The ability to predict the activities of users is an important one for recommender systems and analyses of social media. User activities can be represented in terms of relationships involving three or more things (e.g. when a user tags items on a webpage or tweets about a location he or she visited). Such relationships can be represented as a tensor, and tensor factorization is becoming an increasingly important means for predicting users' possible activities. However, the prediction accuracy of factorization is poor for ambiguous and/or sparsely observed objects. Our solution, Semantic Sensitive Tensor Factorization (SSTF), incorporates the semantics expressed by an object vocabulary or taxonomy into the tensor factorization. SSTF first links objects to classes in the vocabulary (taxonomy) and resolves the ambiguities of objects that may have several meanings. Next, it lifts sparsely observed objects to their classes to create augmented tensors. Then, it factorizes the original tensor and augmented tensors simultaneously. Since it shares semantic knowledge during the factorization, it can resolve the sparsity problem. Furthermore, as a result of the natural use of semantic information in tensor factorization, SSTF can combine heterogeneous and unbalanced datasets from different Linked Open Data sources. We implemented SSTF in the Bayesian probabilistic tensor factorization framework. Experiments on publicly available large-scale datasets using vocabularies from linked open data and a taxonomy from WordNet show that SSTF has up to 12% higher accuracy in comparison with state-of-the-art tensor factorization methods.

Linking named mentions detected in a source document to an existing knowledge base provides disambiguated entity referents for the mentions.This allows better document analysis, knowledge extraction and knowledge base population.Most of the previous research extensively exploited the linguistic features of the source documents in a supervised or semi-supervised way.These systems therefore cannot be easily applied to a new language or domain.In this paper, we present a novel unsupervised algorithm named Quantified Collective Validation that avoids excessive linguistic analysis on the source documents and fully leverages the knowledge base structure for the entity linking task.We show our approach achieves stateof-the-art English entity linking performance and demonstrate successful deployment in a new language (Chinese) and two new domains (Biomedical and Earth Science).Experiment datasets and system demonstration are available at http://tw.rpi.edu/web/doc/hanwang_emnlp_2015 for research purpose.

Predicting human activities is important for improving recommender systems or analyzing social relationships among users. Those human activities are usually repre- sented as multi-object relationships (e.g. user’s tagging activities for items or user’s tweeting activities at some locations). Since multi-object relationships are naturally represented as a tensor, tensor factorization is becom- ing more important for predicting users’ possible ac- tivities. However, its prediction accuracy is weak for ambiguous and/or sparsely observed objects. Our so- lution, Semantic data Representation for Tensor Fac- torization (SRTF), tackles these problems by incorpo- rating semantics into tensor factorization based on the following ideas: (1) It first links objects to vocabu- laries/taxonomies and resolves the ambiguity caused by objects that can be used for multiple purposes. (2) It next links objects to composite classes that merge classes in different kinds of vocabularies/taxonomies (e.g. classes in vocabularies for movie genres and those for directors) to avoid low prediction accuracy caused by rough-grained semantics. (3) It then lifts sparsely observed objects into their classes to solve the sparsity problem for rarely observed objects. To the best of our knowledge, this is the first study that leverages seman- tics to inject expert knowledge into tensor factorization. Experiments show that SRTF achieves up to 10% higher accuracy than state-of-the-art methods.

The important topic of global climate change builds on a huge collection of scientific research. It is common for agencies releasing climate change information to be served with requests for all supporting materials resulting in a particular conclusion. Capturing and presenting global change provenance, linking to the research papers, data sets, models, analyses, observations, satellites, etc., that support the key research findings in this domain can increase understanding and aid in reproducibility of results and conclusions. The U.S. Global Change Research Program is now coordinating the production of a national climate assessment (NCA) that presents our best understanding of global change. We are now developing a global change information system that will present the content of that report and its provenance, including the scientific support for the findings of the assessment. We are using an approach that will present this information both through a human accessible Web site as well as a machine-readable interface for automated mining of the provenance graph. We plan to use the developing World Wide Web Consortium (W3C) PROV data model and ontology for this system. This paper will describe an overview of the process of developing the NCA and how the provenance trail of the report and each of the technical inputs can be captured and represented using the W3C PROV ontology. This will improve the visibility into the assessment process, increase understanding and possibility of reproducibility, and ultimately increase the credibility and trust of the resulting report.