On ordered weighted averaging aggregation operators in multicriteria decisionmaking
IEEE Transactions on Systems, Man and Cybernetics
Fuzzy Sets and Systems
Modern Information Retrieval
Combining Multiple Knowledge Bases
IEEE Transactions on Knowledge and Data Engineering
Information clustering based on fuzzy multisets
Information Processing and Management: an International Journal - Modelling vagueness and subjectivity in information access
Information fusion in the context of multi-document summarization
ACL '99 Proceedings of the 37th annual meeting of the Association for Computational Linguistics on Computational Linguistics
Centroid-based summarization of multiple documents
Information Processing and Management: an International Journal
From single to multi-document summarization: a prototype system and its evaluation
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Possibilistic information fusion using maximal coherent subsets
IEEE Transactions on Fuzzy Systems
IPMU'10 Proceedings of the Computational intelligence for knowledge-based systems design, and 13th international conference on Information processing and management of uncertainty
Elicitation, assessment, and pooling of expert judgments using possibility theory
IEEE Transactions on Fuzzy Systems
RL-bags: A conceptual, level-based approach to fuzzy bags
Fuzzy Sets and Systems
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Information fusion is a research area that investigates how to combine information provided by independent sources into one piece of information. Several aspects of this topic have already been studied leading to, amongst others, aggregation operators in bounded lattices and merge functions of propositional belief bases. In this paper, information fusion is investigated in the context of coreferent objects, which are pieces of data in an information system, that refer to the same real world entity. The fundamental operator in our approach is a merge function that maps a multiset of coreferent objects onto a single object, which is called the solution. We investigate the specific case where objects themselves are multisets, which can be applied to problems such as Multi-Document Summarization (MDS) and fusion of duplicate graphs (e.g. XML documents). Our approach involves the definition of quality measures that express the correctness and the completeness of a given solution. We show how a solution can be found that optimizes a balance between correctness and completeness. Merge functions that result in such a solution are called f-optimal merge functions and we investigate their properties.