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.
Zhiltsov and Agichtein @cite_9 propose a learning to rank approach, where they model the relations between entities through a various set of features, such as language models and other query related features (e.g query length). Finally, through tensor matrix factorisation they find latent similarities between entities, later used in their learning to rank model. One major disadvantage of this approach is that it is supervised, hence, unlikely to perform reasonably well on ad-hoc entity search tasks.
@cite_9
2030868605
This paper presents a word-entity duet framework for utilizing knowledge bases in ad-hoc retrieval. In this work, the query and documents are modeled by word-based representations and entity-based representations. Ranking features are generated by the interactions between the two representations, incorporating information from the word space, the entity space, and the cross-space connections through the knowledge graph. To handle the uncertainties from the automatically constructed entity representations, an attention-based ranking model AttR-Duet is developed. With back-propagation from ranking labels, the model learns simultaneously how to demote noisy entities and how to rank documents with the word-entity duet. Evaluation results on TREC Web Track ad-hoc task demonstrate that all of the four-way interactions in the duet are useful, the attention mechanism successfully steers the model away from noisy entities, and together they significantly outperform both word-based and entity-based learning to rank systems. 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. 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. This paper addresses the problem of Named Entity Recognition in Query (NERQ), which involves detection of the named entity in a given query and classification of the named entity into predefined classes. NERQ is potentially useful in many applications in web search. The paper proposes taking a probabilistic approach to the task using query log data and Latent Dirichlet Allocation. We consider contexts of a named entity (i.e., the remainders of the named entity in queries) as words of a document, and classes of the named entity as topics. The topic model is constructed by a novel and general learning method referred to as WS-LDA (Weakly Supervised Latent Dirichlet Allocation), which employs weakly supervised learning (rather than unsupervised learning) using partially labeled seed entities. Experimental results show that the proposed method based on WS-LDA can accurately perform NERQ, and outperform the baseline methods.