text: The increasing amount of data on the Web, in particular of Linked Data, has led to a diverse landscape of datasets, which make entity retrieval a challenging task. Explicit cross-dataset links, for instance to indicate co-references or related entities can significantly improve entity retrieval. However, only a small fraction of entities are interlinked through explicit statements. In this paper, we propose a two-fold entity retrieval approach. In a first, offline preprocessing step, we cluster entities based on the x---means and spectral clustering algorithms. In the second step, we propose an optimized retrieval model which takes advantage of our precomputed clusters. For a given set of entities retrieved by the BM25F retrieval approach and a given user query, we further expand the result set with relevant entities by considering features of the queries, entities and the precomputed clusters. Finally, we re-rank the expanded result set with respect to the relevance to the query. We perform a thorough experimental evaluation on the Billions Triple Challenge BTC12 dataset. The proposed approach shows significant improvements compared to the baseline and state of the art approaches.
  @cite_7: While some web search users know exactly what they are looking for, others are willing to explore topics related to an initial interest. Often, the user's initial interest can be uniquely linked to an entity in a knowledge base. In this case, it is natural to recommend the explicitly linked entities for further exploration. In real world knowledge bases, however, the number of linked entities may be very large and not all related entities may be equally relevant. Thus, there is a need for ranking related entities. In this paper, we describe Spark, a recommendation engine that links a user's initial query to an entity within a knowledge base and provides a ranking of the related entities. Spark extracts several signals from a variety of data sources, including Yahoo! Web Search, Twitter, and Flickr, using a large cluster of computers running Hadoop. These signals are combined with a machine learned ranking model in order to produce a final recommendation of entities to user queries. This system is currently powering Yahoo! Web Search result pages.
  @cite_21: The Probabilistic Relevance Framework (PRF) is a formal framework for document retrieval, grounded in work done in the 1970—1980s, which led to the development of one of the most successful text-retrieval algorithms, BM25. In recent years, research in the PRF has yielded new retrieval models capable of taking into account document meta-data (especially structure and link-graph information). Again, this has led to one of the most successful Web-search and corporate-search algorithms, BM25F. This work presents the PRF from a conceptual point of view, describing the probabilistic modelling assumptions behind the framework and the different ranking algorithms that result from its application: the binary independence model, relevance feedback models, BM25 and BM25F. It also discusses the relation between the PRF and other statistical models for IR, and covers some related topics, such as the use of non-textual features, and parameter optimisation for models with free parameters.
  @cite_0: We reveal that the Okapi BM25 retrieval function tends to overly penalize very long documents. To address this problem, we present a simple yet effective extension of BM25, namely BM25L, which "shifts" the term frequency normalization formula to boost scores of very long documents. Our experiments show that BM25L, with the same computation cost, is more effective and robust than the standard BM25.
  @cite_19: Triple stores have long provided RDF storage as well as data access using expressive, formal query languages such as SPARQL. The new end users of the Semantic Web, however, are mostly unaware of SPARQL and overwhelmingly prefer imprecise, informal keyword queries for searching over data. At the same time, the amount of data on the Semantic Web is approaching the limits of the architectures that provide support for the full expressivity of SPARQL. These factors combined have led to an increased interest in semantic search, i.e. access to RDF data using Information Retrieval methods. In this work, we propose a method for effective and efficient entity search over RDF data. We describe an adaptation of the BM25F ranking function for RDF data, and demonstrate that it outperforms other state-of-the-art methods in ranking RDF resources. We also propose a set of new index structures for efficient retrieval and ranking of results. We implement these results using the open-source MG4J framework.
  @cite_15: Retrieving semi-structured entities to answer keyword queries is an increasingly important feature of many modern Web applications. The fast-growing Linked Open Data (LOD) movement makes it possible to crawl and index very large amounts of structured data describing hundreds of millions of entities. However, entity retrieval approaches have yet to find efficient and effective ways of ranking and navigating through those large data sets. In this paper, we address the problem of Ad-hoc Object Retrieval over large-scale LOD data by proposing a hybrid approach that combines IR and structured search techniques. Specifically, we propose an architecture that exploits an inverted index to answer keyword queries as well as a semi-structured database to improve the search effectiveness by automatically generating queries over the LOD graph. Experimental results show that our ranking algorithms exploiting both IR and graph indices outperform state-of-the-art entity retrieval techniques by up to 25 over the BM25 baseline.
  @cite_11: Data discovery on the Semantic Web requires crawling and indexing of statements, in addition to the 'linked-data' approach of de-referencing resource URIs. Existing Semantic Web search engines are focused on database-like functionality, compromising on index size, query performance and live updates. We present Sindice, a lookup index over Semantic Web resources. Our index allows applications to automatically locate documents containing information about a given resource. In addition, we allow resource retrieval through inverse-functional properties, offer a full-text search and index SPARQL endpoints. Finally, we extend the sitemap protocol to efficiently index large datasets with minimal impact on data providers.
summary: Following this direction, @cite_15 proposed a hybrid approach based on query expansion and relevance feedback techniques on top of the BM25 ranking function to build an entity retrieval framework. In contrast to this work, we use the state-of-the-art BM25F @cite_19 @cite_21 to assign varying degrees of importance to different parts of a document. Further, through an offline pre-processing step we are able to infer links between similar entities for the retrieval process. This is particularly important when considering datasets that have less links between entities, a significant feature of the work by @cite_15 . Another advantage of adopting BM25F is penalising documents entities, consisting of long textual literals, in the final ranking @cite_0 . Sindice @cite_11 is another approach focusing on indexing RDF documents. It supports data discovery and integration by taking advantage of DBpedia entities as a source to actively index resources. The process performed by Sindice plays a key role in centralising disparate data sources on the Web. The adoption of entities and foremost entity types (topics) is also supported by @cite_7 in the recommendation of entities in Web search. Our approach can benefit Sindice by indexing documents following a topic-based fashion.