Topic modeling with network regularization(About) In this paper, we formally define the problem of topic modeling with network structure (TMN). We propose a novel solution to this problem, which regularizes a statistical topic model with a harmonic regularizer based on a graph structure in the data. The proposed method combines topic modeling and social network analysis, and leverages the power of both statistical topic models and discrete regularization. The output of this model can summarize well topics in text, map a topic onto the network, and discover topical communities.
Apex Data & Knowledge Management Lab(About) Apex Data & Knowledge Management Lab focuses on the research and development in the data and knowledge management area. Current interests include Next Generation Search and Retrieval, Ontology Theory and Engineering, and Semantic Web.
COMM: Core Ontology on Multimedia(About) Semantic descriptions of non-textual media available on the web can be used to facilitate retrieval and presentation of media assets and documents containing them. While technologies for multimedia semantic descriptions already exist, there is as yet no formal description of a high quality multimedia ontology that is compatible with existing (semantic) web technologies. We propose COMM - A Core Ontology for Multimedia based on both the MPEG-7 standard and the DOLCE foundational ontology.
Structured Objects in OWL: Representation and Reasoning. In Proc. of the 17th Int. World Wide Web Conference (WWW 2008), Beijing(About) Very good presentation at WWW 2008. Nominated for the best paper award
Abstract: Applications of semantic technologies often require the representation of and reasoning with structured objects—that is, objects composed of parts connected in complex ways. Although OWL is a general and powerful language, its class descriptions and axioms cannot be used to describe arbitrarily connected structures. An OWL representation of structured objects can thus be underconstrained, which reduces the inferences that can be drawn and causes performance problems in reasoning. To address these problems, we extend OWL with description graphs, which allow for the description of structured objects in a simple and precise way. To represent conditional aspects of the domain, we also allow for SWRL-like rules over description graphs. Based on an observation about the nature of structured objects, we ensure decidability of our formalism. We also present a hypertableau-based decision procedure, which we implemented in the HermiT reasoner. To evaluate its performance, we have extracted description graphs from the GALEN and FMA ontologies, classified them successfully, and even detected a modeling error in GALEN.