Introducing the A-Posteriori Affinity Propagation (APP) algorithm for incremental clustering with cluster consolidation and stratification • Faithfulness and forgetfulness properties in evolutionary clustering for tracing cluster changes over time • Application of incremental clustering to Semantic Shift Detection and diachronic document corpus analysis • Comparison with benchmark algorithms, namely Affinity Propagation (AP) and Affinity Propagation based on NearestNeighbor Assignment (IAPNA), and discussion on scalability benefits
By specifying that published datasets must link to other existing datasets, the 4th linked data principle ensures a Web of data and not just a set of unconnected data islands. The authors propose in this paper the term data linking to name the problem of finding equivalent resources on the Web of linked data. In order to perform data linking, many techniques were developed, finding their roots in statistics, database, natural language processing and graph theory. The authors begin this paper by providing background information and terminological clarifications related to data linking. Then a comprehensive survey over the various techniques available for data linking is provided. These techniques are classified along the three criteria of granularity, type of evidence, and source of the evidence. Finally, the authors survey eleven recent tools performing data linking and we classify them according to the surveyed techniques.
Abstract. We present APFEL Web, a Web-based application designed to provide a flexible user-friendly tool for the graphical visualization of parton distribution functions (PDFs). In this note we describe the technical design of the APFEL Web application, motivating the choices and the framework used for the development of this project. We document the basic usage of APFEL Web and show how it can be used to provide useful input for a variety of collider phenomenological studies. Finally we provide some examples showing the output generated by the application.
The problem of knowledge sharing is eminent in the P2P area. In this paper, we propose a general framework, called HELIOS (Helios Evolving Interaction-based Ontology knowledge Sharing), conceived for supporting dynamic ontology-based knowledge sharing and evolution in P2P systems. The knowledge sharing and evolution processes in HELIOS are based on peer ontologies, describing the knowledge of each peer, and on interactions among peers, allowing information search and knowledge acquisition/extension, according to pre-defined query models and semantic techniques for ontology matching.
Abstract. The evaluation of matching applications is becoming a major issue in the semantic web and it requires a suitable methodological approach as well as appropriate benchmarks. In particular, in order to evaluate a matching application under different experimental conditions, it is crucial to provide a test dataset characterized by a controlled variety of different heterogeneities among data that rarely occurs in real data repositories. In this paper, we propose SWING (Semantic Web INstance Generation), a disciplined approach to the semi-automatic generation of benchmarks to be used for the evaluation of matching applications.
In this paper, we propose a semantic approach for the dynamic resource discovery based on ontologies for a semantically rich description of the metadata characterizing the resources to be shared, and on semantic matching techniques for dynamically and flexibly comparing ontological resource descriptions against a target resource request.
Abstract. In the semantic web environment, where several independent ontologies are used in order to describe knowledge and data, ontologies have to be aligned by defining mappings among the elements of one ontology and the elements of another ontology. Very often, mappings are not derived from the semantics of the ontologies that are compared. Rather, mappings are computed by evaluating the similarity of the ontologies terminology and/or of their syntactic structure. In this paper, we propose a new mapping validation approach. The approach is based on the notion of probabilistic mappings and on the use of probabilistic reasoning techniques to enforce a semantic interpretation of similarity mappings as probabilistic and hypothetical relations among ontology elements.
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