An Improved Entity Similarity Measurement Method

To facilitate the integration of learning resources categorized under different ontology representations, the techniques of ontology mapping can be applied. Through many algorithms and systems have been proposed for ontology mapping, they do not have an automatic weighting strategy on class features to automate the ontology mapping process. A novel method of computing the feature weights is proposed by feature semantic analysis, defining characteristics of the different entities similarity calculation model and weight calculation model. The results show that it makes the ontology mapping process more automatic while retaining satisfying accuracy. Improve ontology mapping effectiveness

A comprehensive analysis of Fermi Gamma-Ray Burst Data: IV. Spectral lag and Its Relation to Ep Evolution

The spectral evolution and spectral lag behavior of 92 bright pulses from 84 gamma-ray bursts (GRBs) observed by the Fermi GBM telescope are studied. These pulses can be classified into hard-to-soft pulses (H2S, 64/92), H2S-dominated-tracking pulses (21/92), and other tracking pulses (7/92). We focus on the relationship between spectral evolution and spectral lags of H2S and H2S-dominated-tracking pulses. %in hard-to-soft pulses (H2S, 64/92) and H2S-dominating-tracking (21/92) pulses. The main trend of spectral evolution (lag behavior) is estimated with $\log E_p\propto k_E\log(t+t_0)$ (${\hat{\tau}} \propto k_{\hat{\tau}}\log E$), where $E_p$ is the peak photon energy in the radiation spectrum, $t+t_0$ is the observer time relative to the beginning of pulse $-t_0$, and ${\hat{\tau}}$ is the spectral lag of photons with energy $E$ with respect to the energy band $8$-$25$ keV. For H2S and H2S-dominated-tracking pulses, a weak correlation between $k_{{\hat{\tau}}}/W$ and $k_E$ is found, where $W$ is the pulse width. We also study the spectral lag behavior with peak time $t_{\rm p_E}$ of pulses for 30 well-shaped pulses and estimate the main trend of the spectral lag behavior with $\log t_{\rm p_E}\propto k_{t_p}\log E$. It is found that $k_{t_p}$ is correlated with $k_E$. We perform simulations under a phenomenological model of spectral evolution, and find that these correlations are reproduced. We then conclude that spectral lags are closely related to spectral evolution within the pulse. The most natural explanation of these observations is that the emission is from the electrons in the same fluid unit at an emission site moving away from the central engine, as expected in the models invoking magnetic dissipation in a moderately-high-$\sigma$ outflow.Comment: 58 pages, 11 figures, 3 tables. ApJ in pres