Knowledge Graph (KG) contains entities and the relations between entities. Due to its representation ability, KG has been successfully applied to support many medical/healthcare tasks. However, in the medical domain, knowledge holds under certain conditions. Such conditions for medical knowledge are crucial for decision-making in various medical applications, which is missing in existing medical KGs. In this paper, we aim to discovery medical knowledge conditions from texts to enrich KGs. Electronic Medical Records (EMRs) are systematized collection of clinical data and contain detailed information about patients, thus EMRs can be a good resource to discover medical knowledge conditions. Unfortunately, the amount of available EMRs is limited due to reasons such as regularization. Meanwhile, a large amount of medical question answering (QA) data is available, which can greatly help the studied task. However, the quality of medical QA data is quite diverse, which may degrade the quality of the discovered medical knowledge conditions. In the light of these challenges, we propose a new truth discovery method, MedTruth, for medical knowledge condition discovery, which incorporates prior source quality information into the source reliability estimation procedure, and also utilizes the knowledge triple information for trustworthy information computation. We conduct series of experiments on real-world medical datasets to demonstrate that the proposed method can discover meaningful and accurate conditions for medical knowledge by leveraging both EMR and QA data. Further, the proposed method is tested on synthetic datasets to validate its effectiveness under various scenarios.

In this paper, we study the problem of extracting a homogeneous graph from a heterogeneous graph. The key challenges of the extraction problem are how to efficiently enumerate paths matched by the provided line pattern and aggregate values for each pair of vertices from the matched paths. To address above two challenges, we propose a parallel graph extraction framework (PGE), where we use vertex-centric model to enumerate paths and compute aggregate functions in parallel. The framework compiles the line pattern into a path concatenation plan and generates the final weighted edges in a divide-and-conquer manner. The new solution outperforms the state-of-the-art ones through the comprehensive experiments.

In this paper, we propose a personalized dialogue generation system, which combines reinforcement learning techniques with an attention-based hierarchical recurrent encoderdecoder model. Firstly, we incorporate user-specific information into the decoder to capture user's background information and speaking style. Secondly, we employ reinforcement learning techniques to maximize future reward in dialogue, which enables our system to generate topic-coherent, informative and grammatical responses. Moreover, we propose three types of rewards to characterize good conversations. Finally, we compare the performance of the following reinforcement learning methods in dialogue generation: policy gradient, Q-learning, and actor-critic algorithms. We conduct experiments to verify the effectiveness of the proposed model on two dialogue datasets. Experimental results demonstrate that our model can generate better personalized dialogues for different users. Quantitatively, our method achieves better performance than the state-of-the-art dialogue systems in terms of BLEU score, perplexity, and human evaluation.

With the rising importance of knowledge exchange, ontologies have become a key technology in the development of shared knowledge models for semantic-driven applications, such as knowledge interchange and semantic integration. Significant progress has been made in the use of entropy to measure the predictability and redundancy of knowledge bases, particularly ontologies. However, the current entropy applications used to evaluate ontologies consider only single-point connectivity rather than path connectivity, assign equal weights to each entity and path, and assume that vertices are static. To address these deficiencies, the present study proposes a Path-based Text-aware Entropy Computation method, PTEC, by considering the path information between different vertices and the textual information within the path to calculate the connectivity path of the whole network and the different weights between various nodes. Information obtained from structure-based embedding and text-based embedding is multiplied by the connectivity matrix of the entropy computation. An experimental evaluation of three real-world ontologies is performed based on ontology statistical information (data quantity), entropy evaluation (data quality), and a case study (ontology structure and text visualization). These aspects mutually demonstrate the reliability of our method. Experimental results demonstrate that PTEC can effectively evaluate ontologies, particularly those in the medical field.

Ranking question answer pairs has attracted increasing attention recently due to its broad applications such as information retrieval and question answering (QA). Significant progresses have been made by deep neural networks. However, background information and hidden relations beyond the context, which play crucial roles in human text comprehension, have received little attention in recent deep neural networks that achieve the state of the art in ranking QA pairs. In the paper, we propose KABLSTM, a Knowledge-aware Attentive Bidirectional Long Short-Term Memory, which leverages external knowledge from knowledge graphs (KG) to enrich the representational learning of QA sentences. Specifically, we develop a context-knowledge interactive learning architecture, in which a context-guided attentive convolutional neural network (CNN) is designed to integrate knowledge embeddings into sentence representations. Besides, a knowledge-aware attention mechanism is presented to attend interrelations between each segments of QA pairs. KABLSTM is evaluated on two widely-used benchmark QA datasets: WikiQA and TREC QA. Experiment results demonstrate that KABLSTM has robust superiority over competitors and sets state-of-the-art.

Heterogeneous graph is a popular data model to represent the real-world relations with abundant semantics. To analyze heterogeneous graphs, an important step is extracting homogeneous graphs from the heterogeneous graphs, called homogeneous graph extraction. In an extracted homogeneous graph, the relation is defined by a line pattern on the heterogeneous graph and the new attribute values of the relation are calculated by user-defined aggregate functions. The key challenges of the extraction problem are how to efficiently enumerate paths matched by the line pattern and aggregate values for each pair of vertices from the matched paths. To address above two challenges, we propose a parallel graph extraction framework, where we use vertex-centric model to enumerate paths and compute aggregate functions in parallel. The framework compiles the line pattern into a path concatenation plan, which determines the order of concatenating paths and generates the final paths in a divide-and-conquer manner. We introduce a cost model to estimate the cost of a plan and discuss three plan selection strategies, among which the best plan can enumerate paths in O(log)(l) iterations, where l is the length of a pattern. Furthermore, to improve the performance of evaluating aggregate functions, we classify the aggregate functions into three categories, i.e., distributive aggregation, algebraic aggregation, and holistic aggregation. Since the distributive and algebraic aggregations can be computed from the partial paths, we speed up the aggregation by computing partial aggregate values during the path enumeration.

The exponential increase of online videos greatly enriches the life of users but also brings huge numbers of near-duplicate videos (NDVs) that seriously challenge the video websites. The video websites entail NDV-related applications such as detection of copyright violation, video monitoring, video re-ranking, and video recommendation. Since these applications adopt different features and different processing procedures due to diverse scenarios, constructing separate and special-purpose systems for them incurs considerable costs on design, implementation, and maintenance. In this article, we propose a general NDV system on Storm (GVoS)—a popular distributed real-time stream processing platform—to simultaneously support a wide variety of video applications. The generality of GVoS is achieved in two aspects. First, we extract the reusable components from various applications. Second, we conduct the communication between components via a mechanism called Stream Shared Message (SSM) that contains the video-related data. Furthermore, we present an algorithm to reduce the size of SSM in order to avoid the data explosion and decrease the network latency. The experimental results demonstrate that GVoS can achieve performance almost the same as the customized systems. Meanwhile, GVoS accomplishes remarkably higher systematic versatility and efficiently facilitates the development of various NDV-related applications.

Microblogs contain the most up-to-date and abundant opinion information on current events. Aspect-based opinion mining is a good way to get a comprehensive summarization of events. The most popular aspect based opinion mining models are used in the field of product and service. However, existing models are not suitable for event mining. In this paper we propose a novel probabilistic generative model (ASEM) to simultaneously discover aspects and the specified opinions. ASEM incorporate a sequence labeling model(CRF) into a generative topic model. Additionally, we adopt a set of features for separating aspects and sentiments. Moreover, we novelly present a continuously learning model. It can utilize the knowledge of one event to learn another, and get a better performance. We use five real world events to do experiment. The experimental results show that ASEM extracts aspects and sentiments well, and ASEM outperforms other state-of-art models and the intuitive two-step method.

The explosion of big data stored in distributed file systems calls for more efficient storage paradigms. While replication is widely used to ensure data availability, erasure codes provide a much better tradeoff between storage and availability. Reed-Solomon (RS) codes are the standard design choice, however, their high repair cost is often considered an unavoidable price to pay for high storage efficiency and high reliability. BASIC codes can achieve the optimal tradeoff between storage capacity and repair bandwidth with much less complexity of regenerating codes, which is first proposed in [1]. This paper integrate one construction of the minimum storage BASIC (MS-BASIC) codes [2] into a Hadoop HDFS cluster testbed with up to 22 storage nodes. We demonstrate that MS-BASIC codes conform to the theoretical findings and achieve recovery bandwidth saving compared to the conventional recovery approach based on RS codes.