The advances of the Linked Open Data LOD initiative are giving rise to a more structured Web of data. Indeed, a few datasets act as hubs e.g., DBpedia connecting many other datasets. They also made possible new Web services for entity detection inside plain text e.g.,i¾?DBpedia Spotlight, thus allowing for new applications that can benefit from a combination of the Web of documents and the Web of data. To ease the emergence of these new applications, we propose a query-biased algorithm LDRANK for the ranking of web of data resources with associated textual data. Our algorithm combines link analysis with dimensionality reduction. We use crowdsourcing for building a publicly available and reusable dataset for the evaluation of query-biased ranking of Web of data resources detected in Web pages. We show that, on this dataset, LDRANK outperforms the state of the art. Finally, we use this algorithm for the construction of semantic snippets of which we evaluate the usefulness with a crowdsourcing-based approach.
Moreover, a study made in 2012 @cite_22 on the over 40 million websites of the Common Crawl corpus http: commoncrawl.org shows that 5.64 However, most of the existing approaches that can be used to rank the resources of graphs coming from the Web of data are not well adapted to this task. Thus, OntologyRank @cite_24 (used by Swoogle) introduces a modified version of PageRank with a teleportation matrix that takes into account the types of the links between ontologies. Similarly, PopRank @cite_1 offers a modified PageRank that considers the different types of predicates between resources. RareRank @cite_9 introduces a modified PageRank with a teleportation matrix that takes into account topical relationships between resources as available from ontologies. The approach introduced in @cite_18 modifies the teleportation matrix by taking into account the ranking of the Web pages within which the resources were discovered. Since this approach can be applied to our context, we include it to our evaluations (see section ). Finally, TRank @cite_15 addresses the task of ranking entity types given an initial entity and its textual context.
@cite_18 @cite_22 @cite_9 @cite_1 @cite_24 @cite_15
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With the success of Open Data a huge amount of tabular data sources became available that could potentially be mapped and linked into the Web of (Linked) Data. Most existing approaches to “semantically label” such tabular data rely on mappings of textual information to classes, properties, or instances in RDF knowledge bases in order to link – and eventually transform – tabular data into RDF. However, as we will illustrate, Open Data tables typically contain a large portion of numerical columns and or non-textual headers; therefore solutions that solely focus on textual “cues” are only partially applicable for mapping such data sources. We propose an approach to find and rank candidates of semantic labels and context descriptions for a given bag of numerical values. To this end, we apply a hierarchical clustering over information taken from DBpedia to build a background knowledge graph of possible “semantic contexts” for bags of numerical values, over which we perform a nearest neighbour search to rank the most likely candidates. Our evaluation shows that our approach can assign fine-grained semantic labels, when there is enough supporting evidence in the background knowledge graph. In other cases, our approach can nevertheless assign high level contexts to the data, which could potentially be used in combination with other approaches to narrow down the search space of possible labels. We introduce a stochastic graph-based method for computing relative importance of textual units for Natural Language Processing. We test the technique on the problem of Text Summarization (TS). Extractive TS relies on the concept of sentence salience to identify the most important sentences in a document or set of documents. Salience is typically defined in terms of the presence of particular important words or in terms of similarity to a centroid pseudo-sentence. We consider a new approach, LexRank, for computing sentence importance based on the concept of eigenvector centrality in a graph representation of sentences. In this model, a connectivity matrix based on intra-sentence cosine similarity is used as the adjacency matrix of the graph representation of sentences. Our system, based on LexRank ranked in first place in more than one task in the recent DUC 2004 evaluation. In this paper we present a detailed analysis of our approach and apply it to a larger data set including data from earlier DUC evaluations. We discuss several methods to compute centrality using the similarity graph. The results show that degree-based methods (including LexRank) outperform both centroid-based methods and other systems participating in DUC in most of the cases. Furthermore, the LexRank with threshold method outperforms the other degree-based techniques including continuous LexRank. We also show that our approach is quite insensitive to the noise in the data that may result from an imperfect topical clustering of documents. Abstract Today, when searching for information on the World Wide Web, one usually performs a query through a term-based search engine. These engines return, as the query's result, a list of Web sites whose contents match the query. For broad topic queries, such searches often result in a huge set of retrieved documents, many of which are irrelevant to the user. However, much information is contained in the link-structure of the World Wide Web. Information such as which pages are linked to others can be used to augment search algorithms. In this context, Jon Kleinberg introduced the notion of two distinct types of Web sites: hubs and authorities . Kleinberg argued that hubs and authorities exhibit a mutually reinforcing relationship : a good hub will point to many authorities, and a good authority will be pointed at by many hubs. In light of this, he devised an algorithm aimed at finding authoritative sites. We present SALSA, a new stochastic approach for link structure analysis, which examines random walks on graphs derived from the link structure. We show that both SALSA and Kleinberg's mutual reinforcement approach employ the same meta-algorithm. We then prove that SALSA is equivalent to a weighted in-degree analysis of the link-structure of World Wide Web subgraphs, making it computationally more efficient than the mutual reinforcement approach. We compare the results of applying SALSA to the results derived through Kleinberg's approach. These comparisons reveal a topological phenomenon called the TKC effect (Tightly Knit Community) which, in certain cases, prevents the mutual reinforcement approach from identifying meaningful authorities. Received November 04, 2008Abstract. Problems which have a huge database always issue challenges to scientists.Evaluating importance of Webs is an interesting example. This problem should be verydiﬃcult about not only computation, but also storage since the Web environment con-tains around 10 billions Web pages. Basing on the “random surfer” idea of PageRankalgorithm, MPageRank greatly improves results of Web search by applying a proba-bilistic model on the link structure of Webs to evaluate “authority” of Webs. UnlikePageRank, in MPageRank, a Web now has diﬀerent ranking scores which depend onthe given multi topics. By assigning a value characterizing a relationship between con-tent of pages and a popular topic, we would like to introduce some new notions suchas the inﬂuence of page and the stability of rank score vector to evaluate the stabilityof Web environment. However, the main idea of establishing the MPageRank modelis to partition our Web graph into smaller-size Web subgraphs. As a consequence ofevaluation and rejection about pages inﬂuence weakly to other pages, the rank scoreof pages of the original Web graph can be approximated from the rank score of pagesin the new partition Web graph.2000 Mathematics Subject Classiﬁcation: ————————–.Key words: —————————————-.