OpenRaiser/Intern-Atlas huggingface dataset:
The impact of negative triple generation strategies and anomalies on knowledge graph completion
Authors: Iti Bansal, Sudhanshu Tiwari, Carlos R. Rivero
Venue: Conference CIKM 2020
Year: 2020

Even though knowledge graphs have proven very useful for several tasks, they are marked by incompleteness. Completion algorithms aim to extend knowledge graphs by predicting missing (subject, predicate, object) triples, usually by training a model to discern between correct (positive) and incorrect (negative) triples. However, under the open-world assumption in which a missing triple is not negative but unknown, negative triple generation is challenging. Although negative triples are known to drive the accuracy of completion models, its impact has not been thoroughly examined yet. To evaluate accuracy, test triples are considered positive and negative triples are derived from them. The evaluation protocol is thus impacted by the generation of negative triples, which remains to be analyzed. Another issue is that the knowledge graphs available for evaluation contain anomalies like severe redundancy, and it is unclear how anomalies affect the accuracy of completion models. In this paper, we analyze the impact of negative triple generation during both training and testing on translation-based completion models. We examine four negative triple generation strategies, which are also used to evaluate the models when anomalies in the test split are included and discarded. In addition to previously-studied anomalies like near-same predicates, we include another anomaly: knowledge present in the test that is missing from the training split. Our main conclusion is that the most common strategy for negative triple generation (local-closed world assumption) can be mimicked by a combination of a naive and a immediate neighborhood strategies. This result suggests that completion models can be learned independently for certain subgraphs, which would render completion models useful in the context of knowledge graph evolution. Although anomalies are considered harmful since they artificially increase the accuracy of completion models, our results show otherwise for certain knowledge graphs, which calls for further research efforts.
Partout: a distributed engine for efficient RDF processing
Authors: Luis Galárraga, Katja Hose, Ralf Schenkel
Venue: Conference WWW 2014
Year: 2014

The increasing interest in Semantic Web technologies has led not only to a rapid growth of semantic data on the Web but also to an increasing number of backend applications relying on efficient query processing. Confronted with such a trend, existing centralized state-of-the-art systems for storing RDF and processing SPARQL queries are no longer sufficient. In this paper, we introduce Partout, a distributed engine for fast RDF processing in a cluster of machines. We propose an effective approach for fragmenting RDF data sets based on a query log and allocating the fragments to hosts in a cluster of machines. Furthermore, Partout's query optimizer produces efficient query execution plans for ad-hoc SPARQL queries.
Dynamic maintenance of web indexes using landmarks
Authors: Lipyeow Lim, Min Wang, Sriram Padmanabhan, Jeffrey Scott Vitter, Ramesh C. Agarwal
Venue: Conference WWW 2003
Year: 2003

Recent work on incremental crawling has enabled the indexed document collection of a search engine to be more synchronized with the changing World Wide Web. However, this synchronized collection is not immediately searchable, because the keyword index is rebuilt from scratch less frequently than the collection can be refreshed. An inverted index is usually used to index documents crawled from the web. Complete index rebuild at high frequency is expensive. Previous work on incremental inverted index updates have been restricted to adding and removing documents. Updating the inverted index for previously indexed documents that have changed has not been addressed.In this paper, we propose an efficient method to update the inverted index for previously indexed documents whose contents have changed. Our method uses the idea of landmarks together with the diff algorithm to significantly reduce the number of postings in the inverted index that need to be updated. Our experiments verify that our landmark-diff method results in significant savings in the number of update operations on the inverted index.
A lightweight knowledge graph embedding framework for efficient inference and storage
Authors: Haoyu Wang, Yaqing Wang, Defu Lian, Jing Gao
Venue: Conference CIKM 2021
Year: 2021

Knowledge graphs, which consist of entities and their relations, have become a popular way to store structured knowledge. Knowledge graph embedding (KGE), which derives a representation for each entity and relation, has been widely used to capture the semantics of the information in the knowledge graphs, and has demonstrated great success in many downstream applications, such as the extraction of similar entities in response to a query entity. However, existing KGE methods cannot work well on emerging knowledge graphs that are large-scale due to the constraints in storage and inference efficiency. In this paper, we propose a lightweight KGE model, LightKG, which significantly reduces storage as well as running time needed for inference. Instead of storing a continuous vector for every entity, LightKG only needs to store a few codebooks, each of which contains some codewords that correspond to the representatives among the embeddings, and the indices that correspond to the codeword selections for entities. Hence LightKG can achieve highly efficient storage. The efficiency of the downstream querying process can be significantly boosted too with the proposed LightKG model as the relevance score between the query and an entity can be efficiently calculated via a quick look-up in a table that contains the scores between the query and codewords. The storage and inference efficiency of LightKG is achieved by its novel design. LightKG is an end-to-end framework that automatically infers codebooks and codewords and generates an approximated embedding for each entity. A residual module is included in LightKG to induce the diversity among codebooks, and a continuous function is adopted to approximate codeword selection, which is non-differential. In addition, to further improve the performance of KGE, we propose a novel dynamic negative sampling method based on quantization, which can be applied to the proposed LightKG or other KGE methods. We conduct extensive experiments on five public datasets. The experiments show that LightKG is search and memory efficient with high approximate search accuracy. Also, the dynamic negative sampling can dramatically improve model performance with over 19% improvement on average.
Meta-Learning Based Hyper-Relation Feature Modeling for Out-of-Knowledge- Base Embedding
Authors: Yufeng Zhang, Weiqing Wang, Wei Chen, Jiajie Xu, An Liu, Lei Zhao
Venue: Conference CIKM 2021
Year: 2021

Knowledge graph (KG) embedding aims to encode both entities and relations into a continuous vector space. Most existing methods require that all entities should be observed during training while ignoring the evolving nature of KG. Major recent efforts on this issue embed new entities by aggregating neighborhood information from existing entities and relations with Graph Neural Network (GNN). However, these methods rely on the neighbors seen during training and suffer from the embedding of new entities with insufficient triplets or triplets with the unseen-to-unseen form. To relieve this problem, we propose a two-stage learning model referred as Hyper-Relation Feature Learning Network (HRFN) for effective out-of-knowledge-base embedding. For the first stage, HRFN learns pre-representations for emerging entities using hyper-relation features meta-learned from the training set. A novel feature aggregating network that involves an entity-centered Graph Convolutional Network (GCN) and a relation-centered GCN is proposed to aggregate information from both new entities themselves and their neighbors. For stage two, a transductive learning network is employed to learn finer-grained embeddings based on above-mentioned pre-representations of new entities. Experimental results on the link prediction task demonstrate the superiority of our model. Further analysis is also done to validate the effectiveness and efficiency of pre-representing emerging entities with the hyper-relation feature.
Fair graph representation learning with imbalanced and biased data
Authors: Yu Wang
Venue: Conference WSDM 2022
Year: 2022

Graph-structured data is omnipresent in various fields, such as biology, chemistry, social media and transportation. Learning informative graph representations are crucial in effectively completing downstream graph-related tasks such as node/graph classification and link prediction. Graph Neural Networks (GNNs), due to their inclusiveness on handling graph-structured data and distinguished data-mining power inherited from deep learning, have achieved significant success in learning graph representations. Nonetheless, most existing GNNs are mainly designed with unrealistic data assumptions, such as the balanced and unbiased data distributions while abounding real-world networks exhibit skewed (i.e., long-tailed) node/graph class distributions and may also encode patterns of previous discriminatory decisions dominated by sensitive attributes. Even further, extensive research efforts have been invested in developing GNN architectures towards improving model utility while most of the time totally ignoring whether the obtained node/graph representations conceal any discriminatory bias, which could lead to prejudicial decisions as GNN-based machine learning models are increasingly being utilized in real-world applications. In light of the prevalence of the above two types of unfairness originated from quantity-imbalanced and discriminatory bias, my research expects to propose novel node/graph representation learning frameworks through constructing innovative GNN architectures and devising novel graph-mining algorithms to learn both fair and expressive node/graph representations that can enjoy a favorable fairness-utility tradeoff.
Learning recommender systems with implicit feedback via soft target enhancement
Authors: Mingyue Cheng 0004, Fajie Yuan, Qi Liu 0003, Shenyang Ge, Zhi Li 0057, Runlong Yu, Defu Lian, Senchao Yuan, Enhong Chen
Venue: Conference SIGIR 2021
Year: 2021

One-hot encoder accompanied by a softmax loss has become the default configuration to deal with the multiclass problem, and is also prevalent in deep learning (DL) based recommender systems (RS). The standard learning process of such methods is to fit the model outputs to a one-hot encoding of the ground truth, referred to as the hard target. However, it is known that these hard targets largely ignore the ambiguity of unobserved feedback in RS, and thus may lead to sub-optimal generalization performance. In this work, we propose SoftRec, a new RS optimization framework to enhance item recommendation. The core idea is that we add additional supervisory signals - well-designed soft targets - for each instance so as to better guide the recommender learning. Meanwhile, we carefully investigate the impacts of specific soft target distributions by instantiating the SoftRec with a series of strategies, including item-based, user-based, and model-based. To verify the effectiveness of SoftRec, we conduct extensive experiments on two public recommendation datasets by using various deep recommendation architectures. The experimental results show that our methods achieve superior performance compared with the standard optimization approaches. Moreover, SoftRec could also exhibit strong performance in cold-start scenarios where user-item interaction has higher sparsity.
CHISEL: Sculpting tabular and non-tabular data on the web
Authors: Johannes Doleschal, Nico Höllerich, Wim Martens, Frank Neven
Venue: Conference WWW 2018
Year: 2018

Chisel is a tool for flexible manipulation of CSV-like data, motivated by the recent effort of the World Wide Web Consortium (W3C) towards a recommendation for tabular data and metadata on the Web. In brief, Chisel supports an expressive built-in schema language for CSV-like data, that can handle both tabular and non-tabular data. Furthermore, it supports a simple programming language for transforming tabular and non-tabular CSV-like data. In the demo, we showcase the system for specifying and validating schemas, building transformations, and setting up a pipeline for automatic conversion of "wild" CSV-like data into structured tabular data. We present use cases for Chisel specifically targeted at exemplifying the ease of specifying, modifying, and understanding Sculpt schemas as well as extracting and transforming data.
Influence function for unbiased recommendation
Authors: Jiangxing Yu, Hong Zhu 0003, Chih-Yao Chang, Xinhua Feng, Bo-Wen Yuan, Xiuqiang He 0001, Zhenhua Dong
Venue: Conference SIGIR 2020
Year: 2020

Recommender system is one of the most successful machine learning technologies for commerce. However, it can reinforce the closed feedback loop problem, where the recommender system generates items to users, then the further recommendation model is trained with the data that users' feedback to the items. Such self-reinforcing pattern can cause data bias problems. There are several debiasing methods, inverse-propensity-scoring (IPS) is a practical one for industry product. Since it is relatively easy to reweight training samples, and ameliorate the distribution shift problem. However,because of deterministic policy problem and confoundings in real-world data, it is hard to predict propensity score accurately. Inspired by the sample reweight work for robust deep learning, we propose a novel influence function based method for recommendation modeling, and analyze how the influence function corrects the bias. In the experiments, our proposed method achieves better performance against the state-of-the-art approaches.
Escape: Efficiently counting all 5-vertex subgraphs
Authors: Ali Pinar, C. Seshadhri, Vaidyanathan Vishal
Venue: Conference WWW 2017
Year: 2017

Counting the frequency of small subgraphs is a fundamental technique in network analysis across various domains, most notably in bioinformatics and social networks. The special case of triangle counting has received much attention. Getting results for 4-vertex or 5-vertex patterns is highly challenging, and there are few practical results known that can scale to massive sizes. We introduce an algorithmic framework that can be adopted to count any small pattern in a graph and apply this framework to compute exact counts for all 5-vertex subgraphs. Our framework is built on cutting a pattern into smaller ones, and using counts of smaller patterns to get larger counts. Furthermore, we exploit degree orientations of the graph to reduce runtimes even further. These methods avoid the combinatorial explosion that typical subgraph counting algorithms face. We prove that it suffices to enumerate only four specific subgraphs (three of them have less than 5 vertices) to exactly count all 5-vertex patterns. We perform extensive empirical experiments on a variety of real-world graphs. We are able to compute counts of graphs with tens of millions of edges in minutes on a commodity machine. To the best of our knowledge, this is the first practical algorithm for 5-vertex pattern counting that runs at this scale. A stepping stone to our main algorithm is a fast method for counting all 4-vertex patterns. This algorithm is typically ten times faster than the state of the art 4-vertex counters.
Factcheck: Validating rdf triples using textual evidence
Authors: Zafar Habeeb Syed, Michael Röder, Axel-Cyrille Ngonga Ngomo
Venue: Conference CIKM 2018
Year: 2018

With the increasing uptake of knowledge graphs comes an increasing need for validating the knowledge contained in these graphs. However, the sheer size and number of knowledge bases used in real-world applications makes manual fact checking impractical. In this paper, we employ sentence coherence features gathered from trustworthy source documents to outperform the state of the art in fact checking. Our approach, FactCheck, uses this information to score how likely a fact is to be true and provides the user the evidence used to validate the input facts. We evaluated our approach on two different benchmark datasets and two different corpora. Our results show that FactCheck outperforms the state of the art by up to 13.3% in F-measure and 19.3% AUC. FactCheck is open-source and is available at https://github.com/dice-group/FactCheck.
Protecting your children from inappropriate content in mobile apps: An automatic maturity rating framework
Authors: Bing Hu, Bin Liu, Neil Zhenqiang Gong, Deguang Kong, Hongxia Jin
Venue: Conference CIKM 2015
Year: 2015

Mobile applications (Apps) could expose children or adolescents to mature themes such as sexual content, violence and drug use, which results in an inappropriate security and privacy risk for them. Therefore, mobile platforms provide rating policies to label the maturity levels of Apps and the reasons why an App has a given maturity level, which enables parents to select maturity-appropriate Apps for their children. However, existing approaches to implement these maturity rating policies are either costly (because of expensive manually labeling) or inaccurate (because of no centralized controls). In this work, we aim to design and build a machine learning framework to automatically predict maturity levels for mobile Apps and the associated reasons with a high accuracy and a low cost.
Mobile app squatting
Authors: Yangyu Hu, Haoyu Wang, Ren He, Li Li, Gareth Tyson, Ignacio Castro, Yao Guo, Lei Wu, Guoai Xu
Venue: Conference WWW 2020
Year: 2020

Domain squatting, the adversarial tactic where attackers register domain names that mimic popular ones, has been observed for decades. However, there has been growing anecdotal evidence that this style of attack has spread to other domains. In this paper, we explore the presence of squatting attacks in the mobile app ecosystem. In “App Squatting”, attackers release apps with identifiers (e.g., app name or package name) that are confusingly similar to those of popular apps or well-known Internet brands. This paper presents the first in-depth measurement study of app squatting showing its prevalence and implications. We first identify 11 common deformation approaches of app squatters and propose “AppCrazy”, a tool for automatically generating variations of app identifiers. We have applied AppCrazy to the top-500 most popular apps in Google Play, generating 224,322 deformation keywords which we then use to test for app squatters on popular markets. Through this, we confirm the scale of the problem, identifying 10,553 squatting apps (an average of over 20 squatting apps for each legitimate one). Our investigation reveals that more than 51% of the squatting apps are malicious, with some being extremely popular (up to 10 million downloads). Meanwhile, we also find that mobile app markets have not been successful in identifying and eliminating squatting apps. Our findings demonstrate the urgency to identify and prevent app squatting abuses. To this end, we have publicly released all the identified squatting apps, as well as our tool AppCrazy.
Have you been properly notified? automatic compliance analysis of privacy policy text with gdpr article 13
Authors: Shuang Liu, Baiyang Zhao, Renjie Guo, Guozhu Meng, Fan Zhang, Meishan Zhang
Venue: Conference WWW 2021
Year: 2021

With the rapid development of web and mobile applications, as well as their wide adoption in different domains, more and more personal data is provided, consciously or unconsciously, to different application providers. Privacy policy is an important medium for users to understand what personal information has been collected and used. As data privacy protection is becoming a critical social issue, there are laws and regulations being enacted in different countries and regions, and the most representative one is the EU General Data Protection Regulation (GDPR). It is thus important to detect compliance issues among regulations, e.g., GDPR, with privacy policies, and provide intuitive results for data subjects (i.e., users), data collection party (i.e., service providers) and the regulatory authorities. In this work, we target to solve the problem of compliance analysis between GDPR (Article 13) and privacy policies. We format the task into a combination of a sentence classification step and a rule-based analysis step. We manually curate a corpus of 36,610 labeled sentences from 304 privacy policies, and benchmark our corpus with several standard sentence classifiers. We also conduct a rule-based analysis to detect compliance issues and a user study to evaluate the usability of our approach. The web-based tool AutoCompliance is publicly accessible 1.
A Taxonomy of Queries for E-commerce Search
Authors: Parikshit Sondhi, Mohit Sharma, Pranam Kolari, ChengXiang Zhai
Venue: Conference SIGIR 2018
Year: 2018

Understanding the search tasks and search behavior of users is necessary for optimizing search engine results. While much work has been done on understanding the users in Web search, little knowledge is available about the search tasks and behavior of users in the E-Commerce (E-Com) search applications. In this paper, we share the first empirical study of the queries and search behavior of users in E-Com search by analyzing search log from a major E-Com search engine. The analysis results show that E-Com queries can be categorized into five categories, each with distinctive search behaviors: (1) Shallow Exploration Queries are short vague queries that a user may use initially in exploring the product space. (2) Targeted Purchase Queries are queries used by users to purchase items that they are generally familiar with, thus without much decision making. (3) Major-Item Shopping Queries are used by users to shop for a major item which is often relatively expensive and thus requires some serious exploration, but typically in a limited scope of choices. (4) Minor-Item Shopping Queries are used by users to shop for minor items that are generally not very expensive, but still require some exploration of choices. (5) Hard-Choice Shopping Queries are used by users who want to deeply explore all the candidate products before finalizing the choice often appropriate when multiple products must be carefully compared with each other. These five categories form a taxonomy for E-Com queries and can shed light on how we may develop customized search technologies for each type of search queries to improve search engine utility.
Leveraging Semantic Information to Facilitate the Discovery of Underserved Podcasts
Authors: Maryam Aziz, Alice Wang, Aasish Pappu, Hugues Bouchard, Yu Zhao, Benjamin A. Carterette, Mounia Lalmas
Venue: Conference CIKM 2021
Year: 2021

Podcasts are a popular medium for rapid dissemination of information, entertainment, and casual conversations. Content aggregators are taking an increased interest in recommending podcasts to listeners to help them build larger audiences. With many podcasts released every day, many podcasts that would be of interest to listeners remain underserved by these recommendation systems. In this paper, we study variables related to podcast appeal to listeners selected at random in a large online study, in a production setting, involving more than five million recommendations. We present the results of two observational studies, which suggests that underserved podcast have the potential to grow their audiences. To mitigate the rich-get-richer effect, we propose leveraging semantic information, via means of knowledge graphs, to recommend underserved podcasts to listeners. Finally, we conduct empirical experiments that show our method is effective at recommending underserved podcasts, in comparison to baseline methods that rely on listening behavior.