James A. Hendler

The syzygy surfer: (Ab)using the semantic web to inspire creativity

This article discusses our development of a new web engine, the syzygy surfer, which aims to induce a search/browsing experience that is more creative than traditional search. We do this by purposefully combining the ambiguity of natural language with the precision of semantic web technologies. Here, we set out the framework for our investigation and discuss the context and background ideas that are informing the research. This paper offers some preliminary examples taken from our work in progress on the device and suggests the way ahead for future developments and applications.

Semantic Social Network Analysis by Cross-Domain Tensor Factorization

Analyzing “what topics” a user discusses with others is important in social network analysis. Since social relationships can be represented as multiobject relationships (e.g., those composed of a user, another user, and the topic of communication), they can be naturally represented as a tensor. By factorizing the tensor, we can perform communication prediction that predicts links among users and the topics discussed among them. The prediction accuracy, however, is often inadequate for applications because: 1) users usually discuss a variety of topics, and thus the prediction results tend to be biased toward popular domains and 2) topics that are rarely discussed among users trigger the sparsity problem in tensor factorization. Our solution, cross-domain tensor factorization (CrTF), first determines the topic domain by analyzing communication logs among users using the DBpedia knowledge base and creates a tensor composed of users, other users, and the topics of communication for each domain; it avoids strong bias toward particular domains. It then simultaneously factorizes tensors across domains while integrating semantics from DBpedia into factorizations; this solves the sparsity problem. Experiments using Twitter data sets show that CrTF achieves higher accuracy than the state-of-the-art tensor-based methods and extracts key topics and social influencers for each domain.

In-context query reformulation for failing SPARQL queries

Knowledge bases for decision support systems are growing increasingly complex, through continued advances in data ingest and management approaches. However, humans do not possess the cognitive capabilities to retain a bird’s-eyeview of such knowledge bases, and may end up issuing unsatisfiable queries to such systems. This work focuses on the implementation of a query reformulation approach for graph-based knowledge bases, specifically designed to support the Resource Description Framework (RDF). The reformulation approach presented is instance-and schema-aware. Thus, in contrast to relaxation techniques found in the state-of-the-art, the presented approach produces in-context query reformulation.