Search

Hiring seasonal workers in call centers to provide customer service is a common practice in B2C companies. The quality of service delivered by both contracting and employee customer service agents depends heavily on the domain knowledge available to them. When observing the internal group messaging channels used by agents, we found that similar questions are often asked repetitively by different agents, especially from less experienced ones. The goal of our work is to leverage the promising advances in conversational AI to provide a chatbot-like mechanism for assisting agents in promptly resolving a customer's issue. In this paper, we develop a neural-based conversational solution that employs BiLSTM with attention mechanism and demonstrate how our system boosts the effectiveness of customer support agents. In addition, we discuss the design principles and the necessary considerations for our system. We then demonstrate how our system, named "Isa" (Intuit Smart Agent), can help customer service agents provide a high-quality customer experience by reducing customer wait time and by applying the knowledge accumulated from customer interactions in future applications.

Populating ontology graphs represents a long-standing problem for the Semantic Web community. Recent advances in translation-based graph embedding methods for populating instance-level knowledge graphs lead to promising new approaching for the ontology population problem. However, unlike instance-level graphs, the majority of relation facts in ontology graphs come with comprehensive semantic relations, which often include the properties of transitivity and symmetry, as well as hierarchical relations. These comprehensive relations are often too complex for existing graph embedding methods, and direct application of such methods is not feasible. Hence, we propose On2Vec, a novel translation-based graph embedding method for ontology population. On2Vec integrates two model components that effectively characterize comprehensive relation facts in ontology graphs. The first is the Component-specific Model that encodes concepts and relations into low-dimensional embedding spaces without a loss of relational properties; the second is the Hierarchy Model that performs focused learning of hierarchical relation facts. Experiments on several well-known ontology graphs demonstrate the promising capabilities of On2Vec in predicting and verifying new relation facts. These promising results also make possible significant improvements in related methods.

Recent years have witnessed the flourishing increases of micro-blog message applications. Prominent examples include Twitter, Facebook's status, and Sina Weibo in China. Messages in these applications are short (140 characters in a message) and easy to create. The subscription and re-sharing features also make it fairly intuitive to propagate. Micro-blog applications provide abundant information to present world scale user interests and social pulse in an unexpected way. But the precious corpus also brings out the noise and fast changing fragments to prohibit effective understanding and management.In this work, we propose a micro-blog provenance model to capture temporal connections within micro-blog messages. Here, provenance refers to data origin identification and transformation logging, demonstrating of great value in recent database and workflow systems. The provenance model is used to represent the message development trail and changes explicitly. We select various types of connections in micro-blog applications to identify the provenance. To cope with the real time micro-message deluge, we discuss a novel message grouping approach to encode and maintain the provenance information. A summary index structure is utilized to enable efficient provenance updating. We collect in-coming messages and compare them with an in-memory index to associate them with related ones. The closely related messages form some virtual provenance representation in a coarse granularity. We periodically dump memory values onto disks.In the actual implementation, we also introduce several adaptive pruning strategies to extend the potential of provenance discovery efficiency. We use the temporal decaying and granularity levels to filter out low chance messages. In the demonstration, we reveal the usefulness of provenance information for rich query retrieval and dynamic message tracking for effective message organization. The real-time collection approach shows advantages over some baselines. Experiments conducted on a real dataset verify the effectiveness and efficiency of our provenance approach. Results show that the partial-indexing strategy and other restriction ones can maintenance the accuracy at 90% and returning rate at 60% with a reasonable low memory usage. This is the first work towards provenance-based indexing support for micro-blog platforms.

Recently, lots of micro-blog message sharing applications have emerged on the web. Users can publish short messages freely and get notified by the subscriptions instantly. Prominent examples include Twitter, Facebook's statuses, and Sina Weibo in China. The Micro-blog platform becomes a useful service for real time information creation and propagation. However, these messages' short length and dynamic characters have posed great challenges for effective content understanding. Additionally, the noise and fragments make it difficult to discover the temporal propagation trail to explore development of micro-blog messages. In this paper, we propose a provenance model to capture connections between micro-blog messages. Provenance refers to data origin identification and transformation logging, demonstrating of great value in recent database and workflow systems. To cope with the real time micro-message deluge, we utilize a novel message grouping approach to encode and maintain the provenance information. Furthermore, we adopt a summary index and several adaptive pruning strategies to implement efficient provenance updating. Based on the index, our provenance solution can support rich query retrieval and intuitive message tracking for effective message organization. Experiments conducted on a real dataset verify the effectiveness and efficiency of our approach. Provenance refers to data origin identification and transformation monitoring, which has been demonstrated of great value in database and workflow systems. In this paper, we propose a provenance model in micro-blog platforms, and design an indexing scheme to support provenance-based message discovery and maintenance, which can capture the interactions of messages for effective message organization. To cope with the real time micro-message tornadoes, we introduce a novel virtual annotation grouping approach to encode and maintain the provenance information. Furthermore, we design a summary index and adaptive pruning strategies to facilitate efficient message update. Based on this provenance index, our approach can support query and message tracking in micro-blog systems. Experiments conducted on real datasets verify the effectiveness and efficiency of our approach.