of references. However, there is often additional relational information in the data. Specifically, references to different entities may cooccur. In these cases, collective entity resolution, in which entities for cooccurring references are determined jointly rather than independently, can improve entity

for answering queries over such ‘unclean ’ databases. Since collective entity resolution approaches — where related references are resolved jointly — have been shown to be more accurate than independent attribute-based resolution, we focus on adapting collective resolution for answering queries. We propose a

in ML, such as firstorder probabilistic models (e.g., Markov and Bayesian networks) and advanced probabilistic inference techniques. A key benefit of these ML tools comes from their purely collective nature: match evidence for related entities can be collectively reinforced into high-probability EM

Entity disambiguation works by linking am-biguous mentions in text to their correspond-ing real-world entities in knowledge base. Re-cent collective disambiguation methods en-force coherence among contextual decisions at the cost of non-trivial inference processes. We propose a fast collective

decisions, i.e., the entities in the same document should be semantically related to each other. In these cases, Collective Entity Linking, in which the name mentions in the same document are linked jointly by exploiting the interdependence between them, can improve the entity linking accuracy. This paper

in such experiments through more general, non-numerical capacities, such as sensitivity to perceptual features or mechanisms of object tracking. We report here a study showing that 5-month-olds can determine the number of collective entities – moving groups of items – when non-numerical percep-tual factors

metadata of the entity. These techniques are limited because they do not make use of the underlying entities that co-occur in the text. Alternatively, collective entity linking is a better solution that resolves all names in a document simultaneously. This is motivated by the fact that in a coherent

Abstract — This paper presents and analyzes an architecture that exploits the serendipitous movement of mobile agents in an environment to collect sensor data in sparse sensor networks. The mobile entities, called MULEs, pick up data from sensors when in close range, buffer it, and drop off

Organizational routines and capabilities are thorny constructs, but their complexity has been largely underappreciated. In this article, the authors illustrate how new and more complex understandings of organizational routines and capabilities can be generated. They do so by breaking them into parts and mapping their interrelationships. Because component parts of routines and capabilities exist at different levels of analysis, the proposal to investigate their multiple relationships contributes to bridging the micro–macro divide in management. Specifically, the authors show how new analytical and methodological techniques can catalyze future research.

. In this paper, we pro-pose a knowledge-incentive approach for entity annotation in biomedicine, and present MeDetect, a prototype system that we developed based on this approach. With this approach, we over-come the problems of previous works using LOD-based collective annotation. Finally, we present