Language, representation and contexts
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ACM SIGIR Forum
Ontologies: a silver bullet for knowledge management and electronic commerce
Ontologies: a silver bullet for knowledge management and electronic commerce
Scaling question answering to the web
ACM Transactions on Information Systems (TOIS)
Modern Information Retrieval
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Ontology mapping: the state of the art
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What's new about the Semantic Web?: some questions
ACM SIGIR Forum
Semantic integration: a survey of ontology-based approaches
ACM SIGMOD Record
Queue - Semi-structured Data
Provenance management in curated databases
Proceedings of the 2006 ACM SIGMOD international conference on Management of data
ACM SIGMOD Record
Ontology Matching
A system for semantic data fusion in sensor networks
Proceedings of the 2007 inaugural international conference on Distributed event-based systems
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IEEE Intelligent Systems
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ACM Computing Surveys (CSUR)
NLDB '08 Proceedings of the 13th international conference on Natural Language and Information Systems: Applications of Natural Language to Information Systems
Ten Challenges for Ontology Matching
OTM '08 Proceedings of the OTM 2008 Confederated International Conferences, CoopIS, DOA, GADA, IS, and ODBASE 2008. Part II on On the Move to Meaningful Internet Systems
idMesh: graph-based disambiguation of linked data
Proceedings of the 18th international conference on World wide web
DBpedia: a nucleus for a web of open data
ISWC'07/ASWC'07 Proceedings of the 6th international The semantic web and 2nd Asian conference on Asian semantic web conference
Everything Is Miscellaneous: The Power of the New Digital Disorder
Everything Is Miscellaneous: The Power of the New Digital Disorder
Towards certain fixes with editing rules and master data
Proceedings of the VLDB Endowment
When owl: sameAs isn't the same: an analysis of identity in linked data
ISWC'10 Proceedings of the 9th international semantic web conference on The semantic web - Volume Part I
Exposing real world information for the web of things
Proceedings of the 8th International Workshop on Information Integration on the Web: in conjunction with WWW 2011
CrowdDB: answering queries with crowdsourcing
Proceedings of the 2011 ACM SIGMOD International Conference on Management of data
Ontology alignment evaluation initiative: six years of experience
Journal on data semantics XV
Augmenting navigation for collaborative tagging with emergent semantics
ISWC'06 Proceedings of the 5th international conference on The Semantic Web
Semantic web architecture: stack or two towers?
PPSWR'05 Proceedings of the Third international conference on Principles and Practice of Semantic Web Reasoning
Relevance feedback between web search and the semantic web
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Three
Social Semantics: The Search for Meaning on the Web
Social Semantics: The Search for Meaning on the Web
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The world is increasingly full of data. Organisations, governments and individuals are creating increasingly large data sources, and in many cases making them publicly available. This offers massive potential for interaction and mutual collaboration. But using this data often creates problems. Those creating the data will use their own terminology, structure and formats for the data, meaning that data from one source will be incompatible with data from another source. When presented with a large, unknown data source, it is very difficult to ascribe meaning to the terms of that data source, and to understand what is being conveyed. Much effort has been invested in data interpretation prior to run-time, with large data sources being matched against each other off-line. But data is often used dynamically, and so to maximise the value of the data it is necessary to extract meaning from it dynamically. We therefore postulate that an essential competent of utilising the world of data in which we increasingly live is the development of the ability to discover meaning on the go in large, heterogenous data.This paper provides an overview of the current state-of-the-art, reviewing the aims and achievements in different fields which can be applied to this problem. We take a brief look at cutting edge research in this field, summarising four papers published in the special issue of the AI Review on Discovering Meaning on the go in Large Heterogenous Data, and conclude with our thoughts about where research in this field is going, and what our priorities must be to enable us to move closer to achieving this goal.