Die Präsentation wird geladen. Bitte warten

Die Präsentation wird geladen. Bitte warten

1 1 Bettina Berendt Humboldt-Universität zu Berlin – www.berendt.de * mit vielen Ko-AutorInnen ** mit Roberto Navigli, Università “La Sapienza”, Roma,

Ähnliche Präsentationen


Präsentation zum Thema: "1 1 Bettina Berendt Humboldt-Universität zu Berlin – www.berendt.de * mit vielen Ko-AutorInnen ** mit Roberto Navigli, Università “La Sapienza”, Roma,"—  Präsentation transkript:

1 1 1 Bettina Berendt Humboldt-Universität zu Berlin – * mit vielen Ko-AutorInnen ** mit Roberto Navigli, Università “La Sapienza”, Roma, Italy Semantic Web Mining* Heute: Semantik für und aus Blogs**

2 2 2 Agenda 1. Motivation und Überblick n Warum Web? Warum Blogs? n Semantic Web Mining 2. Finding your way through blogspace: Using semantics for cross-domain blog analysis

3 3 3 Agenda 1. Motivation und Überblick n Warum Web? Warum Blogs? n Semantic Web Mining 2. Finding your way through blogspace: Using semantics for cross-domain blog analysis

4 4 4 Das Ziel

5 5 5 Das Wissen der Menschheit möglichst vielen Menschen effektiv zugänglich machen.

6 6 6 “Makrokosmos World Wide Web”

7 7 7 “Mikrokosmos Blogosphere”

8 8 8 Konkrete Ziele (Bsp. für Teil 2 dieses Vortrags) Klassifikation: „Dieser Blog behandelt Inhalte aus Ernährung und Gastronomie.“  Vorschläge von Meta-Tags für den Blog  Unterstützung von Blog-Suchmaschinen Empfehlungen mit Erklärung: „Wenn Sie diesen Blog interessant fanden, dann wird Sie vielleicht auch Blog... interessieren, und zwar weil...“

9 9 9 Das Potenzial

10 10 Sehr viel Wissen, für Menschen zugänglich.

11 11 Die Probleme

12 12 Sehr viel Wissen, für Menschen zugänglich.

13 13 Web Mining

14 14 Formen Knowledge discovery (aka Data mining): “the non-trivial process of identifying valid, novel, potentially useful, and ultimately understandable patterns in data.” 1 Web Mining: die Anwendung von Data-Mining- Techniken auf Inhalt, (Hyperlink-) Struktur und Nutzung von Webressourcen. Webmining-Gebiete: Web content mining Web structure mining Web usage mining 1 Fayyad, U.M., Piatetsky-Shapiro, G., Smyth, P., & Uthurusamy, R. (Eds.) (1996). Advances in Knowledge Discovery and Data Mining. Boston, MA: AAAI/MIT Press

15 15 Webmining-Gebiete: Web content mining Web structure mining Web usage mining Web Mining: Beispiele

16 16 Das Hauptproblem des Web Mining

17 17 Syntax in, Syntax out.

18 18

19 19 Semi-automatisches Tagging: Tag-Empfehlung auf Basis von Syntax + existierenden Labels

20 20 Tagyu funktioniert auch (mit Einschränkungen) für Ressourcen in anderen Sprachen

21 21 Funktioniert das wirklich? (1)

22 22 Funktioniert das wirklich? (2)

23 23 Das Wikipedia 300 Component Model, generiert mit diskreter PCA cosco.hiit.fi/search/H300.html/topic_list - common phrases of selected components 1. process; water; air; pressure; gas; body of water; natural gas; high pressure; hot water; fresh water; 2. Mark; Gospel; Matthew; Luke; Rose; Virgin; Virgin Mary; Gospel of John; Gospel of Mark; Gospel of Luke; 3. part; text; Britannica; entry; Encyclopedia Britannica; Encyclop~¦dia Britannica; Encyclopaedia Britannica; domain Encyclop~¦dia Britannica; public domain Encyclop~¦dia Britannica; public domain text; 4. property; theorem; elements; proof; subset; axioms; proposition; natural numbers; fundamental theorem; mathematical logic; 5. Dove; AMD; Dove Streptopelia; imperial crown; Imperial army; imperial court; imperial family; Collared Dove Streptopelia; Imperial Russia; 6. side; feet; long time; long period; right side; left side; long distances; different types; short distance; opposite side; 7. David; bill; Bob; Jim; Allen; Dave; Current stars; former members; Bill Clinton; former President; 8. magazine; newspaper; political parties; public domain text; public opinion; political career; public schools; own right; political life; public service; 9. way; things; boy; cat; long time; same way; same thing; only way; different ways; good thing; 10. problems; zero; sum; digits; ~~; natural numbers; positive integer; mathematical analysis; decimal digits; natural logarithm; 11. population density; couples; races; total area; makeup; Demographics; median age; income; density; housing units; 175. Torres; Iraqi KASUMI KHAZAD Khufu; Granada; Spa; Fra; General information; General Public License; General Bernardo; New Granada; Torres Strait; 176. love; Me; Rolling Stones; love songs; Rolling Stone magazine; Love Me; Fall in Love; Meet Me; love story; professional wrestler; Zusammenfassend – Schwächen rein statistischer Ansätze: Interpretation der Resultate? Existenz von Resultaten? Korrektheit? Inferenzen? Zusammenfassend – Schwächen rein statistischer Ansätze: Interpretation der Resultate? Existenz von Resultaten? Korrektheit? Inferenzen?

24 24 Semantic Web

25 25 Das Semantic Web “The Semantic Web is an extension of the current web in which information is given well-defined meaning, better enabling computers and people to work in co-operation.” 1 “The Semantic Web provides a common framework that allows data to be shared and reused across application, enterprise, and community boundaries. It is a collaborative effort led by W3C with participation from a large number of researchers and industrial partners. It is based on the Resource Description Framework (RDF), which integrates a variety of applications using XML for syntax and URIs for naming.” 2 1 Berners-Lee, T., Hendler, J., & Lassila, O. (2001). The Semantic Web. Sci. American, May Berners-Lee, T. (2000). Semantic Web XML2000.

26 26 Category structure: Top.... Arts... .... Category structure: Top.... Arts... .... Resources: ... Arts John phillips Blown glass A small display of glass by John Phillips Computers Resources: ... Arts John phillips Blown glass A small display of glass by John Phillips Computers Semantic Web: Beispiel

27 27 Warum Semantic Web? Bsp. strukturierte Suche – Metadaten gemäß Dublin Core (DC)

28 28 Semantische Suche: Bsp. 2 – Metadaten gemäß DC + Domänenontologie

29 29 Das Hauptproblem des Semantic Web

30 30 Wer soll das alles machen?

31 31 Der Ansatz

32 32 Web Mining: Maschinelles Lernen extrahiert aus Daten Wissen Das Semantic Web macht Wissen maschinen- verständlich Semantic Web Mining nutze Semantik zur Verbesserung v. Mining nutze Mining zur Generierung v. Semantik Semantic Web Mining nutze Semantik zur Verbesserung v. Mining nutze Mining zur Generierung v. Semantik

33 33

34 34 Web Mining Semantic Web Web Mining Semantic Web... p3ee24304.dip.t-dialin.net - - [19/Mar/2002:12:03: ]"GET /search.html?t=jane%20austen&SID= &ord=asc HTTP/1.0" p3ee24304.dip.t-dialin.net - - [19/Mar/2002:12:05: ] "GET /search.html?t=jane%20austen&m=vide o&SID=023785&ord=desc HTTP/1.0" p3ee24304.dip.t-dialin.net - - [19/Mar/2002:12:06: ] "GET /view.asp?id=3456&SID= HTTP/1.0" Ver- stehen

35 35 Web Mining Semantic Web Literaturverzeichnis [1] Agarwal, R.; Krueger, B. P.; Scholes, G. D.; Yang, M.; Yom, J.; Mets, L.; Fleming, G. R. U ltrafast energy transfer in LHC-II revealed by three-pulse photon echo peak shift measurements, J. Phys. Chem. B, 2000, 104, 2908,... beitragen

36 36 Web Mining Semantic Web ordnen und erklären beitragen

37 37 Agenda 1. Motivation und Überblick n Warum Web? Warum Blogs? n Semantic Web Mining 2. Finding your way through blogspace: Using semantics for cross-domain blog analysis

38 38 Context n Semi-automatic tagging n Blog recommendation n Semantics-enhanced text mining, word sense disambiguation n Exploratory analyses of blog contents n Computational Approaches to Analyzing Weblogs AAAI 2006 Spring Symposium n Read more in the paper:

39 39 Blog recommendation: collaborative + content-based filtering (www.iro.umontreal.ca/~aimeur/publications/Workshop20.pdf)

40 40 An example of exploratory blogs analysis (in which a syntax-based approach is sufficient): the run-up to the 2004 US presidential election ( Adamic & Glance, 2005)

41 41 Our procedure 1. Take a set of blog corpora (= collection of blogs manually labelled as belonging to one topic) 2. In all of the following analyses: l what is blog corpus about? l to which other blog corpora is it related, and why? 3. syntactic analysis: keyphrases 4. semantic analysis I: domain labels 5. semantic analysis II: structural semantic interconnections

42 42 Data

43 43 Sample data: 4 blog corpora n Food and drink n Health and medicine n Law n Weblogs about blogging n Randomly sampled from the Yahoo! blog directory, K words each n Available at

44 44 Syntactic analysis

45 45 What is a blog about? Term Extraction Domain relevance and domain consensus: Keyphrases: DR ≥ 0.35, DC ≥ 0.23 (values from previous experiments) t = term,  = corpus (here: blog corpus), b = a blog (here: as an element of a corpus  k )

46 46 What is shared by two blogs? Syntactic similarity: Jaccard coefficient T(C) = keyphrases / “terminology“ of corpus C

47 47 Semantic analysis I: WordNet and WordNet domains

48 48 WordNet

49 49 Hierarchical knowledge: Domain labels

50 50 Domain label statistics show that the blog corpora have clear thematic foci frequency of domain D in corpus C = no. of keyphrases in C with a sense that maps to D

51 51 Blog foci: Top 5 Domains FoodHealthLawMeta-blogs 1GastronomyMedicineLawTelecommunications 2AlimentationTime periodQualityTime period 3Quality PoliticsPerson 4BotanyBiologyAdministrationPublishing 5PersonPhysicsEconomy

52 52 Top-10 intersections n Law – meta-blogs l Law, politics, economy (+ 3 factotum) n Law – health l Law, psychology (+ 2 factotum) n Health – meta-blogs l Law (+ 2 factotum) n Food – law l Sociology (+ 2 factotum) n No overlap food – health, health – law

53 53 Semantic analysis II: Hierarchical and non-hierarchical knowledge: WordNet and SSI (Structural semantic interconnections)

54 54 The need for word sense disambiguation “She sat by the bank and looked sentimentally at the last fish.“ „She sat by the bank and looked sentimentally at the last coins.“ “She sat by the bank and looked sentimentally at the last coins.“

55 55 WordNet semantic relations

56 56 Structural semantic interconnections: bank – fish Details of SSI‘s enhanced lexcial database (extending WordNet) and of SSI‘s word sense disambiguation are described in R. Navigli & P. Velardi. Structural Semantic Interconnections: a knowledge-based approach to word sense disambiguation. IEEE Transactions on Pattern Analysis and Machine Intelligence (27-7), July, 2005.

57 57 Structural semantic interconnections: bank – coin

58 58 Knowledge-based similarity between blogs Example: n connection between two terms from the domain computer science n path weights: 0.33; 0.25; 0.25 = 1 / path length in no. of edges) Procedure: For each blog pair 1. find all SSI paths between all pairs of a term (keyphrase) from blog 1 and a term from blog 2 (in all conditions but the baseline: choose only terms that map to senses in the top domain(s), and choose only those senses) 2. Measure of blog pair similarity = sum over the weights of all these paths

59 59 Experi- mental settings

60 60 Results (Quantitative view)

61 61 Results: Qualitative view n Baseline: Spurious connections between law – metablogs: via computer science terms  filtered out in domain-label conditions n Correct connections throughout: Food – health: greasy food (cream cheese, chocolate sauce,...) – other fats, or health food n 1/3-relatedness reveals important connections: l Expected: law – metablogs: enterprise (related to law) – computer science (related to telecommunications), publishing, politics: law firms, news organizations, news story, political party l Unexpected: law – food: local government – town planning (including parking lots, the main drag) n Single-term expressions particularly visible in food – health (eggs, onions,... – health food; disease – beef)  lexicalization effect, depends on domains (also related domains in law – metablogs) n 3-relatedness: topic drift, many highly generic single-word terms (activity, life, computer, area, food) establish many generic paths to a 2nd corpus (these terms are „related to“ nearly everything else)  topic drift

62 62 Restricting path grammar to find valid interconnections n Starting from 3-relatedness n ≤ 1 related-to link  filters out 88.8% of the paths n ≤ 2 types of links  filters out 53.4% of the path n Results: l Mostly, “meaningful“ paths were retained. l But further research is needed.

63 63 Questions / future work n Evaluation l Standard datasets („senseval for blogs“): try the following ?! –http://www.blogpulse.com/www2006-workshop/http://www.blogpulse.com/www2006-workshop/ –10 M posts from 1 M weblogs from three weeks in July –This data set has been selected as it spans a period of time during which an event of global significance occurred, namely the London bombings. l Compare syntax- and semantics-based approaches –Assuming that the semi-automatic approaches of Semantic Web Mining give qualitatively better results: How can the quality gains be weigthed against the additional costs of manual post-processing? n Improve path grammars n Ontology learning

64 64 … für Ihre Aufmerksamkeit! Danke …


Herunterladen ppt "1 1 Bettina Berendt Humboldt-Universität zu Berlin – www.berendt.de * mit vielen Ko-AutorInnen ** mit Roberto Navigli, Università “La Sapienza”, Roma,"

Ähnliche Präsentationen


Google-Anzeigen