Muhao Chen

Multilingual Knowledge Graph Embeddings for Cross-lingual Knowledge Alignment

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 crosslingual alignment by human labor is very costly and errorprone. 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 TransE.

On2Vec: Embedding-based Relation Prediction for Ontology Population

Populating ontology graphs represents a long-standing problem for the Semantic Web community. Recent advances in translation-based graph embedding methods for populating instance-level knowledge graphs lead to promising new approaching for the ontology population problem. However, unlike instance-level graphs, the majority of relation facts in ontology graphs come with comprehensive semantic relations, which often include the properties of transitivity and symmetry, as well as hierarchical relations. These comprehensive relations are often too complex for existing graph embedding methods, and direct application of such methods is not feasible. Hence, we propose On2Vec, a novel translation-based graph embedding method for ontology population. On2Vec integrates two model components that effectively characterize comprehensive relation facts in ontology graphs. The first is the Component-specific Model that encodes concepts and relations into low-dimensional embedding spaces without a loss of relational properties; the second is the Hierarchy Model that performs focused learning of hierarchical relation facts. Experiments on several well-known ontology graphs demonstrate the promising capabilities of On2Vec in predicting and verifying new relation facts. These promising results also make possible significant improvements in related methods.

Co-training Embeddings of Knowledge Graphs and Entity Descriptions for Cross-lingual Entity Alignment

Multilingual knowledge graph (KG) embeddings provide latent semantic representations of entities and structured knowledge with cross-lingual inferences, which benefit various knowledge-driven cross-lingual NLP tasks. However, precisely learning such cross-lingual inferences is usually hindered by the low coverage of entity alignment in many KGs. Since many multilingual KGs also provide literal descriptions of entities, in this paper, we introduce an embedding-based approach which leverages a weakly aligned multilingual KG for semi-supervised cross-lingual learning using entity descriptions. Our approach performs co-training of two embedding models, i.e. a multilingual KG embedding model and a multilingual literal description embedding model. The models are trained on a large Wikipedia-based trilingual dataset where most entity alignment is unknown to training. Experimental results show that the performance of the proposed approach on the entity alignment task improves at each iteration of co-training, and eventually reaches a stage at which it significantly surpasses previous approaches. We also show that our approach has promising abilities for zero-shot entity alignment, and cross-lingual KG completion.

Learning Multi-faceted Knowledge Graph Embeddings for Natural Language Processing

Knowledge graphs have challenged the existing embedding-based approaches for representing their multifacetedness. To address some of the issues, we have investigated some novel approaches that (i) capture the multilingual transitions on different language-specific versions of knowledge, and (ii) encode the commonly existing monolingual knowledge with important relational properties and hierarchies. In addition, we propose the use of our approaches in a wide spectrum of NLP tasks that have not been well explored by related works.

User-friendly temporal queries on historical knowledge bases

Abstract DBpedia and other RFD-encoded Knowledge Bases (KB)s give users access to encyclopedic knowledge via SPARQL queries. As the world evolves, the KBs are updated, and the history of entities and their properties becomes of great interest. Thus, we need powerful tools and friendly interfaces to query histories and flash-back to the past. Here, we propose (i) a point-based temporal extension of SPARQL, called SPARQL T , which enables simple and concise expression of temporal queries, and (ii) an extension of Wikipedia Infoboxes to support user-friendly by-example temporal queries implemented by mapping them into SPARQL T . Our main-memory RDF-TX system supports such queries efficiently using Multi-Version B+ trees, compressed indexes, and query optimization techniques, which achieve performance and scalability, as demonstrated by experiments on historical datasets including Cliopedia derived from Wikipedia dumps. We finally discuss how provenance information can be used to add valid-time features to these transaction-time KBs.

Converting spatiotemporal data Among heterogeneous granularity systems

Spatiotemporal data are often expressed in terms of granularities to indicate the measurement units of the data. A granularity system usually consists of a set of granularities that share a “common refined granularity” (CRG) to enable granular comparison and data conversion within the system. However, if data from multiple granularity systems needs to be used in a unified application, it is necessary to extend the data conversion and comparison within a granularity system to those for multiple granularity systems. This paper proposes a formal framework to enable such an extension. The framework involves essentially some preconditions and properties for verifying the existence of a CRG and unifying conversions of incongruous semantics, and supports the approach to integrate multiple systems into one so as to process granular interoperation across systems just like in a single system. Quantification of uncertainty in granularity conversion is also considered to improve the precision of granular comparison.

Demand-driven Cache Allocation Based on Context-aware Collaborative Filtering

Many recent advances of network caching focus on i) more effectively modeling the preferences of a regional user group to different web contents, and ii) reducing the cost of content delivery by storing the most popular contents in regional caches. However, the context under which the users interact with the network system usually causes tremendous variations in a user groups preferences on the contents. To effectively leverage such contextual information for more efficient network caching, we propose a novel mechanism to incorporate context-aware collaborative filtering into demand-driven caching. By differentiating the characterization of user interests based on a priori contexts, our approach seeks to enhance the cache performance with a more dynamic and fine-grained cache allocation process. In particular, our approach is general and adapts to various types of context information. Our evaluation shows that this new approach significantly outperforms previous non-demand-driven caching strategies by offering much higher cached content rate, especially when utilizing the contextual information.

Towards Opportunistic Resource Sharing in Mobile Social Networks: an Evolutionary Game Theoretic Approach

In mobile social networks, the success of resource sharing depends on a high level of cooperations. The motivation of this work is to seek conditions under which cooperation prevails without additional incentive mechanisms such as credit and reputation-based schemes. We apply the Evolutionary Game Theory framework to investigate the formation of cooperation in opportunistic resource sharing. First, we extend the existing Small World In Motion mobility model to preserve real-world localized mobility patterns. On top of the mobility model, a game theoretic model tailored for resource sharing is developed. Preliminary simulation results show that high user cooperation rate emerges when the cost of resource sharing is sufficiently small, even if the Nash Equilibrium of the resource sharing game is non-cooperation. Moreover, we discovered that heterogeneous user mobility patterns promote cooperation.

Enhanced Network Embeddings via Exploiting Edge Labels

Network embedding methods aim at learning low-dimensional latent representation of nodes in a network. While achieving competitive performance on a variety of network inference tasks such as node classification and link prediction, these methods treat the relations between nodes as a binary variable and ignore the rich semantics of edges. In this work, we attempt to learn network embeddings which simultaneously preserve network structure and relations between nodes. Experiments on several real-world networks illustrate that by considering different relations between different node pairs, our method is capable of producing node embeddings of higher quality than a number of state-of-the-art network embedding methods, as evaluated on a challenging multi-label node classification task.

Attention-Based Natural Language Person Retrieval

Following the recent progress in image classification and captioning using deep learning, we develop a novel natural language person retrieval system based on an attention mechanism. More specifically, given the description of a person, the goal is to localize the person in an image. To this end, we first construct a benchmark dataset for natural language person retrieval. To do so, we generate bounding boxes for persons in a public image dataset from the segmentation masks, which are then annotated with descriptions and attributes using the Amazon Mechanical Turk. We then adopt a region proposal network in Faster R-CNN as a candidate region generator. The cropped images based on the region proposals as well as the whole images with attention weights are fed into Convolutional Neural Networks for visual feature extraction, while the natural language expression and attributes are input to Bidirectional Long Short- Term Memory (BLSTM) models for text feature extraction. The visual and text features are integrated to score region proposals, and the one with the highest score is retrieved as the output of our system. The experimental results show significant improvement over the state-of-the-art method for generic object retrieval and this line of research promises to benefit search in surveillance video footage.