Abstract Knowledge graphs have been widely adopted across organizations and research domains, fueling applications that span interactive browsing to large-scale analysis and data science. One design decision in knowledge graph deployment is choosing a representation that optimally supports the application’s consumers. Currently, however, there is no consensus on which representations best support each consumer scenario. In this work, we analyze the fitness of popular knowledge graph representations for three consumer scenarios: knowledge exploration, systematic querying, and graph completion. We compare the accessibility for knowledge exploration through a user study with dedicated browsing interfaces and query endpoints. We assess systematic querying with SPARQL in terms of time and query complexity on both synthetic and real-world datasets. We measure the impact of various representations on the popular graph completion task by training graph embedding models per representation. We experiment with four representations: Standard Reification, N-Ary Relationships, Wikidata qualifiers, and RDF-star. We find that Qualifiers and RDF-star are better suited to support use cases of knowledge exploration and systematic querying, while Standard Reification models perform most consistently for embedding model inference tasks but may become cumbersome for users. With this study, we aim to provide novel insights into the relevance of the representation choice and its impact on common knowledge graph consumption scenarios.

In recent decades, the growing scale of scientific research has led to numerous novel findings. Reproducing these findings is the foundation of future research. However, due to the complexity of experiments, manually assessing scientific research is laborious and time-intensive, especially in social and behavioral sciences. Although increasing reproducibility studies have garnered increased attention in the research community, there is still a lack of systematic ways for evaluating scientific research at scale. In this paper, we propose a novel approach towards automatically assessing scientific publications by constructing a knowledge graph (KG) that captures a holistic view of the research contributions. Specifically, during the KG construction, we combine information from two different perspectives: micro-level features that capture knowledge from published articles such as sample sizes, effect sizes, and experimental models, and macro-level features that comprise relationships between entities such as authorship and reference information. We then learn low-dimensional representations using language models and knowledge graph embeddings for entities (nodes in KGs), which are further used for the assessments. A comprehensive set of experiments on two benchmark datasets shows the usefulness of leveraging KGs for scoring scientific research.

Tables are a widely-used format for data curation. The diversity of domains, layouts, and content of tables makes knowledge extraction challenging. Understanding table layouts is an important step for automatically harvesting knowledge from tabular data. Since table cells are spatially organized into regions, correctly identifying such regions and inferring their functional roles, referred to as functional block detection, is a critical part of understanding table layouts. Earlier functional block detection approaches fail to leverage spatial relationships and higher-level structure, either depending on cell-level predictions or relying on data types as signals for identifying blocks. In this paper, we introduce a flexible functional block detection method by applying agglomerative clustering techniques which merge smaller blocks into larger blocks using two merging strategies. Our proposed method uses cell embeddings with a customized dissimilarity function which utilizes local and margin distances, as well as block coherence metrics to capture cell, block, and table scoped features. Given the diversity of tables in real-world corpora, we also introduce a sampling-based approach for automatically tuning distance thresholds for each table. Experimental results show that our method improves over the earlier state-of-the-art method in terms of several evaluation metrics.

A wealth of human knowledge is expressed in structured tables, across web pages, scientific articles, spreadsheets, and databases. This wealth of knowledge is mirrored by diversity in the vast number of layout structures, content types, formats, and surface forms used to express tables. Recent advances in representation learning and knowledge representation have made progress in exploiting structural regularities in tabular data to unlock this knowledge. In this tutorial, we provide a survey of these advances for a host of table understanding tasks, including table segmentation, semantic typing of cells, transforming tables to knowledge graphs, entity linking, and table retrieval tasks for question answering.

The task of natural language table retrieval (NLTR) seeks to retrieve semantically relevant tables based on natural language queries. Existing learning systems for this task often treat tables as plain text based on the assumption that tables are structured as dataframes. However, tables can have complex layouts which indicate diverse dependencies between subtable structures, such as nested headers. As a result, queries may refer to different spans of relevant content that is distributed across these structures. Moreover, such systems fail to generalize to novel scenarios beyond those seen in the training set. Prior methods are still distant from a generalizable solution to the NLTR problem, as they fall short in handling complex table layouts or queries over multiple granularities. To address these issues, we propose Graph-based Table Retrieval (GTR), a generalizable NLTR framework with multi-granular graph representation learning. In our framework, a table is first converted into a tabular graph, with cell nodes, row nodes and column nodes to capture content at different granularities. Then the tabular graph is input to a Graph Transformer model that can capture both table cell content and the layout structures. To enhance the robustness and generalizability of the model, we further incorporate a self-supervised pre-training task based on graph-context matching. Experimental results on two benchmarks show that our method leads to significant improvements over the current state-of-the-art systems. Further experiments demonstrate promising performance of our method on cross-dataset generalization, and enhanced capability of handling complex tables and fulfilling diverse query intents.

Data collected from heterogeneous sources often have inconsistencies in data format and thus require transformation before the data can be used. A major issue of existing approaches is their dependency on parallel input-output data to learn the transformations. However, parallel data are not always available, and annotation requires excessive human interaction because of format diversity. Therefore, these approaches have limitations when applied to large-scale real-world problems. To address this issue, we introduce UDATA, a novel unsupervised system for non-parallel data transformation. Because the transforming data usually share common syntactic patterns, UDATA discovers common syntactic patterns from input/output examples and synthesizes the transformations between the patterns. Moreover, in UDATA, transformation results are verified by an active learning model and ambiguous results are reported to users for labeling. UDATA achieves accuracy close to other state-of-the-art supervised systems without the need for any labeled data.

There is a large amount of data on the web in tabular form, such as excel sheets, CSVs, and web tables. Often, tabular data is meant for human consumption, using data layouts that are difficult for machines to interpret automatically. Previous work uses the stylistic features of tabular cells (e.g. font size, border type, background color) to classify tabular cells by their role in the data layout of the document (top attribute, data, metadata, etc.). In this paper, we propose a method to embed the semantic and contextual information about tabular cells in a low dimension cell embedding space. We then propose an RNN-based classification technique to use these cell vector representations, combining them with stylistic features introduced in previous work, in order to improve the performance of cell type classification in complex documents. We evaluate the performance of our system on three datasets containing documents with various data layouts, in two settings, in-domain, and cross-domain training. Our evaluation result shows that our proposed cell vector representations in combination with our RNN-based classification technique significantly improves cell type classification performance.

The web contains millions of useful spreadsheets and CSV files, but these files are difficult to use in applications because they use a wide variety of data layouts and terminology. We present Table To Wikidata Mapping Language (T2WML), a language that makes it easy to map and link arbitrary spreadsheets and CSV files to the Wikidata data model. The output of T2WML consists of Wikidata statements that can be loaded in the public Wikidata knowledge base or in a Wikidata clone repository, creating an augmented Wikidata knowledge graph that application developers can query using SPARQL.

Publishing data sources to knowledge graphs is a complicated and laborious process as data sources are often heterogeneous, hierarchical and interlinked. As an example, food price datasets may contain product prices of various units at different markets and times, and different providers can have many choices of formats such as CSV, JSON or spreadsheet. Beyond data formats, these datasets may have differing layout, where one dataset may be organized as a row-based table or relational table (prices are in one column), while another may use a matrix table (prices are in one matrix). To address these problems, we present a novel data description language for mapping datasets to RDF. In particular, our language supports specifying the locations of source attributes in the sources, mapping of the attributes to ontologies, and simple rules to join the data of these attributes to output final RDF triples. Unlike existing approaches, our language is not restricted to specific data layouts such as the Nested Relational Model, or to specific data formats, such as spreadsheet. Our broad data description language presents a format-independent solution, allowing interlinking among multiple heterogeneous sources and representing many diverse data structures that existing tools are unable to handle.

A semantic model of a data source is a representation of the concepts and relationships contained in the data. Building semantic models is a prerequisite to automatically publishing data to a knowledge graph. However, creating these semantic models is a complex process requiring considerable manual effort and can be error-prone. In this paper, we present a novel approach that efficiently searches over the combinatorial space of possible semantic models, and applies a probabilistic graphical model to identify the most probable semantic model for a data source. Probabilistic graphical models offer many advantages over existing methods: they are robust to noisy inputs and provide a straightforward approach for exploiting relationships within the data. Our solution uses a conditional random field (CRF) to encode structural patterns and enforce conceptual consistency within the semantic model. In an empirical evaluation, our approach outperforms state of the art systems by an average 8.4% of F1 score, even with noisy input data.

Knowledge graphs have become an increasingly crucial component in machine intelligence systems, powering ubiquitous digital assistants and inspiring several large scale academic projects across the globe. Our tutorial explains why knowledge graphs are important, how knowledge graphs are constructed, and where new research opportunities exist for improving the state-of-the-art. In this tutorial, we cover the many sophisticated approaches that complete and correct knowledge graphs. We organize this exploration into two main classes of models. The first include probabilistic logical frameworks that use graphical models, random walks, or statistical rule mining to construct knowledge graphs. The second class of models includes latent space models such as matrix and tensor factorization and neural networks. We conclude the tutorial with a critical comparison of techniques and results. We will offer practical advice for novices to identify common empirical challenges and concrete data sets for initial experimentation. Finally, we will highlight promising areas of current and future work.

Entity resolution in settings with rich relational structure often introduces complex dependencies between co-references. Exploiting these dependencies is challenging - it requires seamlessly combining statistical, relational, and logical dependencies. One task of particular interest is entity resolution in familial networks. In this setting, multiple partial representations of a family tree are provided, from the perspective of different family members, and the challenge is to reconstruct a family tree from these multiple, noisy, partial views. This reconstruction is crucial for applications such as understanding genetic inheritance, tracking disease contagion, and performing census surveys. Here, we design a model that incorporates statistical signals, such as name similarity, relational information, such as sibling overlap, and logical constraints, such as transitivity and bijective matching, in a collective model. We show how to integrate these features using probabilistic soft logic, a scalable probabilistic programming framework. In experiments on real-world data, our model significantly outperforms state-of-the-art classifiers that use relational features but are incapable of collective reasoning.

Hybrid recommender systems combine several different sources of information to generate recommendations. These systems demonstrate improved accuracy compared to single-source recommendation strategies. However, hybrid recommendation strategies are inherently more complex than those that use a single source of information, and thus the process of explaining recommendations to users becomes more challenging. In this paper we describe a hybrid recommender system built on a probabilistic programming language, and discuss the benefits and challenges of explaining its recommendations to users. We perform a mixed model statistical analysis of user preferences for explanations in this system. Through an online user survey, we evaluate explanations for hybrid algorithms in a variety of text and visual, graph-based formats, that are either novel designs or derived from existing hybrid recommender systems.

Personalization -- the customization of experiences, interfaces, and content to individual users -- has catalyzed user growth and engagement for many web services. A critical prerequisite to personalization is establishing user identity. However the variety of devices, including mobile phones, appliances, and smart watches, from which users access web services from both anonymous and logged-in sessions poses a significant obstacle to user identification. The resulting entity resolution task of establishing user identity across devices and sessions is commonly referred to as ``visitor stitching.'' We introduce a general, probabilistic approach to visitor stitching using features and attributes commonly contained in web logs. Using web logs from two real-world corporate websites, we motivate the need for probabilistic models by quantifying the difficulties posed by noise, ambiguity, and missing information in deployment. Next, we introduce our approach using probabilistic soft logic (PSL), a statistical relational learning framework capable of capturing similarities across many sessions and enforcing transitivity. We present a detailed description of model features and design choices relevant to the visitor stitching problem. Finally, we evaluate our PSL model on binary classification performance for two real-world visitor stitching datasets. Our model demonstrates significantly better performance than several state-of-the-art classifiers, and we show how this advantage results from collective reasoning across sessions.

Microblogging sites like Twitter provide a platform for sharing ideas and expressing opinions. The widespread popularity of these platforms and the complex social structure that arises within these communities provides a unique opportunity to understand the interactions between users. The political domain, especially in a multi-party system, presents compelling challenges, as political parties have different levels of alignment based on their political strategies. We use Twitter to understand the nuanced relationships between differing political entities in Latin America. Our model incorporates diverse signals from the content of tweets and social context from retweets, mentions and hashtag usage. Since direct communications between entities are relatively rare, we explore models based on the posts of users who interact with multiple political organizations. We present a quantitative and qualitative analysis of the results of models using different features, and demonstrate that a model capable of using sentiment strength, social context, and issue alignment has superior performance to less sophisticated baselines.