Knowledge Graph Embedding with Iterative Guidance from Soft Rules

Embedding knowledge graphs (KGs) into continuous vector spaces is a focus of current research. Combining such an embedding model with logic rules has recently attracted increasing attention. Most previous attempts made a one-time injection of logic rules, ignoring the interactive nature between embedding learning and logical inference. And they focused only on hard rules, which always hold with no exception and usually require extensive manual effort to create or validate. In this paper, we propose Rule-Guided Embedding (RUGE), a novel paradigm of KG embedding with iterative guidance from soft rules. RUGE enables an embedding model to learn simultaneously from 1) labeled triples that have been directly observed in a given KG, 2) unlabeled triples whose labels are going to be predicted iteratively, and 3) soft rules with various confidence levels extracted automatically from the KG. In the learning process, RUGE iteratively queries rules to obtain soft labels for unlabeled triples, and integrates such newly labeled triples to update the embedding model. Through this iterative procedure, knowledge embodied in logic rules may be better transferred into the learned embeddings. We evaluate RUGE in link prediction on Freebase and YAGO. Experimental results show that: 1) with rule knowledge injected iteratively, RUGE achieves significant and consistent improvements over state-of-the-art baselines; and 2) despite their uncertainties, automatically extracted soft rules are highly beneficial to KG embedding, even those with moderate confidence levels. The code and data used for this paper can be obtained from https://github.com/iieir-km/RUGE.Comment: To appear in AAAI 201

Neural Natural Language Inference Models Enhanced with External Knowledge

Modeling natural language inference is a very challenging task. With the availability of large annotated data, it has recently become feasible to train complex models such as neural-network-based inference models, which have shown to achieve the state-of-the-art performance. Although there exist relatively large annotated data, can machines learn all knowledge needed to perform natural language inference (NLI) from these data? If not, how can neural-network-based NLI models benefit from external knowledge and how to build NLI models to leverage it? In this paper, we enrich the state-of-the-art neural natural language inference models with external knowledge. We demonstrate that the proposed models improve neural NLI models to achieve the state-of-the-art performance on the SNLI and MultiNLI datasets.Comment: Accepted by ACL 201

Making modality: transmodal composing in a digital media studio.

The multiple media that exist for communication have historically been theorized as possessing different available means for persuasion and meaning-making. The exigence of these means has been the object of theoretical debate that ranges from cultural studies, language studies, semiology, and philosophies of the mind. This dissertation contributes to such debates by sharing the results of an ethnographically informed study of multimedia composing in a digital media studio. Drawing from Cultural Historical Activity Theory and theories of enactive perception, I analyze the organizational and infrastructural design of a media studio as well as the activity of composer/designers working in said studio. Throughout this analysis I find that implicit in the organization and infrastructure of the media studio is an ethos of conceptualizing communication technology as a legitimizing force. Such an ethos is troubled by my analysis of composer/designers working in the studio, whose activities do not seek outside legitimization but instead contribute to the media milieu. Following these analyses, I conclude that media’s means for persuasion and meaning-making emerge from local practices of communication and design. Finally, I provide a framework for studying the emergence of such means

Interrogating the technical, economic and cultural challenges of delivering the PassivHaus standard in the UK.

A peer-reviewed eBook, which is based on a collaborative research project coordinated by Dr. Henrik Schoenefeldt at the Centre for Architecture and Sustainable Environment at the University of Kent between May 2013 and June 2014. This project investigated how architectural practice and the building industry are adapting in order to successfully deliver Passivhaus standard buildings in the UK. Through detailed case studies the project explored the learning process underlying the delivery of fourteen buildings, certified between 2009 and 2013. Largely founded on the study of the original project correspondence and semi-structured interviews with clients, architects, town planners, contractors and manufacturers, these case studies have illuminated the more immediate technical as well as the broader cultural challenges. The peer-reviewers of this book stressed that the findings included in the book are valuable to students, practitioners and academic researchers in the field of low-energy design. It was launched during the PassivHaus Project Conference, held at the Bulb Innovation Centre on the 27th June 2014