Literature reading notes (c)

2015 Information Fusion_Faceted fusion of

RDF data organize notes

 

First, the paper organize your thoughts flow

1.1 Related research papers

1. RDF distance matching fusion algorithm
2. Fusion Algorithm Based on RDF inline
3. Rule-based restrictions RDF fusion algorithm
4. RDF data fragmentation: RDF data specific topics dispersed in a number of different data sets, each data set of RDF data that contains only one aspect of the subject matter.
5. The dispersion of the RDF data integrated into the same subject in different ways according to
6. RDF constructed from the results returned by search engines in TRG (specific topic RDF graph) and then use the RDF segmentation algorithm discovered a series of aspects.
7. Using RDF segmentation algorithm discovered a series of aspects.
8. Analysis for TRG: In the TRG, a node is o or s triad, while p is the corresponding triad, and both ends of one side vertices of a triplet. TRG is divided into two types of edges, and a reflection of the relationship between s o and the other reflects a relationship between two s. FIG TRG may be considered only between the edges of the structure is divided s FIG.
9. On the data set used: a method of obtaining the field relating to the computer six data sets crawling data from the network, the first aspect of manual annotation each topic for later comparison
10. FIG respect TRG obtained: found that the average degree of each node is the node 2 to 3.5,98% isolated; furthermore two vertices majority (96%) is connected to a point to the same aspect. Further Jaccard similarity calculated similarity score higher in accordance with the two nodes may point to the same node (thereby set a threshold value).
11. Aspect of the discovery algorithm: RDF graph in FIG sub-divided into k disjoint, then the neighboring nodes according to similarity and homogeneity homogeneity node, TRG and is divided into data configuration diagram of FIG; data calculating a similarity according to FIG. series aspects referred ones; if in a side structure of FIG link two subgraphs, and two vertices belong to different aspects, it is possible to merge these two aspects.
12. RDF discovery algorithm specific aspects of the process, see notebook

1.2 thesis problem

Papers problem-solving process 1.3

 

1.4 Experimental methods used paper

1. About manual annotation: labeling rules given first, then two independent label with a theme, if the two conflict marked the emergence submitted to a third party marked and labeled as the final result.
2. Conformity assessment: aspects for cell phones and manual annotation algorithm found set are compared using the contrast index NMI
3. Evaluation found aspects: use of precision, recall, and Fl indicators evaluated before mentioned three methods are compared. Also evaluated between algorithms and manual annotation label
4. FF proposed method than the previously mentioned three methods more effective (TRG considering the topological properties, and taking into account the degree of similarity and the topological properties)
5. In F1, precision and recall these three indicators, six thematic aspects of extraction fusion, FF three methods are superior to the foregoing.
6. The resolution and physical integration FF
7. The FF integrate extended to other areas

The final evaluation of the results of experiments 1.5

1.6 follow-up paper

Second, the paper innovation

1. Finally, some TRG Discovery Algorithm
2. We found neighboring nodes and homogeneity homogeneity TRG similarity node topological properties of these two
3. FF algorithm taking into account these two properties

Third, the proposed reference literature reading

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