Maria Grineva

From WikiPapers
Jump to: navigation, search

Maria Grineva is an author.

Publications

Only those publications related to wikis are shown here.
Title Keyword(s) Published in Language DateThis property is a special property in this wiki. Abstract R C
Blognoon: Exploring a topic in the blogosphere Blogs
Concept search
Semantic search
Wikipedia
Proceedings of the 20th International Conference Companion on World Wide Web, WWW 2011 English 2011 We demonstrate Blognoon, a semantic blog search engine with the focus on topic exploration and navigation. Blognoon provides concept search instead of traditional keywords search and improves ranking by identifying main topics of posts. It enhances navigation over the Blogosphere with faceted interfaces and recommendations. 0 0
Analysis of community structure in Wikipedia English 2009 We present the results of a community detection analysis of the Wikipedia graph. Distinct communities in Wikipedia contain semantically closely related articles. The central topic of a community can be identified using PageRank. Extracted communities can be organized hierarchically similar to manually created Wikipedia category structure. 0 0
Analysis of community structure in Wikipedia (poster) Community detection
Graph analysis
Wikipedia
WWW'09 - Proceedings of the 18th International World Wide Web Conference English 2009 We present the results of a community detection analysis of the Wikipedia graph. Distinct communities in Wikipedia contain semantically closely related articles. The central topic of a community can be identified using PageRank. Extracted communities can be organized hierarchically similar to manually created Wikipedia category structure. Copyright is held by the author/owner(s). 0 0
Effective extraction of thematically grouped key terms from text AAAI Spring Symposium - Technical Report English 2009 We present a novel method for extraction of key terms from text documents. The important and novel feature of our method is that it produces groups of key terms, while each group contains key terms semantically related to one of the main themes of the document. Our method bases on a com-bination of the following two techniques: Wikipedia-based semantic relatedness measure of terms and algorithm for detecting community structure of a network. One of the advantages of our method is that it does not require any training, as it works upon the Wikipedia knowledge base. Our experimental evaluation using human judgments shows that our method produces key terms with high precision and recall. 0 0
Extracting Key Terms From Noisy and Multitheme Documents Semantic relatedness
Contextual advertising
Information retrieval
WWW2009: 18th International World Wide Web Conference 2009 We present a novel method for key term extraction from text documents. In our method, document is modeled as a graph of semantic relationships between terms of that document. We exploit the following remarkable feature of the graph: the terms related to the main topics of the document tend to bunch up into densely interconnected subgraphs or communities, while non-important terms fall into weakly interconnected communities, or even become isolated vertices. We apply graph community detection techniques to partition the graph into thematically cohesive groups of terms. We introduce a criterion function to select groups that contain key terms discarding groups with unimportant terms. To weight terms and determine semantic relatedness between them we exploit information extracted from Wikipedia. Using such an approach gives us the following two advantages. First, it allows effectively processing multi-theme documents. Second, it is good at filtering out noise information in the document, such as, for example, navigational bars or headers in web pages. Evaluations of the method show that it outperforms existing methods producing key terms with higher precision and recall. Additional experiments on web pages prove that our method appears to be substantially more effective on noisy and multi-theme documents than existing methods. 0 0
Extracting key terms from noisy and multi-theme documents Community detection
Graph analysis
Keywords extraction
Semantic similarity
Wikipedia
WWW'09 - Proceedings of the 18th International World Wide Web Conference English 2009 We present a novel method for key term extraction from text documents. In our method, document is modeled as a graph of semantic relationships between terms of that document. We exploit the following remarkable feature of the graph: the terms related to the main topics of the document tend to bunch up into densely interconnected sub-graphs or communities, while non-important terms fall into weakly interconnected communities, or even become isolated vertices. We apply graph community detection techniques to partition the graph into thematically cohesive groups of terms. We introduce a criterion function to select groups that contain key terms discarding groups with unimportant terms. To weight terms and determine semantic relatedness between them we exploit information extracted from Wikipedia. Using such an approach gives us the following two advantages. First, it allows effectively processing multi-theme documents. Second, it is good at filtering out noise information in the document, such as, for example, navigational bars or headers in web pages. Evaluations of the method show that it outperforms existing methods producing key terms with higher precision and recall. Additional experiments on web pages prove that our method appears to be substantially more effective on noisy and multi-theme documents than existing methods. Copyright is held by the International World Wide Web Conference Committee (IW3C2). 0 0