A Hierarchical Recurrent Encoder-Decoder For Generative Context-Aware Query Suggestion

Users may strive to formulate an adequate textual query for their information need. Search engines assist the users by presenting query suggestions. To preserve the original search intent, suggestions should be context-aware and account for the previous queries issued by the user. Achieving context awareness is challenging due to data sparsity. We present a probabilistic suggestion model that is able to account for sequences of previous queries of arbitrary lengths. Our novel hierarchical recurrent encoder-decoder architecture allows the model to be sensitive to the order of queries in the context while avoiding data sparsity. Additionally, our model can suggest for rare, or long-tail, queries. The produced suggestions are synthetic and are sampled one word at a time, using computationally cheap decoding techniques. This is in contrast to current synthetic suggestion models relying upon machine learning pipelines and hand-engineered feature sets. Results show that it outperforms existing context-aware approaches in a next query prediction setting. In addition to query suggestion, our model is general enough to be used in a variety of other applications.Comment: To appear in Conference of Information Knowledge and Management (CIKM) 201

Generating recommendations for entity-oriented exploratory search

We introduce the task of recommendation set generation for entity-oriented exploratory search. Given an input search query which is open-ended or under-specified, the task is to present the user with an easily-understandable collection of query recommendations, with the goal of facilitating domain exploration or clarifying user intent. Traditional query recommendation systems select recommendations by identifying salient keywords in retrieved documents, or by querying an existing taxonomy or knowledge base for related concepts. In this work, we build a text-to-text model capable of generating a collection of recommendations directly, using the language model as a "soft" knowledge base capable of proposing new concepts not found in an existing taxonomy or set of retrieved documents. We train the model to generate recommendation sets which optimize a cost function designed to encourage comprehensiveness, interestingness, and non-redundancy. In thorough evaluations performed by crowd workers, we confirm the generalizability of our approach and the high quality of the generated recommendations

Graph Processing in Main-Memory Column Stores

Evermore, novel and traditional business applications leverage the advantages of a graph data model, such as the offered schema flexibility and an explicit representation of relationships between entities. As a consequence, companies are confronted with the challenge of storing, manipulating, and querying terabytes of graph data for enterprise-critical applications. Although these business applications operate on graph-structured data, they still require direct access to the relational data and typically rely on an RDBMS to keep a single source of truth and access. Existing solutions performing graph operations on business-critical data either use a combination of SQL and application logic or employ a graph data management system. For the first approach, relying solely on SQL results in poor execution performance caused by the functional mismatch between typical graph operations and the relational algebra. To the worse, graph algorithms expose a tremendous variety in structure and functionality caused by their often domain-specific implementations and therefore can be hardly integrated into a database management system other than with custom coding. Since the majority of these enterprise-critical applications exclusively run on relational DBMSs, employing a specialized system for storing and processing graph data is typically not sensible. Besides the maintenance overhead for keeping the systems in sync, combining graph and relational operations is hard to realize as it requires data transfer across system boundaries. A basic ingredient of graph queries and algorithms are traversal operations and are a fundamental component of any database management system that aims at storing, manipulating, and querying graph data. Well-established graph traversal algorithms are standalone implementations relying on optimized data structures. The integration of graph traversals as an operator into a database management system requires a tight integration into the existing database environment and a development of new components, such as a graph topology-aware optimizer and accompanying graph statistics, graph-specific secondary index structures to speedup traversals, and an accompanying graph query language. In this thesis, we introduce and describe GRAPHITE, a hybrid graph-relational data management system. GRAPHITE is a performance-oriented graph data management system as part of an RDBMS allowing to seamlessly combine processing of graph data with relational data in the same system. We propose a columnar storage representation for graph data to leverage the already existing and mature data management and query processing infrastructure of relational database management systems. At the core of GRAPHITE we propose an execution engine solely based on set operations and graph traversals. Our design is driven by the observation that different graph topologies expose different algorithmic requirements to the design of a graph traversal operator. We derive two graph traversal implementations targeting the most common graph topologies and demonstrate how graph-specific statistics can be leveraged to select the optimal physical traversal operator. To accelerate graph traversals, we devise a set of graph-specific, updateable secondary index structures to improve the performance of vertex neighborhood expansion. Finally, we introduce a domain-specific language with an intuitive programming model to extend graph traversals with custom application logic at runtime. We use the LLVM compiler framework to generate efficient code that tightly integrates the user-specified application logic with our highly optimized built-in graph traversal operators. Our experimental evaluation shows that GRAPHITE can outperform native graph management systems by several orders of magnitude while providing all the features of an RDBMS, such as transaction support, backup and recovery, security and user management, effectively providing a promising alternative to specialized graph management systems that lack many of these features and require expensive data replication and maintenance processes

Trie-NLG: Trie Context Augmentation to Improve Personalized Query Auto-Completion for Short and Unseen Prefixes

Query auto-completion (QAC) aims at suggesting plausible completions for a given query prefix. Traditionally, QAC systems have leveraged tries curated from historical query logs to suggest most popular completions. In this context, there are two specific scenarios that are difficult to handle for any QAC system: short prefixes (which are inherently ambiguous) and unseen prefixes. Recently, personalized Natural Language Generation (NLG) models have been proposed to leverage previous session queries as context for addressing these two challenges. However, such NLG models suffer from two drawbacks: (1) some of the previous session queries could be noisy and irrelevant to the user intent for the current prefix, and (2) NLG models cannot directly incorporate historical query popularity. This motivates us to propose a novel NLG model for QAC, Trie-NLG, which jointly leverages popularity signals from trie and personalization signals from previous session queries. We train the Trie-NLG model by augmenting the prefix with rich context comprising of recent session queries and top trie completions. This simple modeling approach overcomes the limitations of trie-based and NLG-based approaches and leads to state-of-the-art performance. We evaluate the Trie-NLG model using two large QAC datasets. On average, our model achieves huge ~57% and ~14% boost in MRR over the popular trie-based lookup and the strong BART-based baseline methods, respectively. We make our code publicly available.Comment: Accepted at Journal Track of ECML-PKDD 202

User Interfaces to the Web of Data based on Natural Language Generation

We explore how Virtual Research Environments based on Semantic Web technologies support research interactions with RDF data in various stages of corpus-based analysis, analyze the Web of Data in terms of human readability, derive labels from variables in SPARQL queries, apply Natural Language Generation to improve user interfaces to the Web of Data by verbalizing SPARQL queries and RDF graphs, and present a method to automatically induce RDF graph verbalization templates via distant supervision

A Study Of Data Informatics: Data Analysis And Knowledge Discovery Via A Novel Data Mining Algorithm

Frequent pattern mining (fpm) has become extremely popular among data mining researchers because it provides interesting and valuable patterns from large datasets. The decreasing cost of storage devices and the increasing availability of processing power make it possible for researchers to build and analyze gigantic datasets in various scientific and business domains. A filtering process is needed, however, to generate patterns that are relevant. This dissertation contributes to addressing this need. An experimental system named fpmies (frequent pattern mining information extraction system) was built to extract information from electronic documents automatically. Collocation analysis was used to analyze the relationship of words. Template mining was used to build the experimental system which is the foundation of fpmies. With the rising need for improved environmental performance, a dataset based on green supply chain practices of three companies was used to test fpmies. The new system was also tested by users resulting in a recall of 83.4%. The new algorithm\u27s combination of semantic relationships with template mining significantly improves the recall of fpmies. The study\u27s results also show that fpmies is much more efficient than manually trying to extract information. Finally, the performance of the fpmies system was compared with the most popular fpm algorithm, apriori, yielding a significantly improved recall and precision for fpmies (76.7% and 74.6% respectively) compared to that of apriori (30% recall and 24.6% precision)

Algorithms for Analyzing and Mining Real-World Graphs

This thesis is about algorithms for analyzing large real-world graphs (or networks). Examples include (online) social networks, webgraphs, information networks, biological networks and scientific collaboration and citation networks. Although these graphs differ in terms of what kind of information the objects and relationships represent, it turns out that the structure of each these networks is surprisingly similar.For computer scientists, there is an obvious challenge to design efficient algorithms that allow large graphs to be processed and analyzed in a practical setting, facing the challenges of processing millions of nodes and billions of edges. Specifically, there is an opportunity to exploit the non-random structure of real-world graphs to efficiently compute or approximate various properties and measures that would be too hard to compute using traditional graph algorithms. Examples include computation of node-to-node distances and extreme distance measures such as the exact diameter and radius of a graph.NWOAlgorithms and the Foundations of Software technolog