We introduce Saga, a next-generation knowledge construction and serving platform for powering knowledge-based applications at industrial scale. Saga follows a hybrid batch-incremental design to continuously integrate billions of facts about real-world entities and construct a central knowledge graph that supports multiple production use cases with diverse requirements around data freshness, accuracy, and availability. In this paper, we discuss the unique challenges associated with knowledge graph construction at industrial scale, and review the main components of Saga and how they address these challenges. Finally, we share lessons-learned from a wide array of production use cases powered by Saga.

Hierarchical classification has been shown to have superior performance than flat classification. It is typically performed on hierarchies created by and for humans rather than for classification performance. As a result, classification based on such hierarchies often yields suboptimal results. In this paper, we propose a novel genetic algorithm-based method on hierarchy adaptation for improved classification. Our approach customizes the typical GA to optimize classification hierarchies. In several text classification tasks, our approach produced hierarchies that significantly improved upon the accuracy of the original hierarchy as well as hierarchies generated by state-of-the-art methods.

A taxonomy organizes concepts or topics in a hierarchical structure and can be created manually or via automated systems. A major drawback of taxonomies is that they require users to have the same view of the topics as the taxonomy creator. Users who do not share that mental taxonomy are likely to have difficulty in finding the desired topic. In this paper, we propose a new approach to taxonomy expansion which is able to provide more flexible views. Based on an existing taxonomy, our algorithm finds possible alternative paths and generates an expanded taxonomy with flexibility in user browsing choices. In experiments on the dmoz Open Directory Project, the rebuilt taxonomies provide more alternative paths and shorter paths to information. User studies show that our expanded taxonomies are preferred compared to the original

Accurate web page classification often depends crucially on information gained from neighboring pages in the local web graph. Prior work has exploited the class labels of nearby pages to improve performance. In contrast, in this work we utilize a weighted combination of the contents of neighbors to generate a better virtual document for classification. In addition, we break pages into fields, finding that a weighted combination of text from the target and fields of neighboring pages is able to reduce classification error by more than a third. We demonstrate performance on a large dataset of pages from the Open Directory Project and validate the approach using pages from a crawl from the Stanford WebBase. Interestingly, we find no value in anchor text and unexpected value in page titles (and especially titles of parent pages) in the virtual document.

Web page classification is important to many tasks in information retrieval and web mining. However, applying traditional textual classifiers on web data often produces unsatisfying results. Fortunately, hyperlink information provides important clues to the categorization of a web page. In this paper, an improved method is proposed to enhance web page classification by utilizing the class information from neighboring pages in the link graph. The categories represented by four kinds of neighbors (parents, children, siblings and spouses) are combined to help with the page in question. In experiments to study the effect of these factors on our algorithm, we find that the method proposed is able to boost the classification accuracy of common textual classifiers from around 70% to more than 90% on a large dataset of pages from the Open Directory Project, and outperforms existing algorithms. Unlike prior techniques, our approach utilizes same-host links and can improve classification accuracy even when neighboring pages are unlabeled. Finally, while all neighbor types can contribute, sibling pages are found to be the most important.

Traditional web link-based ranking schemes use a single score to measure a page's authority without concern of the community from which that authority is derived. As a result, a resource that is highly popular for one topic may dominate the results of another topic in which it is less authoritative. To address this problem, we suggest calculating a score vector for each page to distinguish the contribution from different topics, using a random walk model that probabilistically combines page topic distribution and link structure. We show how to incorporate the topical model within both PageRank and HITS without affecting the overall property and still render insight into topic-level transition. Experiments on multiple datasets indicate that our technique outperforms other ranking approaches that incorporate textual analysis.