Wikipedia is a useful knowledge source that benefits many applications in language processing and knowledge representation. An important feature of Wikipedia is that of categories. Wikipedia pages are assigned different categories according to their contents as human-annotated labels which can be used in information retrieval, ad hoc search improvements, entity ranking and tag recommendations. However, important pages are usually assigned too many categories, which makes it difficult to recognize the most important ones that give the best descriptions. In this paper, we propose an approach to recognize the most descriptive Wikipedia categories. We observe that historical figures in a precise category presumably are mutually similar and such categorical coherence could be evaluated via texts or Wikipedia links of corresponding members in the category. We rank descriptive level of Wikipedia categories according to their coherence and our ranking yield an overall agreement of 88.27 compared with human wisdom.
Entity ranking gains popularity since better rankings boost the performances of search engines, resulting in faster and more precise information retrieval. Wikipedia seems to be a good playground. The problem of ranking web pages could be easily reduced to Wikipedia entity ranking, plus that Wikipedia has a large collection of entities of different types @cite_12 and Wikipedia contains valuable texts, human annotated tags, enriched links and a great structure to analyze ranking effectiveness. Certain ranking can serve as a pivot for extensibility or analysis @cite_5 @cite_2 or be used to answer queries in named entity recognition @cite_8 . Additionally, retrieving real-life knowledge of reputations, fames and historical significance from entity ranking is also valuable @cite_13 .
@cite_8 @cite_2 @cite_5 @cite_13 @cite_12
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In this paper we investigate the task of Entity Ranking on the Web. Searchers looking for entities are arguably better served by presenting a ranked list of entities directly, rather than a list of web pages with relevant but also potentially redundant information about these entities. Since entities are represented by their web homepages, a naive approach to entity ranking is to use standard text retrieval. Our experimental results clearly demonstrate that text retrieval is effective at finding relevant pages, but performs poorly at finding entities. Our proposal is to use Wikipedia as a pivot for finding entities on the Web, allowing us to reduce the hard web entity ranking problem to easier problem of Wikipedia entity ranking. 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. Our main findings are the following. Our first finding is that, in principle, the problem of web entity ranking can be reduced to Wikipedia entity ranking. We found that the majority of entity ranking topics in our test collections can be answered using Wikipedia, and that with high precision relevant web entities corresponding to the Wikipedia entities can be found using Wikipedia's 'external links'. Our second finding is that we can exploit the structure of Wikipedia to improve entity ranking effectiveness. Entity types are valuable retrieval cues in Wikipedia. Automatically assigned entity types are effective, and almost as good as manually assigned types. Our third finding is that web entity retrieval can be significantly improved by using Wikipedia as a pivot. 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 significantly improved over standard text retrieval. We discuss the problem of ranking very many entities of different types. In particular we deal with a heterogeneous set of types, some being very generic and some very specific. We discuss two approaches for this problem: i) exploiting the entity containment graph and ii) using a Web search engine to compute entity relevance. We evaluate these approaches on the real task of ranking Wikipedia entities typed with a state-of-the-art named-entity tagger. Results show that both approaches can greatly increase the performance of methods based only on passage retrieval. Entity ranking has become increasingly important, both for retrieving structured entities and for use in general web search applications. The most common format for linked data, RDF graphs, provide extensive semantic structure via predicate links. While the semantic information is potentially valuable for effective search, the resulting adjacency matrices are often sparse, which introduces challenges for representation and ranking. In this paper, we propose a principled and scalable approach for integrating of latent semantic information into a learning-to-rank model, by combining compact representation of semantic similarity, achieved by using a modified algorithm for tensor factorization, with explicit entity information. Our experiments show that the resulting ranking model scales well to the graphs with millions of entities, and outperforms the state-of-the-art baseline on realistic Yahoo! SemSearch Challenge data sets. Statistical named entity recognisers require costly hand-labelled training data and, as a result, most existing corpora are small. We exploit Wikipedia to create a massive corpus of named entity annotated text. We transform Wikipedia’s links into named entity annotations by classifying the target articles into common entity types (e.g. person, organisation and location). Comparing to MUC, CONLL and BBN corpora, Wikipedia generally performs better than other cross-corpus train test pairs.