Corpus ID: 52937116

Entity-Relationship Search over the Web

  title={Entity-Relationship Search over the Web},
  author={Pedro Saleiro and Natasa Milic-Frayling and E. M. Rodrigues and C. Soares},
Entity-Relationship (E-R) Search is a complex case of Entity Search where the goal is to search for multiple unknown entities and relationships connecting them. We assume that a E-R query can be decomposed as a sequence of sub-queries each containing keywords related to a specific entity or relationship. We adopt a probabilistic formulation of the E-R search problem. When creating specific representations for entities (e.g. context terms) and for pairs of entities (i.e. relationships) it is… Expand


Exploiting Entity Linking in Queries for Entity Retrieval
A new probabilistic component is introduced and it is shown how it can be applied on top of any term-based entity retrieval model that can be emulated in the Markov Random Field framework, including language models, sequential dependence models, as well as their fielded variations. Expand
Entity-relationship queries over wikipedia
This work presents a ranking framework for general entity-relationship queries and a position-based Bounded Cumulative Model for accurate ranking of query answers, and shows the effectiveness and accuracy of the ranking method. Expand
Discovering expansion entities for keyword-based entity search in linked data
This study introduces a framework to expand keyword queries with expansion entities for keyword-based entity search in linked data, and introduces an algorithm called PFC for expansion entities by which to expand a given query. Expand
Relationship Queries on Extended Knowledge Graphs
The TriniT search engine for querying and ranking on extended knowledge graphs that combine relational facts with textual web contents is presented and a model for automatic query relaxation to compensate for mismatches between the data and a user's query is presented. Expand
Proximity-based document representation for named entity retrieval
This work proposes a new document representation which emphasizes text in proximity to entities and thus incorporates sequential information implicit in text and significantly improves retrieval performance. Expand
EntityRank: Searching Entities Directly and Holistically
This work focuses on the core challenge of ranking entities, by distilling its underlying conceptual model Impression Model and developing a probabilistic ranking framework, EntityRank, that is able to seamlessly integrate both local and global information in ranking. Expand
Example Based Entity Search in the Web of Data
It is found that both the text and structure-based approaches are effective in retrieving relevant entities, but that they find different sets of entities. Expand
Parameterized Fielded Term Dependence Models for Ad-hoc Entity Retrieval from Knowledge Graph
The PFSDM and PFFDM are proposed, two novel models for entity retrieval from knowledge graphs, which infer the user's intent behind each individual query concept by dynamically estimating its projection onto the fields of structured entity representations based on a small number of statistical and linguistic features. Expand
Learning joint query interpretation and response ranking
This work proposes two new, natural formulations for joint query interpretation and response ranking that exploit bidirectional flow of information between the knowledge base and the corpus, inspired by probabilistic language models and max-margin discriminative learning. Expand
Language-model-based ranking for queries on RDF-graphs
A language-model-based approach to ranking the results of exact, relaxed and keyword-augmented graph pattern queries over RDF graphs such as ER graphs by estimating a query model and a set of result-graph models and ranks results based on their Kullback-Leibler divergence with respect to the query model. Expand