The Role of Query Sessions in Extracting Instance Attributes from Web Search Queries

Paşca, Marius; Alfonseca, Enrique; Robledo-Arnuncio, Enrique; Martin-Brualla, Ricardo; Hall, Keith B.

doi:10.1007/978-3-642-12275-0_9

Cited by 6 publications

(3 citation statements)

References 30 publications

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“…There are some articles about how to extract attributes from different types of data sources for specific purposes. Pasca et al [39] extract attributes from query streams, query sessions and query logs. Fang [40] contributes with the extraction from Web texts, DOM trees and knowledge bases (KBs), all these extractions are carried out for the purposes of enhancing existing ontologies.…”

Section: Related Workmentioning

confidence: 99%

ANDES: An approach to embed search services on the Web browser

Bosetti¹,

Tacuri²,

Gambo³

et al. 2022

Computer Standards & Interfaces

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Section: Related Workmentioning

confidence: 99%

ANDES: An approach to embed search services on the Web browser

Bosetti¹,

Tacuri²,

Gambo³

et al. 2022

Computer Standards & Interfaces

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“…Many proposed approaches formulate the entity attribute ranking problem as a post processing step of automated attribute-value extraction. In [14,15,16], Pasca et al firstly extract potential class-attribute pairs using linguistically motivated patterns from unstructured text including query logs and query sessions, and then score the attributes using the Bayes model. In [18], Rahul Rai proposed to identify product attributes from customer online reviews using partof-speech(POS) tagging patterns, and to evaluate their importance with several different frequency metrics.…”

Section: Related Researchmentioning

confidence: 99%

Important Attribute Identification in Knowledge Graph

Sun,

Yang,

Zhang

et al. 2018

Preprint

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The knowledge graph(KG) composed of entities with their descriptions and attributes, and relationship between entities, is finding more and more application scenarios in various natural language processing tasks. In a typical knowledge graph like Wikidata, entities usually have a large number of attributes, but it is difficult to know which ones are important. The importance of attributes can be a valuable piece of information in various applications spanning from information retrieval to natural language generation. In this paper, we propose a general method of using external user generated text data to evaluate the relative importance of an entity's attributes. To be more specific, we use the word/sub-word embedding techniques to match the external textual data back to entities' attribute name and values and rank the attributes by their matching cohesiveness. To our best knowledge, this is the first work of applying vector based semantic matching to important attribute identification, and our method outperforms the previous traditional methods. We also apply the outcome of the detected important attributes to a language generation task; compared with previous generated text, the new method generates much more customized and informative messages.

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“…Pasca and Durme (2007) discovery that the predicates extracted from query stream are of 45 per cent higher accuracy than those from Web texts by adopting a head-to-head qualitative comparison. They further conduct in-depth predicate extraction from both query logs and query sessions by Pasca et al . (2010) to prove this point.…”

Section: Related Workmentioning

confidence: 99%

GrandBase: generating actionable knowledge from Big Data

Fang

Sheng

Wang

et al. 2017

PRR

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Purpose This paper aims to propose a system for generating actionable knowledge from Big Data and use this system to construct a comprehensive knowledge base (KB), called GrandBase. Design/methodology/approach In particular, this study extracts new predicates from four types of data sources, namely, Web texts, Document Object Model (DOM) trees, existing KBs and query stream to augment the ontology of the existing KB (i.e. Freebase). In addition, a graph-based approach to conduct better truth discovery for multi-valued predicates is also proposed. Findings Empirical studies demonstrate the effectiveness of the approaches presented in this study and the potential of GrandBase. The future research directions regarding GrandBase construction and extension has also been discussed. Originality/value To revolutionize our modern society by using the wisdom of Big Data, considerable KBs have been constructed to feed the massive knowledge-driven applications with Resource Description Framework triples. The important challenges for KB construction include extracting information from large-scale, possibly conflicting and different-structured data sources (i.e. the knowledge extraction problem) and reconciling the conflicts that reside in the sources (i.e. the truth discovery problem). Tremendous research efforts have been contributed on both problems. However, the existing KBs are far from being comprehensive and accurate: first, existing knowledge extraction systems retrieve data from limited types of Web sources; second, existing truth discovery approaches commonly assume each predicate has only one true value. In this paper, the focus is on the problem of generating actionable knowledge from Big Data. A system is proposed, which consists of two phases, namely, knowledge extraction and truth discovery, to construct a broader KB, called GrandBase.

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The Role of Query Sessions in Extracting Instance Attributes from Web Search Queries

Cited by 6 publications

References 30 publications

ANDES: An approach to embed search services on the Web browser

ANDES: An approach to embed search services on the Web browser

Important Attribute Identification in Knowledge Graph

GrandBase: generating actionable knowledge from Big Data

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