You are provided with the abstract of a scientific paper and the abstracts of the papers it references. Your task is to write a Related Work section for the main paper. This section should concisely summarize and integrate the key points from the cited abstracts, reflecting how they relate to the main paper's contributions and context. In your summary, use numerical citation markers like [1], [2], etc., to refer to the corresponding papers.

Main Abstract:
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.

Cited Abstract(s):
[1]: The integration of the classical Web (of documents) with the emerging Web of Data is a challenging vision. In this paper we focus on an integration approach during searching which aims at enriching the responses of non-semantic search systems (e.g. professional search systems, web search engines) with semantic information, i.e. Linked Open Data (LOD), and exploiting the outcome for providing an overview of the search space and allowing the users (apart from restricting it) to explore the related LOD. We use named entities (e.g. persons, locations, etc.) as the "glue" for automatically connecting search hits with LOD. We consider a scenario where this entity-based integration is performed at query time with no human effort, and no a-priori indexing, which is beneficial in terms of configurability and freshness. To realize this scenario one has to tackle various challenges. One spiny issue is that the number of identified entities can be high, the same is true for the semantic information about these entities that can be fetched from the available LOD (i.e. their properties and associations with other entities). To this end, in this paper we propose a Link Analysis-based method which is used for (a) ranking (and thus selecting to show) the more important semantic information related to the search results, (b) deriving and showing top-K semantic graphs. In the sequel, we report the results of a survey regarding the marine domain with promising results, and comparative results that illustrate the effectiveness of the proposed (Page Rank-based) ranking scheme. Finally, we report experimental results regarding efficiency showing that the proposed functionality can be offered even at query time.

[2]: More and more websites embed structured data describing for instance products, reviews, blog posts, people, organizations, events, and cooking recipes into their HTML pages using markup standards such as Microformats, Microdata and RDFa. This development has accelerated in the last two years as major Web companies, such as Google, Facebook, Yahoo!, and Microsoft, have started to use the embedded data within their applications. In this paper, we analyze the adoption of RDFa, Microdata, and Microformats across the Web. Our study is based on a large public Web crawl dating from early 2012 and consisting of 3 billion HTML pages which originate from over 40 million websites. The analysis reveals the deployment of the different markup standards, the main topical areas of the published data as well as the different vocabularies that are used within each topical area to represent data. What distinguishes our work from earlier studies, published by the large Web companies, is that the analyzed crawl as well as the extracted data are publicly available. This allows our findings to be verified and to be used as starting points for further domain-specific investigations as well as for focused information extraction endeavors.

[3]: Ranking plays important role in contemporary information search and retrieval systems. Among existing ranking algorithms, link analysis based algorithms have been proved to be effective for ranking documents retrieved from large-scale text repositories such as the current Web. Recent developments in semantic Web raise considerable interest in designing new ranking paradigms for various semantic search applications. While ranking methods in this context exist, they have not gained much popularity. In this article we introduce the idea of the ''Rational Research'' model which reflects search behaviour of a ''rational'' researcher in a scientific research environment, and propose the RareRank algorithm for ranking entities in semantic search systems, in particular, we focus on elaborating the rationale and implementation of the algorithm. Experiments are performed using the RareRank algorithm and the results are evaluated by domain experts using popular ranking performance measures. A comparison study with existing link-based ranking algorithms reveals the benefits of the proposed method.

[4]: In contrast with the current Web search methods that essentially do document-level ranking and retrieval, we are exploring a new paradigm to enable Web search at the object level. We collect Web information for objects relevant for a specific application domain and rank these objects in terms of their relevance and popularity to answer user queries. Traditional PageRank model is no longer valid for object popularity calculation because of the existence of heterogeneous relationships between objects. This paper introduces PopRank, a domain-independent object-level link analysis model to rank the objects within a specific domain. Specifically we assign a popularity propagation factor to each type of object relationship, study how different popularity propagation factors for these heterogeneous relationships could affect the popularity ranking, and propose efficient approaches to automatically decide these factors. Our experiments are done using 1 million CS papers, and the experimental results show that PopRank can achieve significantly better ranking results than naively applying PageRank on the object graph.

[5]: Swoogle is a crawler-based indexing and retrieval system for the Semantic Web. It extracts metadata for each discovered document, and computes relations between documents. Discovered documents are also indexed by an information retrieval system which can use either character N-Gram or URIrefs as keywords to find relevant documents and to compute the similarity among a set of documents. One of the interesting properties we compute is ontology rank, a measure of the importance of a Semantic Web document.

[6]: Much of Web search and browsing activity is today centered around entities. For this reason, Search Engine Result Pages (SERPs) increasingly contain information about the searched entities such as pictures, short summaries, related entities, and factual information. A key facet that is often displayed on the SERPs and that is instrumental for many applications is the entity type. However, an entity is usually not associated to a single generic type in the background knowledge bases but rather to a set of more specific types, which may be relevant or not given the document context. For example, one can find on the Linked Open Data cloud the fact that Tom Hanks is a person, an actor, and a person from Concord, California. All those types are correct but some may be too general to be interesting (e.g., person), while other may be interesting but already known to the user (e.g., actor), or may be irrelevant given the current browsing context (e.g., person from Concord, California). In this paper, we define the new task of ranking entity types given an entity and its context. We propose and evaluate new methods to find the most relevant entity type based on collection statistics and on the graph structure interconnecting entities and types. An extensive experimental evaluation over several document collections at different levels of granularity (e.g., sentences, paragraphs, etc.) and different type hierarchies (including DBPedia, Freebase, and schema.org) shows that hierarchy-based approaches provide more accurate results when picking entity types to be displayed to the end-user while still being highly scalable.
Moreover, a study made in 2012 [2] 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 [5] (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 [4] offers a modified PageRank that considers the different types of predicates between resources. RareRank [3] introduces a modified PageRank with a teleportation matrix that takes into account topical relationships between resources as available from ontologies. The approach introduced in [1] 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 [4]5 addresses the task of ranking entity types given an initial entity and its textual context.