Many social Web sites allow users to publish content and annotate with descriptive metadata. In addition to flat tags, some social Web sites have recently began to allow users to organize their content and metadata hierarchically. The social photosharing site Flickr, for example, allows users to group related photos in sets, and related sets in collections. The social bookmarking site Del.icio.us similarly lets users group related tags into bundles. Although the sites themselves don't impose any constraints on how these hierarchies are used, individuals generally use them to capture relationships between concepts, most commonly the broader narrower relations. Collective annotation of content with hierarchical relations may lead to an emergent classification system, called a folksonomy. While some researchers have explored using tags as evidence for learning folksonomies, we believe that hierarchical relations described above offer a high-quality source of evidence for this task. We propose a simple approach to aggregate shallow hierarchies created by many distinct Flickr users into a common folksonomy. Our approach uses statistics to determine if a particular relation should be retained or discarded. The relations are then woven together into larger hierarchies. Although we have not carried out a detailed quantitative evaluation of the approach, it looks very promising since it generates very reasonable, non-trivial hierarchies.
Our approach is similar in spirit to ontology alignment, , @cite_13 . However, unlike those works, which merge a small number of deep and detailed hierarchies, we merge large number of noisy, shallow hierarchies.
@cite_13
2125149214 1597082186 161226219 2007226769
There is a great deal of research on ontology integration which makes use of rich logical constraints to reason about the structural and logical alignment of ontologies. There is also considerable work on matching data instances from heterogeneous schema or ontologies. However, little work exploits the fact that ontologies include both data and structure. We aim to close this gap by presenting a new algorithm (ILIADS) that tightly integrates both data matching and logical reasoning to achieve better matching of ontologies. We evaluate our algorithm on a set of 30 pairs of OWL Lite ontologies with the schema and data matchings found by human reviewers. We compare against two systems - the ontology matching tool FCA-merge [28] and the schema matching tool COMA++ [1]. ILIADS shows an average improvement of 25 in quality over FCA-merge and a 11 improvement in recall over COMA++. One of the main challenges that the Semantic Web faces is the integration of a growing number of independently designed ontologies. In this work, we present paris, an approach for the automatic alignment of ontologies. paris aligns not only instances, but also relations and classes. Alignments at the instance level cross-fertilize with alignments at the schema level. Thereby, our system provides a truly holistic solution to the problem of ontology alignment. The heart of the approach is probabilistic, i.e., we measure degrees of matchings based on probability estimates. This allows paris to run without any parameter tuning. We demonstrate the efficiency of the algorithm and its precision through extensive experiments. In particular, we obtain a precision of around 90 in experiments with some of the world's largest ontologies. Ontology matching, the task of determining relations that hold among terms of two different ontologies, is a key issue in the Semantic Web and other related fields. In order to compare the behaviour of different ontology matching systems, the Ontology Alignment Evaluation Initiative (OAEI) has established a periodical controlled evaluation that comes in a yearly event. We present here our participation in the 2008 initiative. Our schema-based alignment algorithm compares each pair of ontology terms by, firstly, extracting their ontological contexts up to a certain depth (enriched by using transitive entailment) and, secondly, combining different elementary ontology matching techniques (e.g., lexical distances and vector space modelling). Benchmark results show a very good behaviour in terms of precision, while preserving an acceptable recall. Based on our experience, we have also included some remarks about the nature of benchmark test cases that, in our opinion, could help improving the OAEI tests in the future. Ontology mapping seeks to find semantic correspondences between similar elements of different ontologies. It is a key challenge to achieve semantic interoperability in building the Semantic Web. This paper proposes a new generic and adaptive ontology mapping approach, called the PRIOR+, based on propagation theory, information retrieval techniques and artificial intelligence. The approach consists of three major modules, i.e., the IR-based similarity generator, the adaptive similarity filter and weighted similarity aggregator, and the neural network based constraint satisfaction solver. The approach first measures both linguistic and structural similarity of ontologies in a vector space model, and then aggregates them using an adaptive method based on their harmonies, which is defined as an estimator of performance of similarity. Finally to improve mapping accuracy the interactive activation and competition neural network is activated, if necessary, to search for a solution that can satisfy ontology constraints. The experimental results show that harmony is a good estimator of f-measure; the harmony based adaptive aggregation outperforms other aggregation methods; neural network approach significantly boosts the performance in most cases. Our approach is competitive with top-ranked systems on benchmark tests at OAEI campaign 2007, and performs the best on real cases in OAEI benchmark tests.