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Entity ranking using Wikipedia as a pivot
2010
Proceedings of the 19th ACM international conference on Information and knowledge management - CIKM '10
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Wikipedia allows us to properly identify entities and some of their characteristics, and Wikipedia's elaborate category structure allows us to get a handle on the entity's type. ...
Both Wikipedia's external links and the enriched Wikipedia entities with additional links to homepages are significantly better at finding primary web homepages than anchor text retrieval, which in turn ...
Acknowledgments The created Entity Ranking topics test collection is available at http://staff.science.uva.nl/~kamps/effort/data. ...
doi:10.1145/1871437.1871451
dblp:conf/cikm/KapteinSVK10
fatcat:qlvvcmy6wbbspl322ss55yep6y
Using Anchor Text, Spam Filtering and Wikipedia for Web Search and Entity Ranking
2010
Text Retrieval Conference
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For the Entity Ranking Track, we use Wikipedia as a pivot to find relevant entities on the Web. Using category information to retrieve entities within Wikipedia leads to large improvements. ...
Following the external links on Wikipedia pages to find the homepages of the entities in the ClueWeb collection, works better than searching an anchor text index, and combining the external links with ...
When external links are used to find homepages, all Wikipedia results without external links to a page in the ClueWeb Category B collection are excluded from the ranking. ...
dblp:conf/trec/KampsKK10
fatcat:l3viyc7chbexvaggt4y3kqrcay
Result Diversity and Entity Ranking Experiments: Anchors, Links, Text and Wikipedia
2009
Text Retrieval Conference
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Although the links between the Wikipedia entities and pages in the Clueweb collection are sparse, the precision of the existing links is very high. ...
Using Wikipedia as a pivot results in large gains of P10 and NDCG when only primary pages are considered. ...
Acknowledgments This research was supported by the Netherlands Organization for Scientific Research (NWO, grant # 612.066.513, 639.072.601, and 640.001.501). ...
dblp:conf/trec/KapteinKK09
fatcat:viwwrzxdujfh5cznzq5vqrw6de
Delft University at the TREC 2009 Entity Track: Ranking Wikipedia Entities
2009
Text Retrieval Conference
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This paper describes the details of our participation in Entity track of the TREC 2009. ...
The usefulness of Wikipedia for finding representative keywords and named entities on web pages is demonstrated in several publications. ...
Using Wikipedia to find named entities There are several ways to deal with Wikipedia corpus in order to find relevant named entities: simply rank Wikipedia pages, rank Wikipedia pages whose names appear ...
dblp:conf/trec/SerdyukovV09
fatcat:mbkxhsgsyrdk3o46xx6khkv22i
Extracting Named Entities and Synonyms from Wikipedia
2010
2010 24th IEEE International Conference on Advanced Information Networking and Applications
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In this paper we describe how to use Wikipedia contents to automatically generate a dictionary of named entities and synonyms that are all referring to the same entity. ...
Through an experimental evaluation we show that with our approach, we can find named entities and their synonyms with a high degree of accuracy. ...
Acknowledgments The authors would like to thank Jon Atle Gulla for helpful feedback in the initial phase of this work, and George Tsatsaronis and Robert Neumayer for valuable help in improving the paper ...
doi:10.1109/aina.2010.50
dblp:conf/aina/BohnN10
fatcat:m3yqqmj5unac7bjnw3ib5abcsi
Classifying Image Galleries into a Taxonomy Using Metadata and Wikipedia
[chapter]
2012
Lecture Notes in Computer Science
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The proposed method links textual gallery metadata to Wikipedia pages and categories. ...
Entity extraction from metadata, entity ranking, and selection of categories is based on Wikipedia and does not require labeled training data. ...
Find and score entity categories In this step, we try to find taxonomy categories for each entity by using its Wikipedia article. ...
doi:10.1007/978-3-642-31178-9_20
fatcat:gnfcvwnivrcttnym7ywojuwige
Are Human-Input Seeds Good Enough for Entity Set Expansion? Seeds Rewriting by Leveraging Wikipedia Semantic Knowledge
[chapter]
2012
Lecture Notes in Computer Science
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In our method, we leverage Wikipedia as a semantic knowledge to measure semantic relatedness and ambiguity of each seed. ...
In this paper, we propose a novel method which can generate new, high-quality seeds and replace original, poor-quality ones. ...
Are Human-Input Seeds Good Enough for Entity Set Expansion? ...
doi:10.1007/978-3-642-35341-3_9
fatcat:u4vvbxin5jgt3bcr7sitbg3f3u
Automatic Classification and Relationship Extraction for Multi-Lingual and Multi-Granular Events from Wikipedia
2012
International Semantic Web Conference
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As only a small amount of events is available in structured form in DBpedia, we extract these events with a rule-based approach from Wikipedia pages. ...
In this paper we focus on three aspects: (1) extending our prior method for extracting events for a daily granularity, (2) the automatic classification of events and (3) finding relationships between events ...
A part of these events includes categories which can be used to automatically build categories for about 70% of another language set on the basis of links to other Wikipedia/DBpedia entities. ...
dblp:conf/semweb/HienertWP12
fatcat:7cqzepds6vetfljep4u7zvhfya
Focused Search in Books and Wikipedia: Categories, Links and Relevance Feedback
[chapter]
2010
Lecture Notes in Computer Science
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Our findings for the Entity Ranking Track are in direct opposition of our Ad Hoc findings, namely, that the WordNet categories are more effective than the Wikipedia categories. ...
In the Ad Hoc track we investigate focused link evidence, using only links from retrieved sections. ...
In Section 5, we present our approach to the Entity Ranking Track. Finally, in Section 6, we discuss our findings and draw preliminary conclusions. ...
doi:10.1007/978-3-642-14556-8_28
fatcat:jjnb534ljzh5xnpi22rct53mti
Algorithms for Recollection of Search Terms Based on the Wikipedia Category Structure
2014
The Scientific World Journal
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In all our experiments, it takes 1 query on a category and on average 2.49 clicks, compared to 5.68 queries on the database's traditional text search engine for a 68.3% success probability or 6.01 queries ...
This facilitates browsing and offers the users the possibility to look for named entities, even if they forgot their names. ...
[17] use categories and the locality of links in Wikipedia for entity ranking. Hahn et al. ...
doi:10.1155/2014/454868
pmid:24616630
pmcid:PMC3925568
fatcat:l4orpgkd55fdlcaxcp4pnmept4
Choosing Better Seeds for Entity Set Expansion by Leveraging Wikipedia Semantic Knowledge
[chapter]
2012
Communications in Computer and Information Science
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In our method, we leverage Wikipedia semantic knowledge to measure semantic relatedness and ambiguity of each seed. ...
Entity Set Expansion, which refers to expanding a human-input seed set to a more complete set which belongs to the same semantic category, is an important task for open information extraction. ...
For future work, we plan to use other semantic knowledge provided by Wikipedia like category hierarchy to help finding better seeds. ...
doi:10.1007/978-3-642-33506-8_80
fatcat:j4crs6kt6jdu7bsyupud6csvwm
ReFER: Effective Relevance Feedback for Entity Ranking
[chapter]
2011
Lecture Notes in Computer Science
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The most challenging general problem is to find relevant entities, of the correct type and characteristics, based on a free-text query that need not conform to any single ontology or category structure ...
It employs the Wikipedia category structure, but augments that structure with 'smooth categories' to deal with the sparseness of the raw category information. ...
We conclude that the proposed approach can be easily applied to any ER system in order to improve search effectiveness, and that the model performs well on the test collection we used. ...
doi:10.1007/978-3-642-20161-5_26
fatcat:zy3gshhtx5hkzif4y4ivbeivni
The Links Have It: Infobox Generation by Summarization over Linked Entities
[article]
2014
arXiv
pre-print
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In this paper, instead of performing information extraction over unstructured natural language text directly, we focus on a rich set of semi-structured data in Wikipedia articles: linked entities. ...
The idea of this paper is the following: If we can summarize the relationship between the entity and its linked entities, we immediately harvest some of the most important information about the entity. ...
And different from above structural knowledge extraction methods, we use the structured information(linked entities, and categories) only in Wikipedia to extract knowledge(infobox). ...
arXiv:1406.6449v1
fatcat:rxetvzhyqvf6dhyhts2imnuefq
Use of Wikipedia Categories in Entity Ranking
[article]
2007
arXiv
pre-print
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In particular, we explore how to make use of Wikipedia categories to improve entity ranking effectiveness. ...
Wikipedia is a useful source of knowledge that has many applications in language processing and knowledge representation. ...
Acknowledgements Part of this work was completed while James Thom was visiting INRIA in 2007. ...
arXiv:0711.2917v1
fatcat:t4grxc2jwzbrxk3hpm6bedamnm
Named Entity Linking Based On Wikipedia
2014
International Journal of Database Theory and Application
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In this paper, we present the ideas and methodologies on labeling the mentioned entities with the wiki dataset. ...
We focus on maximizing the similarity between the contextual information extracted from Wikipedia and the context of a document, as well as the similarity among the category tags associated with the candidate ...
Acknowledgements This work is supported by the National Science Foundation of China (no. 71273010 and 61379037), and the National Science Foundation of Anhui Province (no. 1208085MG117). ...
doi:10.14257/ijdta.2014.7.1.01
fatcat:trbbl2yni5ddnab77xgccmdovm
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