A personalized query expansion approach using context

Seher, Indra; Ginige, Athula; Shahrestani, Seyed

doi:10.1049/cp:20070396

Cited by 8 publications

(3 citation statements)

References 220 publications

(223 reference statements)

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“…Chatterjee et al [11] categorise a query in three parts – common entities (entities reused across all domains), core entities (entities returned by queries) and auxiliary entities (entities required to filter results). Seher et al [12] propose a personalised query expansion approach that considers the user, the domain and task-specific preferences pertaining to user queries to resolve ambiguity. This work suggests that most of the query terms are domain specific and context dependent.…”

Section: Literature Reviewmentioning

confidence: 99%

Context-based understanding of food-related queries using a culinary knowledge model

Chatterjee

2021

Journal of Information Science

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Dietary practices are governed by a mix of ethnographic aspects, such as social, cultural and environmental factors. These aspects need to be taken into consideration during an analysis of food-related queries. Queries are usually ambiguous. It is essential to understand, analyse and refine the queries for better search and retrieval. The work is focused on identifying the explicit, implicit and hidden facets of a query, taking into consideration the context – culinary domain. This article proposes a technique for query understanding, analysis and refinement based on a domain specific knowledge model. Queries are conceptualised by mapping the query term to concepts defined in the model. This allows an understanding of the semantic point of view of a query and an ability to determine the meaning of its terms and their interrelatedness. The knowledge model acts as a backbone providing the context for query understanding, analysis and refinement and outperforms other models, such as Schema.org , BBC Food Ontology and Recipe Ontology.

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Section: Literature Reviewmentioning

confidence: 99%

Context-based understanding of food-related queries using a culinary knowledge model

Chatterjee

2021

Journal of Information Science

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“…During the past two decades, researchers have developed techniques that enable systems to adapt to their users in many different ways (Seher et al, 2007). One of the major research directions for humancomputer interaction (HCI) and Information Retrieval (IR) has been exploring the novel forms of interaction that can be achieved by integrating computer technology with the everyday physical world in which we live and work.…”

Section: Contextmentioning

confidence: 99%

Context Based Content Aggregation for Social Life Networks

Mathai

Ginige

2013

Proceedings of the 8th International Joint Conference on Software Technologies

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It is extremely useful to have right information at the right time. Social Life Networks (SLN) extend the capabilities of current social networks by combining them with the technological advances now found in Smartphones that include myriad of sensors and multimedia input and output capabilities to provide essential information to support livelihood activities. The challenge is to provide this information within the required context. For this we need to model the context by acquiring the physical data to provide meaningful abstractions with respect to the application domain and the needs of the users. We have developed a physical context model based on user profile, location, time and activity and a mapping to match the logical context of various data sources from which we can get the required information. Based on this model we have developed a SLN for farmers in Sri Lanka to provide agricultural information in the context of farming life cycle stages, location of their farm land, cultivation season and other economic parameters. In the field trails there was unanimous agreement among farmers that this application is very useful for them because they were able to get the required information in context.

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“…Researchers have worked using different techniques to provide personalization when learner searches online learning material on the web. Learner's searching query should be expanded to understand the user request better by identifying domain and task of user query and expanding user query using contextual information such as domain, user and task-specific preferences [1]. To improve search engine quality in a domain specific search engine, Natural language processing is used to find out the similar words which matches the query string.…”

Section: Related Researchmentioning

confidence: 99%

An Agent based Personalized Intelligent elearning

Shah¹,

Jain²

2011

IJCA

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Education is foundation of any modern society. Self Discovery is a key to an individual's education success. Self Discovery learning is the ability of an individual to learn through personal experience and natural exploration. Online Discovery learning tends to be learner-driven and learner-initiated. Digital repository on web is supportive for online discovery learning. In recent era, availability of infrastructure, flexibility of time, learning resources and their means of sharing has increased adaptability of World Wide Web to learn and attain knowledge to a great extent. However, most of the e-learners find difficulty to select location specific, relevant and significant tutorial on the web. Search engines results pages are not based on the interest, knowledge level and literacy rate of learner. More amount of time spent on searching and filtering and comparatively less time is devoted in actual learning. This paper proposes an agent based personalized intelligent e-learning model. The essence of personalized search engine is to save learner's time to re-rank future result list. This Model uses multiple agents which delivers personalized SERP (Search Engine Results Page) and is more suited for personalization of web pages based on learner's query expanded to manifold queries with novel concept of keyword research and discover knowledge using browser's behaviour. The proposed model uses web usage mining techniques to discover the knowledge of learner.

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A personalized query expansion approach using context

Cited by 8 publications

References 220 publications

Context-based understanding of food-related queries using a culinary knowledge model

Context-based understanding of food-related queries using a culinary knowledge model

Context Based Content Aggregation for Social Life Networks

An Agent based Personalized Intelligent elearning

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