Difference between revisions of "Wikipedia Category Graph"
JacopoFarina (Talk | contribs) m (→Results of the analysis) |
JacopoFarina (Talk | contribs) m (WikipediaCategoryGraph moved to Wikipedia Category Graph: most of other pages use this naming convention) |
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{{Project | {{Project | ||
| − | |title=Wikipedia | + | |title=Automatically assigning Wikipedia articles to macrocategories |
|short_descr=Represent Wikipedia Categories with a model based on graphs to further analyze it. | |short_descr=Represent Wikipedia Categories with a model based on graphs to further analyze it. | ||
|coordinator=MarcoColombetti | |coordinator=MarcoColombetti | ||
| Line 6: | Line 6: | ||
|students=JacopoFarina; | |students=JacopoFarina; | ||
|resarea=Social Software and Semantic Web | |resarea=Social Software and Semantic Web | ||
| − | |restopic=Graph Mining and Analysis | + | |restopic=Graph Mining and Analysis; Semantic Tagging; |
|start=2010/06/10 | |start=2010/06/10 | ||
|end=2010/10/01 | |end=2010/10/01 | ||
| − | |status= | + | |status=Closed |
|level=Bs | |level=Bs | ||
| − | |type= | + | |type=Thesis |
}} | }} | ||
| − | The goal of the project is to analyze Wikipedia categories by representing them in a graph based database. | + | The goal of the project is to analyze Wikipedia categories by representing them in a graph based database to further extract informations mainly about assigning macrocategories to articles. |
| − | Wikipedia categories are not a | + | Wikipedia categories are not a tree-based structure: a category may be contained in another one which is contained in another one which is contained in the first one, generating a cyclic reference, a category can contain itself and many categories may be a root category (non contained in others). |
| − | For | + | For these reasons a graph database is better to represent the structure. |
==Creation and further analysis of the database with igraph== | ==Creation and further analysis of the database with igraph== | ||
| − | Wikipedia | + | Wikipedia lets users download the entire site database (with all versions of all articles) or just some selections of it. |
| − | We use a selection which contains the category | + | We use a selection which contains the category structure and article memberships in them. |
| Line 28: | Line 28: | ||
| − | After transferring the structure in a Neo4j graph is possible to create from it a Pajek file (.net) to make general analysis like described [[Social_Network_Analysis_With_Igraph_Package_Using_R|here]]. | + | After transferring the structure in a Neo4j graph it is possible to create from it a Pajek file (.net) to make general analysis like described [[Social_Network_Analysis_With_Igraph_Package_Using_R|here]]. |
==Results of the analysis== | ==Results of the analysis== | ||
| Line 48: | Line 48: | ||
By applying the Tarjan's strongly connected components algorithm to the graph is possible to found 93 structures of up to 2 nodes. Each of them contains at least one cycle. Most of them are composed of two categories about the same thing, like ''History of the Germanic peoples'' and ''Ancient Germanic peoples'', but there are also more curious cases like this one | By applying the Tarjan's strongly connected components algorithm to the graph is possible to found 93 structures of up to 2 nodes. Each of them contains at least one cycle. Most of them are composed of two categories about the same thing, like ''History of the Germanic peoples'' and ''Ancient Germanic peoples'', but there are also more curious cases like this one | ||
| − | [[Image:Struttura fortemente connessa wikipedia.png]] | + | [[Image:Struttura fortemente connessa wikipedia.png|400px]] |
=== Tested algorithms === | === Tested algorithms === | ||
We tried 9 algorithms to choose the category which fit best an article. After confronting the results of the automatic procedure with human made assignments, the best algorithm was choose a different weight to each edge by the traversal direction. | We tried 9 algorithms to choose the category which fit best an article. After confronting the results of the automatic procedure with human made assignments, the best algorithm was choose a different weight to each edge by the traversal direction. | ||
Sizes of the macrocategories determined this way are: | Sizes of the macrocategories determined this way are: | ||
| − | [[Image:Dimensioni macrocategorie costi.png]] | + | [[Image:Dimensioni macrocategorie costi.png|400px]] |
==Download== | ==Download== | ||
*The relation about the project (in Italian) can be found [[Media:Relazione progetto WikipediaCatGraph.pdf|here]] | *The relation about the project (in Italian) can be found [[Media:Relazione progetto WikipediaCatGraph.pdf|here]] | ||
*Sources in Java can be found [[Media:Wikipedia category Graph-sources.zip|here]] | *Sources in Java can be found [[Media:Wikipedia category Graph-sources.zip|here]] | ||
| + | *Slides of the thesis (in Italian) [[Media:Presentazione wikipedia category graph.pdf|here]] | ||
| + | *The thesis (in Italian) [[Media:Tesi wikipedia category graph.pdf|here]] | ||
==Previous Work== | ==Previous Work== | ||
| − | [http://www-users.cs.umn.edu/~echi/papers/2009-CHI2009/p1509.pdf What's in Wikipedia? Mapping Topics and Conflict Using Socially] | + | *[http://www-users.cs.umn.edu/~echi/papers/2009-CHI2009/p1509.pdf What's in Wikipedia? Mapping Topics and Conflict Using Socially] |
Latest revision as of 14:05, 31 August 2011
Automatically assigning Wikipedia articles to macrocategories
| |
| Short Description: | Represent Wikipedia Categories with a model based on graphs to further analyze it. |
| Coordinator: | MarcoColombetti (colombet@elet.polimi.it) |
| Tutor: | DavidLaniado (david.laniado@gmail.com), RiccardoTasso (tasso@elet.polimi.it) |
| Collaborator: | |
| Students: | JacopoFarina (jacopo1.farina@mail.polimi.it) |
| Research Area: | Social Software and Semantic Web |
| Research Topic: | Graph Mining and Analysis, Semantic Tagging |
| Start: | 2010/06/10 |
| End: | 2010/10/01 |
| Status: | Closed |
| Level: | Bs |
| Type: | Thesis |
The goal of the project is to analyze Wikipedia categories by representing them in a graph based database to further extract informations mainly about assigning macrocategories to articles.
Wikipedia categories are not a tree-based structure: a category may be contained in another one which is contained in another one which is contained in the first one, generating a cyclic reference, a category can contain itself and many categories may be a root category (non contained in others).
For these reasons a graph database is better to represent the structure.
Contents
Creation and further analysis of the database with igraph
Wikipedia lets users download the entire site database (with all versions of all articles) or just some selections of it. We use a selection which contains the category structure and article memberships in them.
Neo4j is a graph-based database, which allow a program to create and manipulate graph structures like nodes and relationships.
In order to transfer the database in neo4j format is better save it in a file, which will be read one line at time.
After transferring the structure in a Neo4j graph it is possible to create from it a Pajek file (.net) to make general analysis like described here.
Results of the analysis
We can use the tools described here
The diameter of the graph is 32. This is the maximum distance (number of nodes in the minimum path) between two nodes. These two nodes are Prehistoric life sorted by geography (A category about prehistoric animals without articles) and BMW M20 (a car).
The average distance between two nodes is 5.5568.
The graph density is 6.28*10-7. This is the ratio of the number of edges and the number of possible edges.
By analysing the graph with little Java programs written for this task we calculated the average number of categories per article is 2.68.
Statistically, the 93% of articles has less than 7 categories, 64% less than 3.
The article with more categories is Winston Churchill, with 70 categories. Albert Einstein has the notable number of 56 categories, too.
Strongly connected components
By applying the Tarjan's strongly connected components algorithm to the graph is possible to found 93 structures of up to 2 nodes. Each of them contains at least one cycle. Most of them are composed of two categories about the same thing, like History of the Germanic peoples and Ancient Germanic peoples, but there are also more curious cases like this one
Tested algorithms
We tried 9 algorithms to choose the category which fit best an article. After confronting the results of the automatic procedure with human made assignments, the best algorithm was choose a different weight to each edge by the traversal direction.
Sizes of the macrocategories determined this way are:
Download
- The relation about the project (in Italian) can be found here
- Sources in Java can be found here
- Slides of the thesis (in Italian) here
- The thesis (in Italian) here
