75 research outputs found
Preface to the 2nd Workshop on Search, Exploration, and Analysis in Heterogeneous Datastores
No full textPreface to the 2nd Workshop on Search, Exploration, and Analysis in Heterogeneous Datastores Summary: There were 6 research papers accepted for this volume. Moreover, 6 poster papers where also presented and included in this volume
SOFOS: Demonstrating the Challenges of Materialized View Selection on Knowledge Graphs
No full textAnalytical queries over RDF data are becoming prominent as a result of the proliferation of knowledge graphs. Yet, RDF databases are not optimized to perform such queries efficiently, leading to long processing times. A well known technique to improve the performance of analytical queries is to exploit materialized views.Although popular in relational databases, view materialization for RDF and SPARQL has not yet transitioned into practice, due to the non-trivial application to the RDF graph model. Motivated by a lack of understanding of the impact of view materialization alternatives for RDF data, we demonstrate Sofos, a system that implements and compares several cost models for view materialization. Sofos is, to the best of our knowledge, the first attempt to adapt cost models, initially studied in relational data, to the generic RDF setting, and to propose new ones, analyzing their pitfalls and merits. Sofos takes an RDF dataset and an analytical query for some facet in the data, and compares and evaluates alternative cost models, displaying statistics and insights about time, memory consumption, and query characteristics.</p
Data for: Autonomous Micro-Focus Angle-Resolved Photoemission Spectroscopy
No full text<p>This repository contains the data related to the publication</p>
<p>Steinn Ýmir Ágústsson, Alfred J. H. Jones, Davide Curcio, Søren Ulstrup, Jill Miwa, Davide Mottin, Panagiotis Karras, Philip Hofmann; <strong>Autonomous micro-focus angle-resolved photoemission spectroscopy</strong>. <em>Rev. Sci. Instrum.</em> 1 May 2024; <strong>95</strong> (<em>5</em>): 055106.<em> DOI: <a href="https://doi.org/10.1063/5.0204663" target="_blank" rel="noopener">10.1063/5.0204663</a></em></p>
<p>Please cite the paper above in case of re-use of these data in a scientific publication.</p>
<p>The data were acquired at the SGM4 beamline of the ASTRID2 synchrotron in Arhus, DK as part of the development of an autonomous data acquisition software "SmartScan". Such software, together with all scripts necessary to load the present data, is available on GitHub at <a href="https://github.com/ARPES-ASTRID/smartscan">github.com/ARPES-ASTRID/smartscan</a></p>
Unleashing the Power of Information Graphs
No full textInformation graphs are generic graphs that model dif-ferent types of information through nodes and edges. Knowledge graphs are the most common type of in-formation graphs in which nodes represent entities and edges represent relationships among them. In this pa-per, 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. Ex-emplar queries is a recently introduced paradigm that treats a user query as an example from the desired re-sult 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. 1
Exemplar queries: Give me an example of what you need
No full textSearch engines are continuously employing advanced tech-niques that aim to capture user intentions and provide re-sults that go beyond the data that simply satisfy the query conditions. Examples include the personalized results, re-lated searches, similarity search, popular and relaxed queries. In this work we introduce a novel query paradigm that con-siders 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 specifi-cation of the semantics of such queries and show that they are fundamentally different from notions like queries by ex-ample, approximate and related queries. We provide an im-plementation of these semantics for graph-based data and present an exact solution with a number of optimizations that improve performance without compromising the qual-ity of the answers. We also provide an approximate solution that prunes the search space and achieves considerably bet-ter time-performance with minimal or no impact on effec-tiveness. We experimentally evaluate the effectiveness and efficiency of these solutions with synthetic and real datasets, and illustrate the usefulness of exemplar queries in practice. 1
ReliK: A Reliability Measure for Knowledge Graph Embeddings
No full textCan we assess a priori how well a knowledge graph embedding will perform on a specific downstream task and in a specific part of the knowledge graph? Knowledge graph embeddings (KGEs) represent entities (e.g., “da Vinci,” “Mona Lisa”) and relationships (e.g., “painted”) of a knowledge graph (KG) as vectors. KGEs are generated by optimizing an embedding score, which assesses whether a triple (e.g., “da Vinci,” “painted,” “Mona Lisa”) exists in the graph. KGEs have been proven effective in a variety of web-related downstream tasks, including, for instance, predicting relationships among entities. However, the problem of anticipating the performance of a given KGE in a certain downstream task and locally to a specific individual triple, has not been tackled so far. In this paper, we fill this gap with ReliK, a Reliability measure for KGEs. ReliK relies solely on KGE embedding scores, is task- and KGE-agnostic, and requires no further KGE training. As such, it is particularly appealing for semantic web applications which call for testing multiple KGE methods on various parts of the KG and on each individual downstream task. Through extensive experiments, we attest that ReliK correlates well with both common downstream tasks, such as tail or relation prediction and triple classification, as well as advanced downstream tasks, such as rule mining and question answering, while preserving locality
Multi-Example Search in Rich Information Graphs
No full textIn rich information spaces, it is often hard for users to formally specify the characteristics of the desired answers, either due to the complexity of the schema or of the query language, or even because they do not know exactly what they are looking for. Exemplar queries constitute a query paradigm that overcomes those problems, by allowing users to provide examples of the elements of interest in place of the query specification. In this paper, we propose a general approach where the user-provided example can comprise several partial specification fragments, where each fragment describes only one part of the desired result. We provide a formal definition of the problem, which generalizes existing formulations for both the relational and the graph model. We then describe exact algorithms for its solution for the case of information graphs, as well as top-k algorithms. Experiments on large real datasets demonstrate the effectiveness and efficiency of the proposed approach
Example-based Search:a New Frontier for Exploratory Search
No full textExploration is one of the primordial ways to accrue knowledge about the world and its nature. As we accumulate, mostly automatically, data at unprecedented volumes and speed, our datasets have become complex and hard to understand. In this context, exploratory search provides a handy tool for progressively gather the necessary knowledge by starting from a tentative query that can provide cues about the next queries to issue. An exploratory query should be simple enough to avoid complicate declarative languages (such as SQL) and convoluted mechanism, and at the same time retain the flexibility and expressiveness required to express complex information needs. 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. This shift in semantics has led to a number of methods receiving as query a set of example members of the answer set. The search system then infers the entire answer set based on the given examples and any additional information provided by the underlying database. In this tutorial, we present an excursus over the main example-based methods for exploratory analysis. We show how different data types require different techniques, and present algorithms that are specifically designed for relational, textual, and graph data. We conclude by providing a unifying view of this query-paradigm and identify new exciting research directions.</p
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