCE JOURNALS APPEARS ONCE IN MULTIPLE HAS MANY UNIQUE APPEARS ONCE IN MULTIPLE HAS ONE APPEARS IN ONE HAVE MANY UNIQUE CONTAINS MULTIPLE UNIQUE APPEARS ONCE INMULTIPLE PHYSICAL JOURNALS PHYSICAL PAPERS PHYSICAL AUTHORS CORRESPONDS TO ONE CORRESPONDS TO MULTIPLE CORRESPONDS TO ONE CORRESPONDS TO MULTIPLE CORRESPONDS TO ONE OR NONE CORRESPONDS TO MULTIPLE CORRESPONDS TO ONE CORRESPONDS TO ONE CORRESPONDS TO ONE CORRESPONDS TO MULTIPLE CORRESPONDS TO ONE CORRESPONDS TO MULTIPLE F404_2 Entity-relationship diagram showing relation of physical entities to bibliometric entities PAPERS CITING PAPERS AUTHORS CITING AUTHORS JOURNALS CITING JOURNALS

16. Papers citing papers networks paper citation Papers are reports, references are concept symbols: apples citing oranges Typically 20 times more references than papers: how to handle?

17. PAPER AUTHORS PAPER JOURNALS REFERENCES REFERENCE JOURNALS TERMS PAPERS REFERENCE AUTHORS Bibliographic entities as tokens of research specialty objects Concept symbols Report archives Base knowledge generators, experts Research reports Base knowledge archives Concept symbols, base knowledge Researchers

18. PAPER AUTHORS PAPER JOURNALS REFERENCES REFERENCE JOURNALS TERMS PAPERS REFERENCE AUTHORS Bibliographic links as tokens of research specialty relations Term associated with research reported by Journal archives research reported by Researcher generated base knowledge represented by Journal archives base knowledge represented by Research reported used base knowledge represented by Researcher participated in research reported by

19. Networks in collections of journal papers authorspapers Unipartite cooccurence network Bipartite network Like entities: entities of same entity-type Unlike entities: entities drawn from more than one entity-type

20. authors papers terms paper journalsreference journals references reference authors Networks in collections of journal papers

21. r1r2r3r4r5r6r7r1r2r3r4r5r6r7 p1p2p3p4p5p6p7p8p1p2p3p4p5p6p7p8 ap 1 ap 2 ap 3 ap 4 ap 5 ap 6 ap 7 ap 8 ar 1 ar 2 ar 3 ar 4 reference authors referencespapers paper authors Cascaded bipartite networks

22. Occurrence and co-occurrence matrices Each occurrence matrix has two associated co-occurrence matrices.

23. PAPER AUTHORS PAPER JOURNALS REFERENCES REFERENCE JOURNALS TERMS Bibliographic coupling Reference co-citation PAPERS REFERENCE AUTHORS Author co-citation Journal co-citation Co-authorship Term co-occurrence Paper coupling by term Paper coupling by paper journal Paper coupling by reference journal Paper coupling by paper author Paper coupling by reference author PRIMARY ENTITYRELATIVE ENTITY CO-OCCURRENCE RELATION KEY PAPERS REFER -ENCES Bibliographic coupling Co-occurrence relations Co-occurrence relations are used map the structure of a scientific specialty by providing a means to find entity groups through clustering.

24. PAPER AUTHORS PAPER JOURNALS REFERENCES REFERENCE JOURNALS TERMS PAPERS REFERENCE AUTHORS Bibliographic cooccurrence links as tokens of unipartite research specialty relations Two terms both associated with research reported by Two papers associated with similar research Two pieces of base knowledge both used in research reported by Two papers’ research both used base knowledge represented by Two researchers worked together on research reported by Two researchers both generated base knowledge used by research reported by

25. Paper author to reference author matrix, O[ap;ar] Paper author co-occurrence matrix, C[ap;ar] Paper author i Occurrence feature vector, O i [ap;ar] Co-occurrence feature vector, C i [ap;ar] F404_34 Entity feature vectors Features are measurable quantities used to characterize entities for pattern recognition and clustering purposes. A feature vector is an array of features used for pattern recognition and clustering. Each entity has two feature vectors per occurrence matrix.

26. Interpretation of occurrence feature vectors Examples of occurrence feature vectors for entities in a collection of papers. Primary entity- type x 1 Secondary entity-type x 2 Feature vector for entity i Interpretation paperreferenceOi[p;r]Oi[p;r]a) The concept symbols used by a paper (Small, 1978). b) the knowledge sources used by a paper. referencepaperOi[r;p]Oi[r;p]The papers using a reference as a concept symbol. paper author paperO i [ap;p]A paper author’s oeuvre paper author reference author O i [ap;ar]The reference authors whose work a paper author reads and uses. An author’s identity (White, 2001). reference author paper author O i [ar;ap]The paper authors that read and use a reference author’s work. paper journal reference journal O i [jp;jr]The reference journals holding source knowledge used by papers in a paper journal reference journal paper journal O i [jr;jp]The paper journals whose papers draw knowledge from a reference journal papertermsOi[p;t]Oi[p;t]A paper’s research vocabulary

27. Interpretation of co-occurrence feature vectors Examples of co-occurrence feature vectors for entities in a collection of papers. Primary entity- type x 1 Secondary entity-type x 2 Feature vector for entity i Interpretation paperreferenceCi[p;r]Ci[p;r]The papers that use the same concept symbols as paper i. (Papers covering the same topic as paper i.) referencepaperCi[r;p]Ci[r;p]The references being used by the same papers the use reference i. (Exemplar references for the same Kuhnian paradigm as reference i. ) paper author paperC i [ap;p]The collaborators of paper author i. paper author reference author C i [ap;ar]The paper authors using the same knowledge sources as paper author i. Paper author i’s invisible college. reference author paper author C i [ar;ap]The reference authors used as knowledge sources by the same paper authors as reference author i. The image of reference author i. (White, 2001) paper journal reference journal C i [jp;jr]The paper journals using the same sources of knowledge as paper journal i. reference journal paper journal C i [jr;jp]The reference journals (sources of knowledge) being used by the same paper journals as reference journal i. papertermsCi[p;t]Ci[p;t]Papers using the same research vocabulary as paper i. (Papers covering the same topic as reference journal i.)

28. PAPER AUTHORS PAPER JOURNALS REFERENCES REFERENCE JOURNALS TERMS PAPERS REFERENCE AUTHORS Bibliographic cooccurrence clusters as tokens of research specialty group objects Research subtopic vocabularies Base knowledge groups, “paradigms” Research front: “papers by topic” Research teams Base knowledge generator groups. “schools of thought” Research team oeuvres Research front library Base knowledge libraries

29. Visualization of matrices

30. Research front timeline Papers clustered by common references to form a hierarchical collection of research fronts Papers plotted as circles in track by research front. Circle size is proportional to total times cited, redness is proportional to times cited in the last year. Labels manually generated by browsing titles in paper clusters for themes

31. Major sub-specialies Modern toxin research Vaccines and genetics 1950’s to 1970’s research Detection of anthraxBioterrorism

32. Historical development

33. Research front to reference crossmap CURRENT TOXIN RESEARCH CURRENT VACCINE RESEARCH BIOTERROR ANTHRAX DETECTION 1980’S& EARLY ’90’S TOXIN AND VACCINE RESEARCH EARLY TOXIN RESEARCH EARLY RESEARCH TOXIN AND VACCINE Dixon reference

34. Reference usage plot BREAKTHROUGH IN TOXIN RESEARCH LEPPLA& FRIEDLANDER KEY REFERENCES IN TOXIN RESEARCH BRACHMAN STUDY OF VACCINE EFFICACY OBSOLETE EARLY RESEARCH OLD REFERENCES STILL CURRENT BIOTERROR DETECTION EARLY INHALATION ANTHRAX RESEARCH

35. Paper author usage plot FRIEDLANDER LEPPLA TURNBULL WRIGHT THORNE NO LONGER ACTIVE COLLIER MOCK NO LONGER ACTIVE

36. Research front to index terms crossmap TOXIN TERMS TOXIN EXPRESSION TERMS VACCINE TERMS BIOTERROR TERMS

37. Questions

38. EVENT REPORT LINGUIS- TIC TERMS INCIDENT TYPE TOWNDISTRICTCOUNTRY GOVERN- MENT OFFICIAL TERROR- IST PERSONAL NAMES TERROR- IST GROUP VICTIM LAW INFORCE- MENT OFFICER OTHER ENTITIES: EVENT DATE REPORT DATE LINKS SUPPLIED BY ANALYSTS OR INFERENCE DIRECT LINKS FROM ENTITY EXTRACTION PROPOSED ENTITY-RELATIONSHIP MODEL FOR TERRORIST INCIDENT REPORTS ENTITIES IN YELLOW TO BE IMPLEMENTED