2dSearch: A Visual Approach to Search Strategy Formulation

Knowledge workers (such as healthcare information professionals, patent agents and media monitoring professionals) need to create and execute search strategies that are accurate, repeatable and transparent. The traditional solution is to use lineby-line ‘query builders’ such as those offered by proprietary database vendors. However, these offer limited support for error checking or query optimization, and their output can often be compromised by errors and inefficiencies. In this paper, we present a new approach to query formulation in which concepts are expressed as objects on a two-dimensional canvas. Relationships between objects are articulated by manipulating them using drag and drop. Automated search term suggestions are provided using a combination of knowledge-based and statistical natural language processing techniques. This approach has the potential to eliminate many sources of inefficiency, make the query semantics more transparent, and offers further opportunities for query refinement and optimisation

Database Learning: Toward a Database that Becomes Smarter Every Time

In today's databases, previous query answers rarely benefit answering future queries. For the first time, to the best of our knowledge, we change this paradigm in an approximate query processing (AQP) context. We make the following observation: the answer to each query reveals some degree of knowledge about the answer to another query because their answers stem from the same underlying distribution that has produced the entire dataset. Exploiting and refining this knowledge should allow us to answer queries more analytically, rather than by reading enormous amounts of raw data. Also, processing more queries should continuously enhance our knowledge of the underlying distribution, and hence lead to increasingly faster response times for future queries. We call this novel idea---learning from past query answers---Database Learning. We exploit the principle of maximum entropy to produce answers, which are in expectation guaranteed to be more accurate than existing sample-based approximations. Empowered by this idea, we build a query engine on top of Spark SQL, called Verdict. We conduct extensive experiments on real-world query traces from a large customer of a major database vendor. Our results demonstrate that Verdict supports 73.7% of these queries, speeding them up by up to 23.0x for the same accuracy level compared to existing AQP systems.Comment: This manuscript is an extended report of the work published in ACM SIGMOD conference 201

A Factoid Question Answering System for Vietnamese

In this paper, we describe the development of an end-to-end factoid question answering system for the Vietnamese language. This system combines both statistical models and ontology-based methods in a chain of processing modules to provide high-quality mappings from natural language text to entities. We present the challenges in the development of such an intelligent user interface for an isolating language like Vietnamese and show that techniques developed for inflectional languages cannot be applied "as is". Our question answering system can answer a wide range of general knowledge questions with promising accuracy on a test set.Comment: In the proceedings of the HQA'18 workshop, The Web Conference Companion, Lyon, Franc

Natural language processing

Beginning with the basic issues of NLP, this chapter aims to chart the major research activities in this area since the last ARIST Chapter in 1996 (Haas, 1996), including: (i) natural language text processing systems - text summarization, information extraction, information retrieval, etc., including domain-specific applications; (ii) natural language interfaces; (iii) NLP in the context of www and digital libraries ; and (iv) evaluation of NLP systems

QUERY ANALYSIS FOR TWO-LEVEL SENSOR TOPOLOGIES WITH DATA PROTECTION AND RELIABILITY

The storage nodes, which act as an intermediate layer between the sensors and the sink, can be hacked, allowing attackers to learn sensitive data and process query results.Privacy and integrity were the cornerstones of the application of sensor networks with two levels. Prior schedules for secure query processing are weak because they reveal very little information, so attackers can estimate statistical data based on domain knowledge and the date of query results. In this study we propose the first top-k query processing system that protects the privacy of the sensor data and the integrity of the query results. To maintain privacy, we build an index for each data element collected by the sensor using a semi-random hash function and Blom filters and converting top-k queries to queries in the upper range. To maintain integration, we propose that the data partition algorithm divide each data element into a time interval and associate the partition data with the data. The attached information ensures that the repository can verify the integrity of the query results. We officially show that our software is protected under the IND-CKA security model. Our empirical results from real-life data show that our approach is rigorous and practical for large network size

Event-based hyperspace analogue to language for query expansion

Bag-of-words approaches to information retrieval (IR) are effective but assume independence between words. The Hyperspace Analogue to Language (HAL) is a cognitively motivated and validated semantic space model that captures statistical dependencies between words by considering their co-occurrences in a surrounding window of text. HAL has been successfully applied to query expansion in IR, but has several limitations, including high processing cost and use of distributional statistics that do not exploit syntax. In this paper, we pursue two methods for incorporating syntactic-semantic information from textual ‘events’ into HAL. We build the HAL space directly from events to investigate whether processing costs can be reduced through more careful definition of word co-occurrence, and improve the quality of the pseudo-relevance feedback by applying event information as a constraint during HAL construction. Both methods significantly improve performance results in comparison with original HAL, and interpolation of HAL and relevance model expansion outperforms either method alone