Abstract-Trust is an important yet complex and little understood dyadic relation among actors in a social network. There are many dimensions to trust; trust plays an important role in the formation of coalitions in social networks, in assessing quality and credibility of information as well as in determining how information flows through the network.In this paper, we present algorithmically quantifiable measures of trust which can be determined from the communication behavior of the actors in a social communication network. The basis for our study is a proposition that trust results in likely communication behavior patterns which are statistically different from random communication in a network. Detecting the statistically significant realizations of this trust-like behavior allows us to develop a quantitative measure of who-trusts-whom relation in the network.Since our measure of trust is based on quantifiable behavior, we call it behavioral trust. We develop algorithms to efficiently compute behavioral trust and we validate these measures on the Twitter network.

No abstract

No abstract

Linked Data provide many benefits to data consumers, but many publicly available datasets are still released in the Comma Separated Values (CSV) format, a ubiquitous common denominator. We introduce a methodology to transform such datasets into Linked Data. Our design is based on requirements identified while surveying existing governmental datasets released by data.gov. We present an implementationindependent RDF vocabulary to describe how a CSV dataset should be promoted into Linked Data, and use a Java-based converter to produce 5.3 billion RDF triples from 312 data.gov datasets.

Abstract. As SPARQL endpoints are increasingly used to serve linked data, their ability to scale becomes crucial. Although much work has been done to improve query evaluation, little has been done to take advantage of caching. Effective solutions for caching query results can improve scalability by reducing latency, network IO, and CPU overhead. We show that simple augmentation of the database indexes found in common SPARQL implementations can directly lead to effective caching at the HTTP protocol level. Using tests from the Berlin SPARQL benchmark, we evaluate the potential of such caching to improve overall efficiency of SPARQL query evaluation.