Modern Information Retrieval
Understanding and Using Context
Personal and Ubiquitous Computing
Strength and similarity of affix removal stemming algorithms
ACM SIGIR Forum
Context-sensitive information retrieval using implicit feedback
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Essential Business Process Modeling
Essential Business Process Modeling
VIVACE context based search platform
CONTEXT'07 Proceedings of the 6th international and interdisciplinary conference on Modeling and using context
Scaling up top-K cosine similarity search
Data & Knowledge Engineering
Optimizing enterprise search by automatically relating user context to textual document content
i-KNOW '11 Proceedings of the 11th International Conference on Knowledge Management and Knowledge Technologies
Linear combination of component results in information retrieval
Data & Knowledge Engineering
An ontological context-aware approach for a dynamic business process formulation
ACIIDS'13 Proceedings of the 5th Asian conference on Intelligent Information and Database Systems - Volume Part II
Ontology-based personalised retrieval in support of reminiscence
Knowledge-Based Systems
An approach for selecting seed URLs of focused crawler based on user-interest ontology
Applied Soft Computing
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An ontology is a formal conceptualization of a domain, specifying the concepts of the domain and the relations between them. It is however not a straight forward task to use this knowledge for information retrieval purposes. In this paper we describe the concept of an ontological profile, which is a semantic extension of an ontology where each ontology concept is given a description in terms of a vector of weighted keywords. An experiment has been conducted with a prototype search engine using ontological profiles for query expansion. The evaluation shows encouraging results compared to standard keyword based search. Furthermore, we describe the notion of context in an information retrieval setting and address how we can combine semantics and context in search based on query expansion.