Report on the first workshop on bias in automatic knowledge graph construction at AKBC 2020

2020 ◽  
Vol 54 (2) ◽  
pp. 1-9
Author(s):  
Tara Safavi ◽  
Edgar Meij ◽  
Fatma Özcan ◽  
Miriam Redi ◽  
Gianluca Demartini ◽  
...  
Keyword(s):  
Knowledge Base ◽  
Research Agenda ◽  
Knowledge Graph ◽  
Knowledge Base Construction ◽  
The Future ◽  
Knowledge Graphs ◽  
Automated Knowledge ◽  
Common Understanding

We report on the First Workshop on Bias in Automatic Knowledge Graph Construction (KG-BIAS), which was co-located with the Automated Knowledge Base Construction (AKBC) 2020 conference. Identifying and possibly remediating any sort of bias in knowledge graphs, or in the methods used to construct or query them, has clear implications for downstream systems accessing and using the information in such graphs. However, this topic remains relatively unstudied, so our main aim for organizing this workshop was to bring together a group of people from a variety of backgrounds with an interest in the topic, in order to arrive at a shared definition and roadmap for the future. Through a program that included two keynotes, an invited paper, three peer-reviewed full papers, and a plenary discussion, we have made initial inroads towards a common understanding and shared research agenda for this timely and important topic.

scholarly journals End-to-End Structure-Aware Convolutional Networks for Knowledge Base Completion

2019 ◽  
Vol 33 ◽  
pp. 3060-3067 ◽  
Author(s):  
Chao Shang ◽  
Yun Tang ◽  
Jing Huang ◽  
Jinbo Bi ◽  
Xiaodong He ◽  
...  
Keyword(s):  
Knowledge Base ◽  
State Of The Art ◽  
The State ◽  
Graph Connectivity ◽  
Knowledge Graph ◽  
Graph Node ◽  
Convolutional Network ◽  
Node Attributes ◽  
Knowledge Graphs ◽  
End To End

Knowledge graph embedding has been an active research topic for knowledge base completion, with progressive improvement from the initial TransE, TransH, DistMult et al to the current state-of-the-art ConvE. ConvE uses 2D convolution over embeddings and multiple layers of nonlinear features to model knowledge graphs. The model can be efficiently trained and scalable to large knowledge graphs. However, there is no structure enforcement in the embedding space of ConvE. The recent graph convolutional network (GCN) provides another way of learning graph node embedding by successfully utilizing graph connectivity structure. In this work, we propose a novel end-to-end StructureAware Convolutional Network (SACN) that takes the benefit of GCN and ConvE together. SACN consists of an encoder of a weighted graph convolutional network (WGCN), and a decoder of a convolutional network called Conv-TransE. WGCN utilizes knowledge graph node structure, node attributes and edge relation types. It has learnable weights that adapt the amount of information from neighbors used in local aggregation, leading to more accurate embeddings of graph nodes. Node attributes in the graph are represented as additional nodes in the WGCN. The decoder Conv-TransE enables the state-of-the-art ConvE to be translational between entities and relations while keeps the same link prediction performance as ConvE. We demonstrate the effectiveness of the proposed SACN on standard FB15k-237 and WN18RR datasets, and it gives about 10% relative improvement over the state-of-theart ConvE in terms of HITS@1, HITS@3 and HITS@10.

scholarly journals Explainable Reasoning over Knowledge Graphs for Recommendation

2019 ◽  
Vol 33 ◽  
pp. 5329-5336 ◽  
Author(s):  
Xiang Wang ◽  
Dingxian Wang ◽  
Canran Xu ◽  
Xiangnan He ◽  
Yixin Cao ◽  
...  
Keyword(s):  
Knowledge Base ◽  
State Of The Art ◽  
Recurrent Network ◽  
User Preferences ◽  
Knowledge Graph ◽  
Complementary Information ◽  
Sequential Dependencies ◽  
Factorization Machine ◽  
Knowledge Graphs ◽  
Collaborative Knowledge

Incorporating knowledge graph into recommender systems has attracted increasing attention in recent years. By exploring the interlinks within a knowledge graph, the connectivity between users and items can be discovered as paths, which provide rich and complementary information to user-item interactions. Such connectivity not only reveals the semantics of entities and relations, but also helps to comprehend a user’s interest. However, existing efforts have not fully explored this connectivity to infer user preferences, especially in terms of modeling the sequential dependencies within and holistic semantics of a path.In this paper, we contribute a new model named Knowledgeaware Path Recurrent Network (KPRN) to exploit knowledge graph for recommendation. KPRN can generate path representations by composing the semantics of both entities and relations. By leveraging the sequential dependencies within a path, we allow effective reasoning on paths to infer the underlying rationale of a user-item interaction. Furthermore, we design a new weighted pooling operation to discriminate the strengths of different paths in connecting a user with an item, endowing our model with a certain level of explainability. We conduct extensive experiments on two datasets about movie and music, demonstrating significant improvements over state-of-the-art solutions Collaborative Knowledge Base Embedding and Neural Factorization Machine.

scholarly journals Multilingual Knowledge Graph Embeddings for Cross-lingual Knowledge Alignment

2017 ◽  
Author(s):  
Muhao Chen ◽  
Yingtao Tian ◽  
Mohan Yang ◽  
Carlo Zaniolo
Keyword(s):  
Knowledge Base ◽  
Knowledge Bases ◽  
Loss Functions ◽  
Knowledge Graph ◽  
Graph Embeddings ◽  
Linear Transformations ◽  
Human Labor ◽  
Knowledge Graphs ◽  
Cross Lingual ◽  
Entity Relationships

Many recent works have demonstrated the benefits of knowledge graph embeddings in completing monolingual knowledge graphs. Inasmuch as related knowledge bases are built in several different languages, achieving cross-lingual knowledge alignment will help people in constructing a coherent knowledge base, and assist machines in dealing with different expressions of entity relationships across diverse human languages. Unfortunately, achieving this highly desirable cross-lingual alignment by human labor is very costly and error-prone. Thus, we propose MTransE, a translation-based model for multilingual knowledge graph embeddings, to provide a simple and automated solution. By encoding entities and relations of each language in a separated embedding space, MTransE provides transitions for each embedding vector to its cross-lingual counterparts in other spaces, while preserving the functionalities of monolingual embeddings. We deploy three different techniques to represent cross-lingual transitions, namely axis calibration, translation vectors, and linear transformations, and derive five variants for MTransE using different loss functions. Our models can be trained on partially aligned graphs, where just a small portion of triples are aligned with their cross-lingual counterparts. The experiments on cross-lingual entity matching and triple-wise alignment verification show promising results, with some variants consistently outperforming others on different tasks. We also explore how MTransE preserves the key properties of its monolingual counterpart.

scholarly journals Using Semantics and Statistics to Turn Data into Knowledge

AI Magazine ◽  
2015 ◽  
Vol 36 (1) ◽  
pp. 65-74 ◽  
Author(s):  
Jay Pujara ◽  
Hui Miao ◽  
Lise Getoor ◽  
William W. Cohen
Keyword(s):  
Knowledge Base ◽  
State Of The Art ◽  
Relational Learning ◽  
Statistical Relational Learning ◽  
Knowledge Bases ◽  
Knowledge Graph ◽  
Learning Framework ◽  
Knowledge Base Construction ◽  
Order Of Magnitude ◽  
Soft Logic

Many information extraction and knowledge base construction systems are addressing the challenge of deriving knowledge from text. A key problem in constructing these knowledge bases from sources like the web is overcoming the erroneous and incomplete information found in millions of candidate extractions. To solve this problem, we turn to semantics — using ontological constraints between candidate facts to eliminate errors. In this article, we represent the desired knowledge base as a knowledge graph and introduce the problem of knowledge graph identification, collectively resolving the entities, labels, and relations present in the knowledge graph. Knowledge graph identification requires reasoning jointly over millions of extractions simultaneously, posing a scalability challenge to many approaches. We use probabilistic soft logic (PSL), a recently-introduced statistical relational learning framework, to implement an efficient solution to knowledge graph identification and present state-of-the-art results for knowledge graph construction while performing an order of magnitude faster than competing methods.

Mobile Software Assurance Informed through Knowledge Graph Construction: The OWASP Threat of Insecure Data Storage

2020 ◽  
Vol 2 (2) ◽  
Author(s):  
Suzanna Schmeelk ◽  
Lixin Tao
Keyword(s):  
Data Storage ◽  
Program Analysis ◽  
Web Application ◽  
Security Analysis ◽  
Knowledge Graph ◽  
Healthcare Applications ◽  
Sensitive Data ◽  
Knowledge Graphs ◽  
Mobile Malware Detection ◽  
Software Assurance

Many organizations, to save costs, are movinheg to t Bring Your Own Mobile Device (BYOD) model and adopting applications built by third-parties at an unprecedented rate.  Our research examines software assurance methodologies specifically focusing on security analysis coverage of the program analysis for mobile malware detection, mitigation, and prevention.  This research focuses on secure software development of Android applications by developing knowledge graphs for threats reported by the Open Web Application Security Project (OWASP).  OWASP maintains lists of the top ten security threats to web and mobile applications.  We develop knowledge graphs based on the two most recent top ten threat years and show how the knowledge graph relationships can be discovered in mobile application source code.  We analyze 200+ healthcare applications from GitHub to gain an understanding of their software assurance of their developed software for one of the OWASP top ten moble threats, the threat of “Insecure Data Storage.”  We find that many of the applications are storing personally identifying information (PII) in potentially vulnerable places leaving users exposed to higher risks for the loss of their sensitive data.

scholarly journals TransET: Knowledge Graph Embedding with Entity Types

Electronics ◽  
2021 ◽  
Vol 10 (12) ◽  
pp. 1407
Author(s):  
Peng Wang ◽  
Jing Zhou ◽  
Yuzhang Liu ◽  
Xingchen Zhou
Keyword(s):  
Link Prediction ◽  
State Of The Art ◽  
Score Function ◽  
Graph Embedding ◽  
Vector Spaces ◽  
Knowledge Graph ◽  
Semantic Features ◽  
Knowledge Graphs ◽  
Real World Datasets ◽  
Low Dimensional

Knowledge graph embedding aims to embed entities and relations into low-dimensional vector spaces. Most existing methods only focus on triple facts in knowledge graphs. In addition, models based on translation or distance measurement cannot fully represent complex relations. As well-constructed prior knowledge, entity types can be employed to learn the representations of entities and relations. In this paper, we propose a novel knowledge graph embedding model named TransET, which takes advantage of entity types to learn more semantic features. More specifically, circle convolution based on the embeddings of entity and entity types is utilized to map head entity and tail entity to type-specific representations, then translation-based score function is used to learn the presentation triples. We evaluated our model on real-world datasets with two benchmark tasks of link prediction and triple classification. Experimental results demonstrate that it outperforms state-of-the-art models in most cases.

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