The similarity of words extracted from the rich text relation network is the main way to calculate the semantic similarity. Complex relational information and text content in Wikipedia website, Community Question Answering and social network, provide abundant corpus for semantic similarity calculation. However, most typical research only focused on single relationship. In this paper, we propose a semantic similarity calculation model which integrates multiple relational information, and map multiple relationship to the same semantic space through learning representing matrix and semantic matrix to improve the accuracy of semantic similarity calculation. In experiments, we confirm that the semantic calculation method which integrates many kinds of relationships can improve the accuracy of semantic calculation, compared with other semantic calculation methods.

As a new generation voice service, Voice over LTE (VoLTE) has attracted worldwide attentions in both the academia and industry. Different from the traditional voice call based on circuit-switched (CS), VoLTE evolves into the packet-switched (PS) field, which has long been open to the public. Though designed rigorously, similar to VoIP services, VoLTE also suffers from SIP (Session Initiation Protocal) flooding attacks. Due to the high performance requirement, the SIP flooding attacks in VoLTE is more difficult to defend than that in traditional VoIP service. In this paper, enlightened by Counting Bloom Filter (CBF), we design a versatile CBF-like structure, PFilter, to detect the flooding anomalies. Compared with previous relevant works, our scheme gains advantages in many aspects including detection of low-rate flooding attack and stealthy flooding attack. Moreover, not only can our scheme detect the attacks with high accuracy, but also find out the attackers to ensure normal operation of VoLTE by eliminating their negative effects. Extensive experiments are performed to well evaluate the performance of the proposed scheme.