Nowadays enterprises request information technologies that leverage structured and unstructured information for providing a single integrated view of business problems in order to foster better business process management and decision making. The growing interest in semantic technologies is due to the limitation of existing enterprise information technologies to answer these new challenging nee...

It is expensive and time-consuming to collect sufficient labeled data build human activity recognition (HAR) models. Training on existing often makes the model biased towards distribution of training data, thus might perform terribly test with different distributions. Although efforts transfer learning domain adaptation try solve above problem, they still need access unlabeled target domain, wh...

As with many domains, information retrieval and knowledge management (IR/KM) in agriculture suffers from the problems of semantic heterogeneity, making it difficult for providers to disseminate their services effectively and for users to retrieve the information they need. Based on the analysis of resources in the domain of agriculture, this paper proposes a) application profiles for dealing wi...

RESTful services are increasingly gaining traction over WS-* ones. As with WS-* services, their semantic annotation can provide benefits in tasks related to their discovery, composition and mediation. In this paper we present an approach to automate the semantic annotation of RESTful services using a cross-domain ontology like DBpedia, domain ontologies like GeoNames, and additional external re...

One of the key challenges in electronic government (e-government) is the development of systems that can be easily integrated and interoperated to provide seamless services delivery to citizens. In recent years, Semantic Web technologies based on ontology have emerged as promising solutions to the above engineering problems. However, current research practicing semantic development in e-governm...

Many applications need a lexicon that represents semantic information but acquiring lexical information is time consuming. We present a corpus-based bootstrapping algorithm that assists users in creating domain-specific semantic lexicons quickly. Our algorithm uses a representative text corpus for the domain and a small set of ‘seed words’ that belong to a semantic class of interest. The algori...

With the expanding of the Semantic Web and the availability of numerous ontologies which provide domain background knowledge and semantic descriptors to the data, the amount of semantic data is rapidly growing. The data mining community is faced with a paradigm shift: instead of mining the abundance of empirical data supported by the background knowledge, the new challenge is to mine the abunda...

Ontologies as well as controlled vocabularies, and domain-specific terminologies are increasingly being developed to capture semantics for life science applications. A requirement for database systems is to allow the domain of a table column to be such hierarchical terminologies and to provide query support. The paper addresses this requirement by introducing the notion of a semantic domain for...

In this paper, we introduce a common semantic framework for developing and formally modeling use cases and task models. Use cases are the notation of choice for functional requirements specification and documentation, whereas task models are used as a starting point for user interface design. Based on their intrinsic characteristics we devise an intermediate semantic domain for use cases and fo...

Computational cognitive models developed so far do not incorporate any effect of individual differences in domain knowledge of users in predicting user clicks on search result pages. We address this problem using a cognitive model of information search which enables us to use two semantic spaces having low (general semantic space) and high (special semantic space) amount of medical and health r...