Davide Mottin

Exemplar queries: give me an example of what you need

Search engines are continuously employing advanced techniques that aim to capture user intentions and provide results that go beyond the data that simply satisfy the query conditions. Examples include the personalized results, related searches, similarity search, popular and relaxed queries. In this work we introduce a novel query paradigm that considers a user query as an example of the data in which the user is interested. We call these queries exemplar queries and claim that they can play an important role in dealing with the information deluge. We provide a formal specification of the semantics of such queries and show that they are fundamentally different from notions like queries by example, approximate and related queries. We provide an implementation of these semantics for graph-based data and present an exact solution with a number of optimizations that improve performance without compromising the quality of the answers. We also provide an approximate solution that prunes the search space and achieves considerably better time-performance with minimal or no impact on effectiveness. We experimentally evaluate the effectiveness and efficiency of these solutions with synthetic and real datasets, and illustrate the usefulness of exemplar queries in practice.

A probabilistic optimization framework for the empty-answer problem

We propose a principled optimization-based interactive query relaxation framework for queries that return no answers. Given an initial query that returns an empty answer set, our framework dynamically computes and suggests alternative queries with less conditions than those the user has initially requested, in order to help the user arrive at a query with a non-empty answer, or at a query for which no matter how many additional conditions are ignored, the answer will still be empty. Our proposed approach for suggesting query relaxations is driven by a novel probabilistic framework based on optimizing a wide variety of application-dependent objective functions. We describe optimal and approximate solutions of different optimization problems using the framework. We analyze these solutions, experimentally verify their efficiency and effectiveness, and illustrate their advantage over the existing approaches.

Graph Query Reformulation with Diversity

We study a problem of graph-query reformulation enabling explorative query-driven discovery in graph databases. Given a query issued by the user, the system, apart from returning the result patterns, also proposes a number of specializations (i.e., supergraphs) of the original query to facilitate the exploration of the results. We formalize the problem of finding a set of reformulations of the input query by maximizing a linear combination of coverage (of the original querys answer set) and diversity among the specializations. We prove that our problem is hard, but also that a simple greedy algorithm achieves a (1/2)-approximation guarantee. The most challenging step of the greedy algorithm is the computation of the specialization that brings the maximum increment to the objective function. To efficiently solve this step, we show how to compute the objective-function increment of a specialization linearly in the number of its results and derive an upper bound that we exploit to devise an efficient search-space visiting strategy. An extensive evaluation on real and synthetic databases attests high efficiency and accuracy of our proposal.

IQR: an interactive query relaxation system for the empty-answer problem

We present IQR, a system that demonstrates optimization based interactive relaxations for queries that return an empty answer. Given an empty answer, IQR dynamically suggests one relaxation of the original query conditions at a time to the user, based on certain optimization objectives, and the user responds by either accepting or declining the relaxation, until the user arrives at a non-empty answer, or a non-empty answer is impossible to achieve with any further relaxations. The relaxation suggestions hinge on a proba- bilistic framework that takes into account the probability of the user accepting a suggested relaxation, as well as how much that relaxation serves towards the optimization objec- tive. IQR accepts a wide variety of optimization objectives - user centric objectives, such as, minimizing the number of user interactions (i.e., effort) or returning relevant results, as well as seller centric objectives, such as, maximizing profit. IQR offers principled exact and approximate solutions for gen- erating relaxations that are demonstrated using multiple, large real datasets.

Searching with XQ: the exemplar query search engine

We demonstrate XQ, a query engine that implements a novel technique for searching relevant information on the web and in various data sources, called Exemplar Queries. While the traditional query model expects the user to provide a set of specifications that the elements of interest need to satisfy, XQ expects the user to provide only an element of interest and we infer the desired answer set based on that element. Through the various examples we demonstrate the functionality of the system and its applicability in various cases. At the same time, we highlight the technical challenges for this type of query answering and illustrate the implementation approach we have materialized. The demo is intended for both researchers and practitioners and aims at illustrating the benefits of the adoption of this new form of query answering in practical applications and the further study and advancement of its technical solutions.

Exemplar queries: a new way of searching

Modern search engines employ advanced techniques that go beyond the structures that strictly satisfy the query conditions in an effort to better capture the user intentions. In this work, we introduce a novel query paradigm that considers a user query as an example of the data in which the user is interested. We call these queries exemplar queries. We provide a formal specification of their semantics and show that they are fundamentally different from notions like queries by example, approximate queries and related queries. We provide an implementation of these semantics for knowledge graphs and present an exact solution with a number of optimizations that improve performance without compromising the result quality. We study two different congruence relations, isomorphism and strong simulation, for identifying the answers to an exemplar query. We also provide an approximate solution that prunes the search space and achieves considerably better time performance with minimal or no impact on effectiveness. The effectiveness and efficiency of these solutions with synthetic and real datasets are experimentally evaluated, and the importance of exemplar queries in practice is illustrated.

New trends on exploratory methods for data analytics

Data usually comes in a plethora of formats and dimensions, rendering the exploration and information extraction processes cumbersome. Thus, being able to cast exploratory queries in the data with the intent of having an immediate glimpse on some of the data properties is becoming crucial. An exploratory query should be simple enough to avoid complicate declarative languages (such as SQL) and mechanisms, and at the same time retain the flexibility and expressiveness of such languages. Recently, we have witnessed a rediscovery of the so called example-based methods, in which the user, or the analyst circumvent query languages by using examples as input. An example is a representative of the intended results, or in other words, an item from the result set. Example-based methods exploit inherent characteristics of the data to infer the results that the user has in mind, but may not able to (easily) express. They can be useful both in cases where a user is looking for information in an unfamiliar dataset, or simply when she is exploring the data without knowing what to find in there. In this tutorial, we present an excursus over the main methods for exploratory analysis, with a particular focus on example-based methods. We show how different data types require different techniques, and present algorithms that are specifically designed for relational, textual, and graph data.

Unleashing the Power of Information Graphs

Information graphs are generic graphs that model different types of information through nodes and edges. Knowledge graphs are the most common type of information graphs in which nodes represent entities and edges represent relationships among them. In this paper, we argue that exploitation of information graphs can lead into novel query answering capabilities that go beyond the existing capabilities of keyword search, and focus on one of them, namely, exemplar queries. Exemplar queries is a recently introduced paradigm that treats a user query as an example from the desired result set. In this paper, we describe the foundations of exemplar queries and the significant role of information graphs, and we present several applications and relevant research directions.

VERSE: Versatile Graph Embeddings from Similarity Measures

Embedding a web-scale information network into a low-dimensional vector space facilitates tasks such as link prediction, classification, and visualization. Past research has addressed the problem of extracting such embeddings by adopting methods from words to graphs, without defining a clearly comprehensible graph-related objective. Yet, as we show, the objectives used in past works implicitly utilize similarity measures among graph nodes. In this paper, we carry the similarity orientation of previous works to its logical conclusion; we propose VERtex Similarity Embeddings (VERSE), a simple, versatile, and memory-efficient method that derives graph embeddings explicitly calibrated to preserve the distributions of a selected vertex-to-vertex similarity measure. VERSE learns such embeddings by training a single-layer neural network. While its default, scalable version does so via sampling similarity information, we also develop a variant using the full information per vertex. Our experimental study on standard benchmarks and real-world datasets demonstrates that VERSE, instantiated with diverse similarity measures, outperforms state-of-the-art methods in terms of precision and recall in major data mining tasks and supersedes them in time and space efficiency, while the scalable sampling-based variant achieves equally good result as the non-scalable full variant.

Graph Exploration: From Users to Large Graphs

The increasing interest in social networks, knowledge graphs, protein-interaction, and many other types of networks has raised the question how users can explore such large and complex graph structures easily. Current tools focus on graph management, graph mining, or graph visualization but lack user-driven methods for graph exploration. In many cases graph methods try to scale to the size and complexity of a real network. However, methods miss user requirements such as exploratory graph query processing, intuitive graph explanation, and interactivity in graph exploration. While there is consensus in database and data mining communities on the definition of data exploration practices for relational and semi-structured data, graph exploration practices are still indeterminate. In this tutorial, we will discuss a set of techniques, which have been developed in the last few years for independent purposes, within a unified graph exploration taxonomy. The tutorial will provide a generalized definition of graph exploration in which the user interacts directly with the system either providing feedback or a partial query. We will discuss common, diverse, and missing properties of graph exploration techniques based on this definition, our taxonomy, and multiple applications for graph exploration. Concluding this discussion we will highlight interesting and relevant challenges for data scientists in graph exploration.