Abstract Knowledge graph (KG) entity linkers link entity mentions from a source data representation to their corre sponding entities in a target KG. A knowledge graph (KG) is a popular graph model expressing semantic information about entities, concepts, and relationships. Consequently, entity linking from tables to KGs has become increasingly important, allowing semantic table augmentation and other advanced data integration tasks. However, existing evaluations of entity linkers are incomplete, as they only evaluate for specific applications and only consider aggregated output quality metrics: they do not consider the performance and effectiveness of the individual entity linking components along with their scalability. To address this gap, we provide an in-depth analysis and taxonomy of state of-the-art entity linkers by thoroughly evaluating the quality and scalability of the individual entity linking components. Hence, we evaluate entity linkers on four existing entity linking benchmarks using DBpedia and Wikidata and identify major bottlenecks to be overcome to make these entity linkers fully applicable in real-world use-cases. We identified candidate generation as the most crucial entity linking step, which commonly is overlooked. Furthermore, we show that most entity linkers are irreproducible, either because they are not open-source, or because they use irreproducible public endpoints and datasets. Acknowledgements This research was partially funded by the Danish Council for Independent Research (DFF), grant agreement no. DFF 804800051B, the Poul Due Jensen Fond, and DataGEMS, funded by the European Union’s Horizon Europe Research and Innovation programme, grant agreement no. 101188416.

Abstract Finding relevant tables is challenging and commonly performed using keyword, join, or union search. However, these are not always able to retrieve all the relevant tables since they usually require some form of exact matches or structural similarity. Semantic table search has recently been proposed as a novel technique that exploits knowledge graphs and various forms of semantic similarity for the retrieval of semantically relevant data lake tables. Specifically, semantic table search computes the relevance of a table based on the contained entity mentions without requiring strict structural similarities. This enables the discovery of a much larger set of relevant tables compared to existing approaches. In this paper, we demonstrate Jazero, the first semantic search system for data lakes featuring various semantic similarity metrics. Jazero uses the query-by-example paradigm, where a user provides an example table containing data of interest, enabling entity-centric discovery of data lake tables w.r.t. the query entity tuples. Jazero further enables scalable semantic table search with search space pre-filtering using the popular hierarchical navigable small world index overdifferent vector representations for knowledge graph entities, leading to an average runtime improvement of 87.4%. This corresponds to an average runtime of 3.3s. Jazero allows users to experience this novel discovery paradigm with various entity representations and similarity functions and to perform data discovery in multiple data lake instances. Jazerohas proven to retrieve near-disjoint results to keyword search, whilst retaining the same NDCG ranking performance. Acknowledgements This research was partially funded by the Danish Council for Independent Research (DFF), grant agreement no. DFF 804800051B, the Poul Due Jensen Fond, and DataGEMS, funded by the European Union’s Horizon Europe Research and Innovation programme, grant agreement no. 101188416.

Yelp Open Dataset (YOD) is a widely used dataset for Recommender Systems (RS). Multiple Knowledge Graphs (KGs) have been built for YOD, but they have various issues: the conversion processes usually do not follow state-of-the-art methodologies, fail to properly link to other KGs, do not link to existing vocabularies, ignore important data, and are generally of small size. Instead, we present the Yelp Collaborative Knowledge Graph (YCKG), where we correctly integrating taxonomies, product categories, business locations, and the Yelp social network, through common practices within the semantic web community, overcoming all these issues. As a result, the YCKG includes 150k businesses and 16.9M reviews from 1.9M distinct real users, resulting in over 244 million triples, 144 distinct predicates, for about 72 million resources, with an average in-degree and out-degree of 3.3 and 12.2, respectively. Further, we release both the data and the code used to generate the KG for inspection and further extensions. This dataset can be used to develop and test both recommendation and data-mining algorithms able to exploit rich and semantically meaningful knowledge. We publicize the code for the CKG construction on: https://github.com/MadsCorfixen/The-Yelp-Collaborative-Knowledge-Graph.

In data lakes, one of the core challenges remains finding relevant tables. We introduce the notion of semantic data lakes, i.e., repositories where datasets are linked to concepts and entities described in a knowledge graph (KG). We formalize the problem of semantic table search, i.e., retrieving tables containing information semantically related to a given set of entities, and provide the first formal definition of semantic relatedness of a dataset to tuples of entities. Our solution offers the first general framework to compute the semantic relevance of the contents of a table w.r.t. entity tuples, as well as efficient algorithms (exploiting semantic signals, such as entity types and embeddings) to scale the semantic search to repositories with hundreds of thousands of distinct tables. Our extensive experiments on both real-world and synthetic benchmarks show that our approach is able to retrieve more relevant tables (up to 5.4 times higher recall) in comparison to existing methods while ensuring fast response times (up to 17 times faster with LSH).

Abstract Usually, recommender systems are trained on a set of users and items and then used to recommend new user-item pairings among those seen during training. As users and items are added continuously, there is a pressing need to provide recommendations for new users and items, i.e., for users and items not seen during training. Solutions to this problem exploit techniques like meta-learning or auxiliary information encoded in knowledge graphs to learn an “inductive bias”. Yet, most existing works can either recommend for new users or new items not seen during training but not both. Further, existing methods have rarely been compared to each other. Finally, existing evaluations of these methods use a random split of training data, and thus do not consider temporal splits of ratings in training and testing. This setting ensures testing is correctly performed on user interactions that actually occur after the training period. In this paper, we propose a framework for training and testing the methods on three real world datasets, and perform a deeper analysis of each dataset to better understand the effect of emerging popularity trends. As a result, our re-evaluation of state-of-the-art methods identifies strong architectures and solutions for inductive recommendation. We find that inductive methods that perform aggregation are able to outperform non-aggregating methods in all settings; performances vary greatly across settings, pointing to new important research questions.

Discovering duplicate or high-overlapping tables in table collections is a crucial task for eliminating redundant information, detecting inconsistencies in the evolution of a table across its multiple versions produced over time, and identifying related tables. Candidate duplicate or related tables to support this task can be identified via the estimation of the largest table overlap. Unfortunately, current solutions for finding it present serious scalability issues for heavy workloads: Sloth, the state of-the-art framework for its estimation, requires more than three days of machine time for computing 100k table overlaps. In this paper, we introduce ARMADILLO, an approach based on graph neural networks that learns table embeddings whose cosine similarity approximates the overlap ratio between tables, i.e., the ratio between the area of their largest table overlap and the area of the smaller table in the pair. We also introduce two new annotated datasets based on GitTables and a Wikipedia table corpus containing 1.32 million table pairs overall labeled with their overlap. Evaluating the performance of ARMADILLO on these datasets, we observed that it is able to calculate overlaps between pairs of tables several times faster than the state-of-the-art method while maintaining a good quality in approximating the exact result.

Inductive Recommender Systems are capable of recommending for new users and with new items thus avoiding the need to retrain after new data reaches the system. However, these methods are still trained on all the data available, requiring multiple days to train a single model, without counting hyperparameter tuning. In this work we focus on graph-based recommender systems, i.e., systems that model the data as a heterogeneous network. In other applications, graph sampling allows to study a subgraph and generalize the findings to the original graph. Thus, we investigate the applicability of sampling techniques for this task. We test on three real world datasets, with three state-of-the-art inductive methods, and using six different sampling methods. We find that its possible to maintain performance using only 50% of the training data with up to 86% percent decrease in training time; however, using less training data leads to far worse performance. Further, we find that when it comes to data for recommendations, graph sampling should also account for the temporal dimension. Therefore, we find that if higher data reduction is needed, new graph based sampling techniques should be studied and new inductive methods should be designed.

Property Graphs are a versatile and expressive data model that has gained widespread adoption due to their flexibility in supporting labeled and attributed nodes and edges. They are well-established in research communities and are becoming widespread in companies and organizations across various sectors. They have been boosted by a fervent ISO/IEC standardization activity, leading to dedicated query and schema languages. While the current standards are still evolving, opportunities remain to enrich them with features such as composability. The plethora of existing query languages reflects a rich and diverse ecosystem, which ongoing unification efforts aim to align. This tutorial aims to deepen the understanding of Property Graph standards by showcasing their strengths, highlighting recent unification efforts, clarifying the central role of schema constraints, and exploring the rich landscape of research and industrial opportunities shaping the future of graph data management.

Query Performance Prediction (QPP) is the task of predicting the query runtime performance prior to its execution. While QPP has been studied in relational database systems, it has received little attention for RDF stores, i.e., triplestores that are queried via the SPARQL query language. Existing methods predict the query performance based on the syntactic similarity between a given query and past queries in the query logs. This means that they are not able to generalize to unseen queries with unseen structures or characteristics. We propose a novel GCNN architecture, PlanRGCN, to generalize to unseen queries, fully exploit statistics on the stored KG, and offer more scalable pre-training than the state of the art methods. Furthermore, our architecture is the first to support non-trivial SPARQL operators. In our experiments, we demonstrate both the superior robustness of our prediction method and its practical effect on two downstream tasks: (1) load balancing, achieving a throughput improvement of up to 207% on real-world query logs and (2) execution control, processing up to 70% more queries.

Writing SPARQL queries is often an iterative process, where users refine queries until they meet their information needs. However, long-running query executions can lead to inefficient workflows, as users must wait idly for results — potentially without success due to strict timeouts imposed by public endpoints. In this demo, we present the Smart SPARQL Advisor (SSA), a system that integrates Query Performance Prediction (QPP) to proactively mitigate these issues. By predicting query runtimes prior to execution, SSA alerts users to potentially slow or timeout-prone queries and, when necessary, employs a large language model (LLM) guided by latent representations from the QPP model to suggest alternative query formulations. We demonstrate that SSA enables users to identify performant queries and understand performance bottlenecks, thereby reducing idle time and avoiding unproductive query executions. Through this approach, SSA fosters more responsive and resource-efficient interactions with triplestores, enhancing both user experience and triplestore utilization.

Synthetic data generation (SDG) is the process of generating a new synthetic dataset based on the statistical properties of a confidential existing dataset. Differential privacy is the property of a SDG mechanism that establishes how protected individuals whose sensitive data is part of the confidential dataset are, when sharing such data. To ensure a SDG is differentially private, noise is injected into the statistics learned from the dataset. Depending on the amount of noise injected, we witness a trade-off between privacy and utility. Privacy is then measured via a set of privacy metrics that usually establish a lower bound on a few aspects of the privacy-utility trade-off. Therefore, it is not possible to assess privacy based only on one metric. To close this gap, we demonstrate PrivEval, a tool to assist users in evaluating the privacy properties of a synthetic dataset. PrivEval implements several privacy metrics and validates them on both a single user and the overall dataset. Besides, PrivEval checks assumptions behind each metric. Hence, PrivEval is a first step to bridge the gap between privacy experts and the general public to make privacy estimation more transparent.

Exploratory search on Knowledge Graphs (KGs) arises when a user needs to understand and extract insights from an unfamiliar KG. In these exploratory sessions, the users issue a series of queries to identify relevant portions of the KG that can answer their questions, with each query answer informing the formulation of the next query. Despite the widespread adoption of KGs, the needs of current KG exploration use cases are not well understood. This work presents the “Exploratory Search Workflows” (ESW) collection focusing on real-world exploration sessions of an open-domain KG, Wikidata, conducted by 57 M.Sc. Computer Engineering students in two advanced Graph Database course editions. This resource includes 234 real exploratory workflows, each containing an average of 45 SPARQL queries and reference workflows that serve as gold-standard solutions to the proposed tasks. The ESW collection is also available as an RDF graph and accessible via a public SPARQL endpoint. It allows for analysis of real user sessions, understanding query evolution and complexity, and serves as the first query benchmark for KG management systems for exploratory search.

Datasets play a central role in scholarly communications. However, scholarly graphs are often incomplete, particularly due to the lack of connections between publications and datasets. Therefore, the importance of dataset recommendation—identifying relevant datasets for a scientific paper, an author, or a textual query—is increasing. Although various methods have been proposed for this task, their reproducibility remains unexplored, making it difficult to compare them with new approaches. We reviewed current recommendation methods for scientific datasets, focusing on the most recent and competitive approaches, including an SVM-based model, a bi-encoder retriever, a method leveraging co-authors and citation network embeddings, and a heterogeneous variational graph autoencoder. These approaches underwent a comprehensive analysis under consistent experimental conditions. Our reproducibility efforts show that three methods can be reproduced, while the graph variational autoencoder is challenging due to unavailable code and test datasets. Hence, we re-implemented this method and performed a component-based analysis to examine its strengths and limitations. Furthermore, our study indicated that three out of four considered methods produce subpar results when applied to real-world data instead of specialized datasets with ad-hoc features.

Table search aims to answer a query with a ranked list of tables. Unfortunately, current test corpora have focused mostly on needle-in-the-haystack tasks, where only a few tables are expected to exactly match the query intent. Instead, table search tasks often arise in response to the need for retrieving new datasets or augmenting existing ones, e.g., for data augmentation within data science or machine learning pipelines. Existing table repositories and benchmarks are limited in their ability to test retrieval methods for table search tasks. Thus, to close this gap, we introduce a novel dataset for query-by-example Semantic Table Search. This novel dataset consists of two snapshots of the large-scale Wikipedia tables collection from 2013 and 2019 with two important additions: (1) a page and topic aware ground truth relevance judgment and (2) a large-scale DBpedia entity linking annotation. Moreover, we generate a novel set of entity-centric queries that allows testing existing methods under a novel search scenario: semantic exploratory search. The resulting resource consists of 9,296 novel queries, 610,553 query-table relevance annotations, and 238,038 entity-linked tables from the 2013 snapshot. Similarly, on the 2019 snapshot, the resource consists of 2,560 queries, 958,214 relevance annotations, and 457,714 total tables. This makes our resource the largest annotated table-search corpus to date (97 times more queries and 956 times more annotated tables than any existing benchmark). We perform a user study among domain experts and prove that these annotators agree with the automatically generated relevance annotations. As a result, we can re-evaluate some basic assumptions behind existing table search approaches identifying their shortcomings along with promising novel research directions.

Knowledge Graphs can be encoded using different data models. They are especially abundant using RDF and recently also as property graphs. While knowledge graphs in RDF adhere to the subject-predicate-object structure, property graphs utilize multi-labeled nodes and edges, featuring properties as key/value pairs. Both models are employed in various contexts, thus applications often require transforming data from one model to another. To enhance the interoperability of the two models, we present a novel technique, S3PG, to convert RDF knowledge graphs into property graphs exploiting two popular standards to express schema constraints, i.e., SHACL for RDF and PG-Schema for property graphs. S3PG is the first approach capable of transforming large knowledge graphs to property graphs while fully preserving information and semantics. We have evaluated S3PG on real-world large-scale graphs, showing that, while existing methods exhibit lossy transformations (causing a loss of up to 70% of query answers), S3PG consistently achieves 100% accuracy. Moreover, when considering evolving graphs, S3PG exhibits fully monotonic behavior and requires only a fraction of the time to incorporate changes compared to existing methods.

Due to their expressive power, Knowledge Graphs (KGs) have received increasing interest not only as means to structure and integrate heterogeneous information but also as a native storage format for large amounts of knowledge and statistical data. Therefore, analytical queries over KG data, typically stored as RDF, have become increasingly important. Yet, formulating such queries represents a difficult task for users that are not familiar with the query language (typically SPARQL) and the structure of the dataset at hand. To overcome this limitation, we propose Re2xOLAP: The first comprehensive interactive approach that allows to reverse-engineer and refine RDF exploratory OLAP queries over KGs containing statistical data. Thus, Re2xOLAP enables to perform KG exploratory analytics without requiring the user to write any query at all.We achieve this goal by first reverseengineering analytical SPARQL queries from a small set of userprovided examples and then, given the reverse-engineered query, we propose intuitive and explainable exploratory query refinements to iteratively help the user obtain the desired information. Our experiments on real-world large-scale KGs show that Re2xOLAP can efficiently reverse-engineer analytical SPARQL queries solely based on a small set of input examples. Additionally, we demonstrate the expressive power of our interactive refinement methods by showing that Re2xOLAP allows users to navigate hundreds of thousands of different exploration paths with just a few interactions.

Knowledge Graphs (KGs) represent heterogeneous domain knowledge on the Web and within organizations. There exist shapes constraint languages to define validating shapes to ensure the quality of the data in KGs. Existing techniques to extract validating shapes often fail to extract complete shapes, are not scalable, and are prone to produce spurious shapes. To address these shortcomings, we propose the Quality Shapes Extraction (QSE) approach to extract validating shapes in very large graphs, for which we devise both an exact and an approximate solution. QSE provides information about the reliability of shape constraints by computing their confidence and support within a KG and in doing so allows to identify shapes that are most informative and less likely to be affected by incomplete or incorrect data. To the best of our knowledge, QSE is the first approach to extract a complete set of validating shapes from WikiData. Moreover, QSE provides a 12x reduction in extraction time compared to existing approaches, while managing to filter out up to 93% of the invalid and spurious shapes, resulting in a reduction of up to 2 orders of magnitude in the number of constraints presented to the user, e.g., from 11,916 to 809 on DBpedia.

Large amounts of spatial, textual, and temporal (STT) data are being produced daily. This is data containing an unstructured component (text), a spatial component (geographic position), and a time component (timestamp). Therefore, there is a need for a powerful and general way of analyzing STT data together. In this paper, we define and formalize the Spatio-Textual-Temporal Cube (STTCube) structure to enable combined effective and efficient analytical queries over STT data. Our novel data model over STT objects enables novel joint and integrated STT insights that are hard to obtain using existing methods. Furthermore, our proposed STTCube Incremental Maintenance (IMstt) method maintains the already constructed STTCube efficiently when new data arrives. Moreover, we introduce the new concept of STT measures with associated novel STT-OLAP operators. To allow for efficient large-scale analytics, we present a pre-aggregation framework for exact and approximate computation of STT measures. Our comprehensive experimental evaluation on a real-world Twitter dataset confirms that our proposed methods reduce query response time by 1–5 orders of magnitude compared to the No Materialization baseline and decrease storage cost between 97% and 99.9% compared to the Full Materialization baseline while adding only a negligible overhead in the STTCube construction time. Moreover, approximate computation achieves an accuracy between 90% and 100% while reducing query response time by 3–5 orders of magnitude compared to No Materialization and IMstt achieves an order of magnitude improvement in maintenance time compared to the baseline maintenance method.

Abstract RDF triplestores’ ability to store and query knowledge bases augmented with semantic annotations has attracted the attention of both research and industry. A multitude of systems offer varying data representation and indexing schemes. However, as recently shown for designing data structures, many design choices are biased by outdated considerations and may not result in the most efficient data representation for a given query workload. To overcome this limitation, we identify a novel three-dimensional design space. Within this design space, we map the trade-offs between different RDF data representations employed as part of an RDF triplestore and identify unexplored solutions. We complement the review with an empirical evaluation of ten standard SPARQL benchmarks to examine the prevalence of these access patterns in synthetic and real query workloads. We find some access patterns, to be both prevalent in the workloads and under-supported by existing triplestores. This shows the capabilities of our model to be used by RDF store designers to reason about different design choices and allow a (possibly artificially intelligent) designer to evaluate the fit between a given system design and a query workload.

With the growing popularity of storing data in native RDF, we witness more and more diverse use cases with complex SPARQL queries. As a consequence, query optimization - and in particular cardinality estimation and join ordering - becomes even more crucial. Classical methods exploit global statistics covering the entire RDF graph as a whole, which naturally fails to correctly capture correlations that are very common in RDF datasets, which then leads to erroneous cardinality estimations and suboptimal query execution plans. The alternative of trying to capture correlations in a fine-granular manner, on the other hand, results in very costly preprocessing steps to create these statistics. Hence, in this paper we propose shapes statistics, which extend the recent SHACL standard with statistic information to capture the correlation between classes and properties. Our extensive experiments on synthetic and real data show that shapes statistics can be generated and managed with only little overhead without disadvantages in query runtime while leading to noticeable improvements in cardinality estimation.

Existing data quality works have so far focused on the computation of many data characteristics as a mean of quantifying different quality dimensions, like freshness, consistency, accuracy, or completeness, that are all defined about some ideal (clean) dataset. We claim that this approach falls short in providing a full specification of the quality of the data since it does not take into consideration the task for which the data is to be used, neither any future instances of the dataset. We argue that apart from the difference from the clean dataset, it is equally important to know the degree to which such difference affects the results of the task at hand. Thus, we extend the existing data quality definition to include that degree. Our approach, not only allows data quality to be considered in the context of the intended task, but can also provide useful information even in the absence of the clean dataset, and proffer an understanding of the effect of data quality in future dataset instances. We describe a system and its implementation that computes this extended form of data quality through a principled approach of systematic noise generation and task result evaluation. We perform numerous experiments illustrating the effectiveness of the approach and how this allows contextualizing traditional data quality measures.

We demonstrate a system that enables a data-centric approach in understanding data quality. Instead of directly quantifying data quality as traditionally done, it disrupts the quality of the dataset and monitors the deviations in the output of an analytic task at hand. It computes the correlation factor between the disruption and the deviation and uses it as the quality metric. This allows users to understand not only the quality of their dataset but also the effect that present and future quality issues have to the intended analytic tasks. This is a novel data-centric approach aimed at complementing existing solutions. On top of the new information that it provides, and in contrast to existing techniques of data quality, it neither requires knowledge of the clean datasets, nor of the constraints on which the data should comply.

Analytical 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.

Graph pattern mining aims at identifying structures that appear frequently in large graphs, under the assumption that frequency signifies importance. In real life, there are many graphs with weights on nodes and/or edges. For these graphs, it is fair that the importance (score) of a pattern is determined not only by the number of its appearances, but also by the weights on the nodes/edges of those appearances. Scoring functions based on the weights do not generally satisfy the apriori property, which guarantees that the number of appearances of a pattern cannot be larger than the frequency of any of its sub-patterns, and hence allows faster pruning. Therefore, existing approaches employ other, less efficient, pruning strategies. The problem becomes even more challenging in the case of multiple weighting functions that assign different weights to the same nodes/edges. In this work we propose a new family of scoring functions that respects the apriori property, and thus can rely on effective pruning strategies. We provide efficient and effective techniques for mining patterns in multi-weighted graphs, and we devise both an exact and an approximate solution. In addition, we propose a distributed version of our approach, which distributes the appearances of the patterns to examine among multiple workers. Extensive experiments on both real and synthetic datasets prove that the presence of edge weights and the choice of scoring function affect the patterns mined, and the quality of the results returned to the user. Moreover, we show that, even when the performance of the exact algorithm degrades because of an increasing number of weighting functions, the approximate algorithm performs well and with fairly good quality. Finally, the distributed algorithm proves to be the best choice for mining large and rich input graphs.

Knowledge Graphs (KGs) have been integrated in several models of recommendation to augment the informational value of an item by means of its related entities in the graph. Yet, existing datasets only provide explicit ratings on items and no information is provided about users' opinions of other (non-recommendable) entities. To overcome this limitation, we introduce a new dataset, called the MindReader dataset, providing explicit user ratings both for items and for KG entities. In this first version, the MindReader dataset provides more than 102 thousands explicit ratings collected from 1,174 real users on both items and entities from a KG in the movie domain. This dataset has been collected through an online interview application that we also release as open source. As a demonstration of the importance of this new dataset, we present a comparative study of the effect of the inclusion of ratings on non-item KG entities in a variety of state-of-the-art recommendation models. In particular, we show that most models, whether designed specifically for graph data or not, see improvements in recommendation quality when trained on explicit non-item ratings. Moreover, for some models, we show that non-item ratings can effectively replace item ratings without loss of recommendation quality. This finding, in addition to an observed greater familiarity from users towards certain descriptive entities than movies, motivates the use of KG entities for both warm and cold-start recommendations.

Graphs are everywhere. Personalized Page Rank (PPR) is a particularly important task to support search and exploration within such datasets. PPR computes the proximity between query nodes and other nodes in the graph. This is used, among others, for entity exploration, query expansion, and product recommendation. Graph databases are used for storing knowledge graphs. Unfortunately, the exact computation of PPR is computationally expensive. While different solutions have been proposed to compute PPR values with high precision, these are extremely complex to implement, and in some cases require heavy preprocessing. In this work, we sustain that a better approach exists: particle filtering. Particle filtering methods produce ranks with sufficient precision while exploiting what graph databases architectures are already optimized for: navigating local connections. We present the implementation of such an approach in a popular commercial database and show how this outperforms the already implemented functionality. With this, we aim to motivate future research to optimize and improve upon this research direction.

Abstract Life Cycle Sustainability Analysis (LCSA) studies the complex processes describing product life cycles and their impact on the environment, economy, and society. Effective and transparent sustainability assessment requires access to data from a variety of heterogeneous sources across countries, scientific and ecsonomic sectors, and institutions. Moreover, given their important role for governments and policymakers, the results of many different steps of this analysis should be made freely available, alongside the information about how they have been computed in order to ensure accountability. In this paper, we describe how Semantic Web technologies in general and PROV-O in particular, are used to enable transparent sharing and integration of datasets for LCSA. We describe the challenges we encountered in helping a community of domain experts with no prior expertise in Semantic Web technologies to fully overcome the limitations of their current practice in integrating and sharing open data. This resulted in the first nucleus of an open data repository of information about global production. Furthermore, we describe how we enable domain experts to track the provenance of particular pieces of information that are crucial in higher-level analysis.

We consider the task of exploratory search through graph queries on knowledge graphs. We propose to assist the user by expanding the query with intuitive suggestions to provide a more informative (full) query that can retrieve more detailed and relevant answers. To achieve this result, we propose a model that can bridge graph search paradigms with well-established techniques for information-retrieval. Our approach does not require any additional knowledge from the user and builds on principled language modelling approaches. We empirically show the effectiveness and efficiency of our approach on a large knowledge graph and how our suggestions are able to help build more complete and informative queries.

Exploration 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.

Exploration 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 hopefully leads to answers at least partially relevant and that can provide cues about the next queries to issue. 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, show techniques tailored to different data types, and provide a unifying view of the problem. We show how different data types require different techniques, and present algorithms that are specifically designed for relational, textual, and graph data.

In 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.

Despite the increasing interest in graph databases their requirements and specifications are not yet fully understood by everyone, leading to a great deal of variation in the supported functionalities and the achieved performances. In this work, we provide a comprehensive study of the existing graph database systems. We introduce a novel microbenchmarking framework that provides insights on their performance that go beyond what macro-benchmarks can offer. The framework includes the largest set of queries and operators so far considered. The graph database systems are evaluated on synthetic and real data, from different domains, and at scales much larger than any previous work. The framework is materialized as an open-source suite and is easily extended to new datasets, systems, and queries 1 .

Exploring knowledge graphs can be a daunting task for any user, expert or novice. This is due to the complexity of the schema or because they are unfamiliar with the contents of the data, or even because they do not know precisely what they are looking for. For the same reason there is a significant demand for exploratory methods for this kind of data. We propose X 2 Q, a system that facilitates the exploration of knowledge graphs with a hands-on approach. X 2 Q embodies the flexible multi-exemplar query paradigm, in which easy to express examples serve as the basis for formulating sophisticated, and hard to express queries. Our system helps building examples in an interactive fashion, by showing results of the partial exemplar query as well as suggestions for improving the current examples. Then, the user feedback is incorporated in our scores to filter the irrelevant suggestions upfront. X 2 Q returns answers in real-time on Freebase, one of the largest available knowledge graphs.

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.

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.

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.