1 1 Bettina Berendt Humboldt-Universität zu Berlin – * mit vielen Ko-AutorInnen ** mit Roberto...

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Bettina Berendt

Humboldt-Universität zu Berlin – www.berendt.de

* mit vielen Ko-AutorInnen

** mit Roberto Navigli, Università “La Sapienza”, Roma, Italy

Semantic Web Mining*

Heute:Semantik für und aus Blogs**

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Agenda

1. Motivation und Überblick

Warum Web? Warum Blogs?

Semantic Web Mining

2. Finding your way through blogspace:

Using semantics for cross-domain blog analysis

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Agenda

1. Motivation und Überblick

Warum Web? Warum Blogs?

Semantic Web Mining

2. Finding your way through blogspace:

Using semantics for cross-domain blog analysis

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Das Ziel

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Das Wissen der Menschheit möglichst vielen Menschen effektiv zugänglich machen.

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6“Makrokosmos World Wide Web”

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“Mikrokosmos Blogosphere”

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Konkrete Ziele(Bsp. für Teil 2 dieses Vortrags)

Klassifikation:„Dieser Blog behandeltInhalte aus Ernährungund Gastronomie.“ Vorschläge von Meta-Tags für den Blog Unterstützung vonBlog-Suchmaschinen

Klassifikation:„Dieser Blog behandeltInhalte aus Ernährungund Gastronomie.“ Vorschläge von Meta-Tags für den Blog Unterstützung vonBlog-Suchmaschinen

Empfehlungenmit Erklärung:„Wenn Sie diesen Bloginteressant fanden,dann wird Sie vielleichtauch Blog ... interessieren,und zwar weil ...“

Empfehlungenmit Erklärung:„Wenn Sie diesen Bloginteressant fanden,dann wird Sie vielleichtauch Blog ... interessieren,und zwar weil ...“

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Das Potenzial

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Sehr viel Wissen, für Menschen zugänglich.

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Die Probleme

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Sehr viel Wissen, für Menschen zugänglich.

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Web Mining

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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

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Webmining-Gebiete:

Web content mining

Web structure mining

Web usage mining

Web Mining:Beispiele

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Das Hauptproblem des Web Mining

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Syntax in, Syntax out.

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

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

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Funktioniert das wirklich? (1)

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Funktioniert das wirklich? (2)

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23Das 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?

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Semantic Web

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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.

2 http://www.w3.org/2001/sw/3 Berners-Lee, T. (2000). Semantic Web XML2000.

www.w3.org/2000/Talks/1206-xml2k-tbl/

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26Category structure:<RDF xmlns:r="http://www.w3.org/TR/RDF/" xmlns:d="http://purl.org/dc/elements/1.0/" xmlns="http://directory.mozilla.org/rdf"><Topic r:id="Top"> <tag catid="1"/> <d:Title>Top</d:Title> <narrow r:resource="Top/Arts"/> ....</Topic><Topic r:id="Top/Arts"> <tag catid="2"/> <d:Title>Arts</d:Title> <narrow r:resource="Top/Arts/Books"/> ... <narrow r:resource="Top/Arts/Artists"/> <symbolic r:resource="Typography:Top/Computers/Fonts"/></Topic>....</RDF>

Category structure:<RDF xmlns:r="http://www.w3.org/TR/RDF/" xmlns:d="http://purl.org/dc/elements/1.0/" xmlns="http://directory.mozilla.org/rdf"><Topic r:id="Top"> <tag catid="1"/> <d:Title>Top</d:Title> <narrow r:resource="Top/Arts"/> ....</Topic><Topic r:id="Top/Arts"> <tag catid="2"/> <d:Title>Arts</d:Title> <narrow r:resource="Top/Arts/Books"/> ... <narrow r:resource="Top/Arts/Artists"/> <symbolic r:resource="Typography:Top/Computers/Fonts"/></Topic>....</RDF>

Resources:<RDF xmlns:r="http://www.w3.org/TR/RDF/" xmlns:d="http://purl.org/dc/elements/1.0/" xmlns="http://directory.mozilla.org/rdf"> ...<Topic r:id="Top/Arts"> <tag catid="2"/> <d:Title>Arts</d:Title> <link r:resource="http://www3...ca/…./file.html"/></Topic><ExternalPage about="http://www…ca/file .html"> <d:Title>John phillips Blown glass</d:Title> <d:Description>A small display of glass by John Phillips</d:Description></ExternalPage><Topic r:id="Top/Computers"> <tag catid="4"/> <d:Title>Computers</d:Title> <link r:resource="http://www.cs.tcd.ie/FME/"/> <link r:resource=”http://foo.asdfsa….."/></Topic></RDF>

Resources:<RDF xmlns:r="http://www.w3.org/TR/RDF/" xmlns:d="http://purl.org/dc/elements/1.0/" xmlns="http://directory.mozilla.org/rdf"> ...<Topic r:id="Top/Arts"> <tag catid="2"/> <d:Title>Arts</d:Title> <link r:resource="http://www3...ca/…./file.html"/></Topic><ExternalPage about="http://www…ca/file .html"> <d:Title>John phillips Blown glass</d:Title> <d:Description>A small display of glass by John Phillips</d:Description></ExternalPage><Topic r:id="Top/Computers"> <tag catid="4"/> <d:Title>Computers</d:Title> <link r:resource="http://www.cs.tcd.ie/FME/"/> <link r:resource=”http://foo.asdfsa….."/></Topic></RDF>

Semantic Web:Beispiel

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Warum Semantic Web?Bsp. strukturierte Suche

– Metadaten gemäß Dublin Core (DC)

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Semantische Suche: Bsp. 2 – Metadaten

gemäß DC + Domänenontologie

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Das Hauptproblem des Semantic Web

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Wer soll das alles machen?

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Der Ansatz

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Web Mining: Maschinelles Lernen extrahiert aus Daten Wissen

Das Semantic Webmacht 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

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Web Mining

Semantic Web

Web Mining

Semantic Web

...p3ee24304.dip.t-dialin.net - - [19/Mar/2002:12:03:51 +0100]"GET /search.html?t=jane%20austen&SID=023785&ord=asc HTTP/1.0" 200 1759 p3ee24304.dip.t-dialin.net - - [19/Mar/2002:12:05:06 +0100] "GET /search.html?t=jane%20austen&m=video&SID=023785&ord=desc HTTP/1.0" 200 8450p3ee24304.dip.t-dialin.net - - [19/Mar/2002:12:06:41 +0100] "GET /view.asp?id=3456&SID=023785 HTTP/1.0" 200 3478...

Ver-stehen

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Web Mining

Semantic Web

...<BIBLIOGRAPHY><FLOAT><PAGENUMBER>136</PAGENUMBER></FLOAT><HEAD>Literaturverzeichnis</HEAD>

<CITATION WORKTYPE="journal" PUBLISHED="PUBLISHED"><CUT ID="bib-15-">[1] </CUT><WORKAUTHOR>Agarwal, R.; Krueger, B. P.; Scholes, G. D.; Yang, M.; Yom, J.; Mets, L.; Fleming, G. R.</WORKAUTHOR>U<ARTICLETITLE>ltrafast energy transfer in LHC-II revealed by three-pulse photon echo peak shift measurements</ARTICLETITLE>, <WORKTITLE>J. Phys. Chem. B</WORKTITLE>, <PUBDATE>2000</PUBDATE>, <NUMBER>104</NUMBER>, <PAGES>2908</PAGES>, </CITATION>

...

beitragen

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Web Mining

Semantic Web

ordnen und

erklären

beitragen

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Agenda

1. Motivation und Überblick

Warum Web? Warum Blogs?

Semantic Web Mining

2. Finding your way through blogspace:

Using semantics for cross-domain blog analysis

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Context

Semi-automatic tagging

Blog recommendation

Semantics-enhanced text mining, word sense disambiguation

Exploratory analyses of blog contents

Computational Approaches to Analyzing Weblogs AAAI 2006 Spring Symposium

Read more in the paper:

http://www2.wiwi.hu-berlin.de/~berendt/Papers/SS0603BerendtB.pdf

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

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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)

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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:

what is blog corpus about?

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

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Data

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Sample data: 4 blog corpora

Food and drink

Health and medicine

Law

Weblogs about blogging

Randomly sampled from the Yahoo! blog directory, 140-330 K words each

Available at

http://www.wiwi.hu-berlin.de/˜berendt/Blogs/Sample20050917/

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Syntactic analysis

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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)

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What is shared by two blogs? Syntactic similarity: Jaccard coefficient

T(C) = keyphrases / “terminology“of corpus C

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Semantic analysis I:WordNet and WordNet domains

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WordNet

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Hierarchical knowledge: Domain labels

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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

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Blog foci: Top 5 Domains

Food Health Law Meta-blogs

1 Gastronomy Medicine Law Telecommunications

2 Alimentation Time period Quality Time period

3 Quality Quality Politics Person

4 Botany Biology Administration Publishing

5 Person Physics Economy Economy

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Top-10 intersections

Law – meta-blogs

Law, politics, economy (+ 3 factotum)

Law – health

Law, psychology (+ 2 factotum)

Health – meta-blogs

Law (+ 2 factotum)

Food – law

Sociology (+ 2 factotum)

No overlap food – health, health – law

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

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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.“

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WordNet semantic relations

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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.

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Structural semantic interconnections: bank – coin

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Knowledge-based similarity between blogs

Example:

connection between two terms from the domain computer science

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

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Experi-mental settings

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Results (Quantitative view)

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Results: Qualitative view

Baseline: Spurious connections between law – metablogs: via computer science terms filtered out in domain-label conditions

Correct connections throughout: Food – health: greasy food (cream cheese, chocolate sauce, ...) – other fats, or health food

1/3-relatedness reveals important connections: Expected: law – metablogs: enterprise (related to law) – computer science

(related to telecommunications), publishing, politics: law firms, news organizations, news story, political party

Unexpected: law – food: local government – town planning (including parking lots, the main drag)

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)

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

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Restricting path grammar to find valid interconnections

Starting from 3-relatedness

≤ 1 related-to link filters out 88.8% of the paths

≤ 2 types of links filters out 53.4% of the path

Results:

Mostly, “meaningful“ paths were retained.

But further research is needed.

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Questions / future work

Evaluation Standard datasets („senseval for blogs“): try the following ?!

– http://www.blogpulse.com/www2006-workshop/

– 10 M posts from 1 M weblogs from three weeks in July 2005.

– 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.

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?

Improve path grammars

Ontology learning

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… für Ihre Aufmerksamkeit!

Danke …