We present ReGroup, a novel end-user interactive machine learning system for helping people create custom, on demand groups in online social networks. As a person adds members to a group, ReGroup iteratively learns a probabilistic model of group membership specific to that group. ReGroup then uses its currently learned model to suggest additional members and group characteristics for filtering. Our evaluation shows that ReGroup is effective for helping people create large and varied groups, whereas traditional methods (searching by name or selecting from an alphabetical list) are better suited for small groups whose members can be easily recalled by name. By facilitating on demand group creation, ReGroup can enable in-context sharing and potentially encourage better online privacy practices. In addition, applying interactive machine learning to social network group creation introduces several challenges for designing effective end-user interaction with machine learning. We identify these challenges and discuss how we address them in ReGroup.

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Regroup: interactive machine learning for on-demand group creation in social networks

The amounts of available Semantic Web (SW) data (including Linked Open Data) constantly increases. Users would like to browse and explore effectively such information spaces without having to be acquainted with the various vocabularies and query language syntaxes. This paper discusses the work that has been done in the area for the case of RDF/S datasets, with emphasis on session-based interaction schemes for exploratory search. In particular, it surveys the related works according to various aspects, such as assumed user goals, structuring of the underlying information space, generality and configuration requirements, and various (state space-based) features of the navigation structure. Subsequently it introduces a small but concise formal model of the interaction (that captures the core functionalities) which is used as reference model for describing what the existing systems support. Finally the paper describes the evaluation methods that have been used. Overall, the presented analysis aids the understanding and comparison of the various different approaches that have been proposed so far.

Faceted exploration of RDF/S datasets: a survey

Faceted Search is an exploratory search mechanism, which provides an iterative way to refine search results by a faceted taxonomy. With the benefit of search results diversification, no need for a priori knowledge, and never leading to zero result, it can significantly reduce information overload. Faceted Search has witnessed a booming interest in the last ten years. In this paper, we first analyze the representative facet search models. Next, we present a general faceted search framework, and survey the related methods and techniques, including facet term extraction, hierarchy construction, compound term generation and facet ranking. Then we discuss the metrics for faceted search evaluation, and also highlight the main characteristics of a number of existing faceted search systems. Some directions for future research are finally presented.

A survey of faceted search

• A submitted manuscript is the version of the article upon submission and before peer-review. There can be important differences between the submitted version and the official published version of record. People interested in the research are advised to contact the author for the final version of the publication, or visit the DOI to the publisher's website. • The final author version and the galley proof are versions of the publication after peer review. • The final published version features the final layout of the paper including the volume, issue and page numbers.

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Designing algorithms for big graph datasets : a study of computing bisimulation and joins

In recent years, there has been a strong trend of incorporating results from structured data sources into keyword-based web search systems such as Bing or Amazon. When presenting structured data, facets are a powerful tool for navigating, refining, and grouping the results. For a given structured data source, a fundamental problem in supporting faceted search is finding an ordered selection of attributes and values that will populate the facets. This creates two sets of challenges. First, because of the limited screen real-estate, it is important that the top facets best match the anticipated user intent. Second, the huge scale of available data to such engines demands an automated unsupervised solution.In this paper, we model the user faceted-search behavior using the intersection of web query-logs with existing structured data. Since web queries are formulated as free-text queries, a challenge in our approach is the inherent ambiguity in mapping keywords to the different possible attributes of a given entity type. We present an automated solution that elicits user preferences on attributes and values, employing different disambiguation techniques ranging from simple keyword matching, to more sophisticated probabilistic models. We demonstrate experimentally the scalability of our solution by running it on over a thousand categories of diverse entity types and measure the facet quality with a real-user study.

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Facet discovery for structured web search: a query-log mining approach

This paper proposes Facetedpedia, a faceted retrieval system for information discovery and exploration in Wikipedia. Given the set of Wikipedia articles resulting from a keyword query, Facetedpedia generates a faceted interface for navigating the result articles. Compared with other faceted retrieval systems, Facetedpedia is fully automatic and dynamic in both facet generation and hierarchy construction, and the facets are based on the rich semantic information from Wikipedia. The essence of our approach is to build upon the collaborative vocabulary in Wikipedia, more specifically the intensive internal structures (hyperlinks) and folksonomy (category system). Given the sheer size and complexity of this corpus, the space of possible choices of faceted interfaces is prohibitively large. We propose metrics for ranking individual facet hierarchies by user's navigational cost, and metrics for ranking interfaces (each with k facets) by both their average pairwise similarities and average navigational costs. We thus develop faceted interface discovery algorithms that optimize the ranking metrics. Our experimental evaluation and user study verify the effectiveness of the system.

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Facetedpedia: dynamic generation of query-dependent faceted interfaces for wikipedia

This paper presents a fully convolutional scene graph generation (FCSGG) model that detects objects and relations simultaneously. Most of the scene graph generation frameworks use a pre-trained two-stage object detector, like Faster R-CNN, and build scene graphs using bounding box features. Such pipeline usually has a large number of parameters and low inference speed. Unlike these approaches, FCSGG is a conceptually elegant and efficient bottom-up approach that encodes objects as bounding box center points, and relationships as 2D vector fields which are named as Relation Affinity Fields (RAFs). RAFs encode both semantic and spatial features, and explicitly represent the relationship between a pair of objects by the integral on a sub-region that points from subject to object. FCSGG only utilizes visual features and still generates strong results for scene graph generation. Comprehensive experiments on the Visual Genome dataset demonstrate the efficacy, efficiency, and generalizability of the proposed method. FCSGG achieves highly competitive results on recall and zeroshot recall with significantly reduced inference time.

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Fully Convolutional Scene Graph Generation

In data warehousing and OLAP applications, scalar-level predicates in SQL become increasingly inadequate to support a class of operations that require set-level comparison semantics, i.e., comparing a group of tuples with multiple values. Currently, complex SQL queries composed by scalar-level operations are often formed to obtain even very simple set-level semantics. Such queries are not only difficult to write but also challenging for a database engine to optimize, thus can result in costly evaluation. This paper proposes to augment SQL with set predicate, to bring out otherwise obscured set-level semantics. We studied two approaches to processing set predicates-an aggregate function-based approach and a bitmap index-based approach. Moreover, we designed a histogram-based probabilistic method of set predicate selectivity estimation, for optimizing queries with multiple predicates. The experiments verified its accuracy and effectiveness in optimizing queries.

Set Predicates in SQL: Enabling Set-Level Comparisons for Dynamically Formed Groups

Facetedpedia is a faceted search system that dynamically discovers query-dependent faceted interfaces for Wikipedia search result articles. In this paper, we give an overview of Facetedpedia, present the system architecture and implementation techniques, and elaborate on a demonstration scenario.

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Facetedpedia: enabling query-dependent faceted search for wikipedia

Users are tapping into massive, heterogeneous entity graphs for many applications. It is challenging to select entity graphs for a particular need, given abundant datasets from many sources and the oftentimes scarce information for them. We propose methods to produce preview tables for compact presentation of important entity types and relationships in entity graphs. The preview tables assist users in attaining a quick and rough preview of the data. They can be shown in a limited display space for a user to browse and explore, before she decides to spend time and resources to fetch and investigate the complete dataset. We formulate several optimization problems that look for previews with the highest scores according to intuitive goodness measures, under various constraints on preview size and distance between preview tables. The optimization problem under distance constraint is NP-hard. We design a dynamic-programming algorithm and an Apriori-style algorithm for finding optimal previews. Results from experiments, comparison with related work and user studies demonstrated the scoring measures' accuracy and the discovery algorithms' efficiency.

https://dl.acm.org/doi/pdf/10.1145/2882903.2915221

Ning Yan

Papers

Facetedpedia: dynamic generation of query-dependent faceted interfaces for wikipedia

Fully Convolutional Scene Graph Generation

Set Predicates in SQL: Enabling Set-Level Comparisons for Dynamically Formed Groups

Facetedpedia: enabling query-dependent faceted search for wikipedia

Generating Preview Tables for Entity Graphs