RiMOM: A Dynamic Multistrategy Ontology Alignment Framework

By Juan-Zi Li, Jie Tang, Yi Li, Qiong Luo. 2009

471 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Management Science and Operations Research, Molecular Biology, Natural Language Processing

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Citations in the corpus

SIGMa: simple greedy matching for aligning large knowledge bases (2012, methodology): “In this experiment, we test the performance of SiGMa on the three benchmark datasets and compare them with the best published results so far that we are aware of: PARIS [28] for the Person-Restaurants datasets (which compared favorably over ObjectCoref [15]); and RiMoM [20] for Rexa-DBPL.”
Generic schema matching, ten years later (2011, methodology): “Figure 2 shows a comparative overview of selected tools: Cupid [45], COMA++ [3][19][20], ASMOV [40], Falcon-AO [39], RiMON [44], AgreementMaker [16], OII Harmony [67].”
A self-training approach for resolving object coreference on the semantic web (2011, background): “RiMOM [18] is a purely similarity-based system, which integrated many matchers to exploit a range of characteristics for both concepts and instances.”
PARIS: Probabilistic Alignment of Relations, Instances, and Schema (2011, background): “Only very few approaches address the cause of aligning both schema and instances: the Ri-MOM [22] and iliads [32] systems.”
Ontology Matching: State of the Art and Future Challenges (2013, background): “Speciﬁcally, alignments can be used as navigation links within a multi-faceted browser to access a collection via thesauri it was not originally indexed with [30].”
Ontology Matching (2007, background): “Tuning is often automated through predefined rules (Mochol and Jentzsch 2008; Li et al. 2009; Peukert et al. 2012) or machine learning (Lee et al.”