Question answering over knowledge graphs (QAKG) involves interpreting natural language questions and linking them to structured knowledge graphs. Existing benchmark datasets (e.g., QALD, LC-QuAD) are limited in size and annotation, hindering QAKG model generalization. To address this, we present QueryBridge, a dataset with over one million annotated questions paired with SPARQL queries. Each question is tagged with essential elements (e.g., entities, relationships) and annotated by query shape (e.g., chain, star) to support complex reasoning.

Cold-start recommendation, which addresses the challenge of recommending items to new users without sufficient historical interaction data, remains difficult in personalized recommender systems. Cross-domain recommendation methods have gained attention in addressing this issue by transferring user preferences from a source domain to alleviate the cold-start problem in a target domain. Traditional embedding-based approaches typically rely on a one-to-one alignment over continuous user embeddings using overlapping user IDs, which often leads to severe overfitting and limited generalization, particularly when overlapping users between domains are sparse. In this paper, we propose a novel hierarchical recommendation framework specifically targeting the cold-start problem by modeling user preferences as hierarchical interests derived from textual embeddings and Residual Quantized Variational Autoencoders (RQ-VAE). Unlike traditional methods that directly align embeddings, our approach builds a mapping function transferring hierarchical user interest structures from the source to the target domain. This hierarchical mapping significantly enhances generalization, providing a more comprehensive and robust representation of user preferences. Additionally, we employ a generative rewriting mechanism utilizing Large Language Models to refine user-generated reviews into concise, semantically enriched summaries that explicitly highlight user interests. Extensive evaluations on Amazon review datasets demonstrate the effectiveness of our hierarchical preference modeling and generative rewriting approach, outperforming existing embedding-based methods consistently, especially in cold-start and sparsely populated scenarios. Our proposed method thus provides a robust, flexible solution for personalized recommendation tasks in cold-start conditions.

As data in the telecommunications industry becomes more voluminous and complex, extracting insightful information requires efficient and scalable systems that can effectively link and manage this data. This paper introduces a novel, multi-layered approach to managing interlinked data for Cloud Radio Access Network (CloudRAN) at Ericsson, utilizing Knowledge Graphs (KGs). Our system is structured into six distinct layers, each focusing on a specific aspect of managing interlinked data. This division enhances clarity and manageability, and promotes effective teamwork and collaborative development. A cornerstone of our architecture is its modularity, which enables the flexible exchange of components, such as the triple store, with minimal impact on the system's operations, ensuring longevity and adaptability to evolving technological trends. Moreover, we introduce novel applications in knowledge graph summarization and semantic search, specifically engineered for industrial decision-making. These innovations provide concise insights and actionable intelligence, fostering rapid and informed decision-making processes crucial for industry professionals. Finally, we discuss the lessons learned from deploying and utilizing this six-layer framework.

Recently, there has been an upsurge in the number of knowledge graphs (KG) that can only be accessed by experts. Non-expert users lack an adequate understanding of the queried knowledge graph's vocabulary and structure, as well as the syntax of the structured query language used to express the user's information needs. To increase the user base of these KGs, a set of Question Answering (QA) systems that use natural language to query these knowledge graphs have been introduced. However, finding a benchmark that accurately evaluates the quality of a QA system is a difficult task due to (1) the high degree of variation in the fine-grained properties among the existing benchmarks, (2) the static nature of the existing benchmarks versus the evolving nature of KGs, and (3) the limited number of KGs targeted by existing benchmarks, which hinders the usability of QA systems in real-world deployment over KGs that are different from those that were used in the evaluation of the QA systems. In this paper, we introduce Maestro, a benchmark generation system for question answering over knowledge graphs. Maestro can generate a new benchmark for any KG given the KG and, optionally, a text corpus that covers this KG. The benchmark generated by Maestro is guaranteed to cover all the properties of the natural language questions and queries that were encountered in the literature as long as the targeted KG includes these properties. Maestro also generates high-quality natural language questions with various utterances that are on par with manually-generated ones to better evaluate QA systems.

Multi-domain text classification (MDTC) aims to leverage all available resources from multiple domains to learn a predictive model that can generalize well on these domains. Recently, many MDTC methods adopt adversarial learning, shared-private paradigm, and entropy minimization to yield state-of-the-art results. However, these approaches face three issues: (1) Minimizing domain divergence can not fully guarantee the success of domain alignment; (2) Aligning marginal feature distributions can not fully guarantee the discriminability of the learned features; (3) Standard entropy minimization may make the predictions on unlabeled data over-confident, deteriorating the discriminability of the learned features. In order to address the above issues, we propose a co-regularized adversarial learning (CRAL) mechanism for MDTC. This approach constructs two diverse shared latent spaces, performs domain alignment in each of them, and punishes the disagreements of these two alignments with respect to the predictions on unlabeled data. Moreover, virtual adversarial training (VAT) with entropy minimization is incorporated to impose consistency regularization to the CRAL method. Experiments show that our model outperforms state-of-the-art methods on two MDTC benchmarks.

Multi-domain text classification (MDTC) has obtained remarkable achievements due to the advent of deep learning. Recently, many endeavors are devoted to applying adversarial learning to extract domain-invariant features to yield state-of-the-art results. However, these methods still face one challenge: transforming original features to be domain-invariant distorts the distributions of the original features, degrading the discriminability of the learned features. To address this issue, we first investigate the structure of the batch classification output matrix and theoretically justify that the discriminability of the learned features has a positive correlation with the Frobenius norm of the batch output matrix. Based on this finding, we propose a maximum batch Frobenius norm (MBF) method to boost the feature discriminability for MDTC. Experiments on two MDTC benchmarks show that our MBF approach can effectively advance state-of-the-art performance.

In recent years, a significant number of question answering (QA) systems that retrieve answers to natural language questions from knowledge graphs (KG) have been introduced. However, finding a benchmark that accurately evaluates the quality of a question answering system is a difficult task because of (1) the high degree of variations with respect to the fine-grained properties among the available benchmarks, (2) the static nature of the available benchmarks versus the evolving nature of KGs, and (3) the limited number of KGs targeted by existing benchmarks, which hinders the usability of QA systems in real deployment over KGs that are different from those which the QA system was evaluated using. In this demonstration, we introduce SmartBench, an automatic benchmark generating system for QA over any KG. The benchmark generated by SmartBench is guaranteed to cover all the properties of the natural language questions and queries that were encountered in the literature as long as the targeted KG includes these properties.

Using the shared-private paradigm and adversarial training can significantly improve the performance of multi-domain text classification (MDTC) models. However, there are two issues for the existing methods: First, instances from the multiple domains are not sufficient for domain-invariant feature extraction. Second, aligning on the marginal distributions may lead to a fatal mismatch. In this paper, we propose mixup regularized adversarial networks (MRANs) to address these two issues. More specifically, the domain and category mixup regularizations are introduced to enrich the intrinsic features in the shared latent space and enforce consistent predictions in-between training instances such that the learned features can be more domain-invariant and discriminative. We conduct experiments on two benchmarks: The Amazon review dataset and the FDU-MTL dataset. Our approach on these two datasets yields average accuracies of 87.64% and 89.0% respectively, outperforming all relevant baselines.

A plethora of question answering (QA) systems that retrieve answers to natural language questions from knowledge graphs have been developed in recent years. However, choosing a benchmark to accurately assess the quality of a question answering system is a challenging task due to the high degree of variations among the available benchmarks with respect to their fine-grained properties. In this demonstration, we introduce CBench, an extensible, and more informative benchmarking suite for analyzing benchmarks and evaluating QA systems. CBench can be used to analyze existing benchmarks with respect to several fine-grained linguistic, syntactic, and structural properties of the questions and queries in the benchmarks. Moreover, CBench can be used to facilitate the evaluation of QA systems using a set of popular benchmarks that can be augmented with other user-provided benchmarks. CBench not only evaluates a QA system based on popular single-number metrics but also gives a detailed analysis of the linguistic, syntactic, and structural properties of answered and unanswered questions to help the developers of QA systems to better understand where their system excels and where it struggles.

Recently, there has been an increase in the number of knowledge graphs that can be only queried by experts. However, describing questions using structured queries is not straightforward for non-expert users who need to have sufficient knowledge about both the vocabulary and the structure of the queried knowledge graph, as well as the syntax of the structured query language used to describe the user's information needs. The most popular approach introduced to overcome the aforementioned challenges is to use natural language to query these knowledge graphs. Although several question answering benchmarks can be used to evaluate question-answering systems over a number of popular knowledge graphs, choosing a benchmark to accurately assess the quality of a question answering system is a challenging task. In this paper, we introduce CBench, an extensible, and more informative benchmarking suite for analyzing benchmarks and evaluating question answering systems. CBench can be used to analyze existing benchmarks with respect to several fine-grained linguistic, syntactic, and structural properties of the questions and queries in the benchmark. We show that existing benchmarks vary significantly with respect to these properties deeming choosing a small subset of them unreliable in evaluating QA systems. Until further research improves the quality and comprehensiveness of benchmarks, CBench can be used to facilitate this evaluation using a set of popular benchmarks that can be augmented with other user-provided benchmarks. CBench not only evaluates a question answering system based on popular single-number metrics but also gives a detailed analysis of the linguistic, syntactic, and structural properties of answered and unanswered questions to better help the developers of question answering systems to better understand where their system excels and where it struggles.

One of the main challenges in large-scale data integration for relational schemas is creating an accurate mediated schema, and generating accurate semantic mappings between heterogeneous data sources and this mediated schema. Some applications can start with a moderately accurate mediated schema and mappings and refine them over time, which is referred to as the pay-as-you-go approach to data integration. Creating the mediated schema and mappings automatically to bootstrap the pay-as-you-go approach has been extensively studied. However, refining the mediated schema and mappings is still an open challenge because the data sources are usually heterogeneous and use diverse and sometimes ambiguous vocabularies. In this paper, we introduce UFeed, a system that refines relational mediated schemas and mappings based on user feedback over query answers. UFeed translates user actions into refinement operations that are applied to the mediated schema and mappings to improve their quality. We experimentally verify that UFeed improves the quality of query answers over real heterogeneous data sources extracted from the web.

There has recently been an increase in the number of RDF knowledge bases published on the Internet. These rich RDF data sets can be useful in answering many queries, but much more interesting queries can be answered by integrating data from different data sets. This has given rise to research on automatically linking different RDF data sets representing different knowledge bases. This is challenging due to the scale and semantic heterogeneity of these data sets. Various approaches have been proposed, but there is room for improving the quality of the generated links. In this demonstration, we showcase ALEX, a system that aims at improving the quality of links between RDF data sets by using feedback provided by users on the answers to linked data queries. ALEX starts with multiple RDF data sets that are linked using any automatic linking algorithm. ALEX enables the user to issue queries that integrate data from different data sets, and to provide feedback on the answers to these queries. ALEX uses this feedback to eliminate incorrect links between the data sets and discover new links. In this demonstration, we show ALEX in action on multiple data sets from the Linked Open Data cloud.

There is currently a large amount of publicly accessible structured data available as RDF data sets. For example, the Linked Open Data (LOD) cloud now consists of thousands of RDF data sets with over 30 billion triples, and the number and size of the data sets is continuously growing. Many of the data sets in the LOD cloud provide public SPARQL endpoints to allow issuing queries over them. These end-points enable users to retrieve data using precise and highly expressive SPARQL queries. However, in order to do so, the user must have sufficient knowledge about the data sets that she wishes to query, that is, the structure of data, the vocabulary used within the data set, the exact values of literals, their data types, etc. Thus, while SPARQL is powerful, it is not easy to use. An alternative to SPARQL that does not require as much prior knowledge of the data is some form of keyword search over the structured data. Keyword search queries are easy to use, but inherently ambiguous in describing structured queries. This demonstration introduces Sapphire, a system for querying RDF data that strikes a middle ground between ambiguous keyword search and difficult-to-use SPARQL. Our system does not replace either, but utilizes both where they are most effective. Sapphire helps the user construct expressive SPARQL queries that represent her information needs without requiring detailed knowledge about the queried data sets. These queries are then executed over public SPARQL endpoints from the LOD cloud. Sapphire guides the user in the query writing process by showing suggestions of query terms based on the queried data, and by recommending changes to the query based on a predictive user model.

There has recently been an increase in the number of RDF knowledge bases published on the Internet. These rich RDF data sets can be useful in answering many queries, but much more interesting queries can be answered by integrating information from different data sets. This has given rise to research on automatically linking different RDF data sets representing different knowledge bases. This is challenging due to their scale and semantic heterogeneity. Various approaches have been proposed, but there is room for improving the quality of the generated links.

A wide range of applications, including bioinformatics, time series, and log analysis, depend on the identification of repetitions in very long sequences. The problem of finding maximal pairs subsumes most important types of repetition-finding tasks. Existing solutions require both the input sequence and its index (typically an order of magnitude larger than the input) to fit in memory. Moreover, they are serial algorithms with long execution time. Therefore, they are limited to small datasets, despite the fact that modern applications demand orders of magnitude longer sequences. In this paper we present RACE, a parallel system for finding maximal pairs in very long sequences. RACE supports parallel execution on stand-alone multicore systems, in addition to scaling to thousands of nodes on clusters or supercomputers. RACE does not require the input or the index to fit in memory; therefore, it supports very long sequences with limited memory. Moreover, it uses a novel array representation that allows for cache-efficient implementation. RACE is particularly suitable for the cloud (e.g., Amazon EC2) because, based on availability, it can scale elastically to more or fewer machines during its execution. Since scaling out introduces overheads, mainly due to load imbalance, we propose a cost model to estimate the expected speedup, based on statistics gathered through sampling. The model allows the user to select the appropriate combination of cloud resources based on the provider's prices and the required deadline. We conducted extensive experimental evaluation with large real datasets and large computing infrastructures. In contrast to existing methods, RACE can handle the entire human genome on a typical desktop computer with 16GB RAM. Moreover, for a problem that takes 10 hours of serial execution, RACE finishes in 28 seconds using 2,048 nodes on an IBM BlueGene/P supercomputer.