Ontology matching determines the correspondences between concepts and relations of related ontologies. In this paper, we put forward an ontology hierarchies matching approach based on lattices alignment. The proposed ...
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Ontology matching determines the correspondences between concepts and relations of related ontologies. In this paper, we put forward an ontology hierarchies matching approach based on lattices alignment. The proposed lattice-based matching algorithm can be utilized not only in matching processes between two ontologies, but also in annotation processes between an ontology and its corresponding resources. Experiments on spatiotemporal ontology annotation have been carried out which shown the applicability of the approach.
Both WordNet and Chinese Classified Thesaurus(CCT) are widely used in information retrieval and management systems. In this paper we propose a novel approach for building bilingual ontologies based on these existing k...
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It widely realized that the integration of database and information retrieval techniques will provide users with a wide range of high quality services. In this paper, we study processing an l-keyword query, p1, p2, , ...
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As the memory capacity increases and the hardware becomes cheaper, main memory databases (MMDB) have come true and been used in more and more applications, because they can provide better response time and throughputs...
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Sequential pattern mining is an important method in data mining. Traditional mining algorithms are not adapted to the fast, unlimited, continuous and dynamic data stream because they are multiple pass in scanning data...
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Sequential pattern mining is an important method in data mining. Traditional mining algorithms are not adapted to the fast, unlimited, continuous and dynamic data stream because they are multiple pass in scanning database. Some approximate sequential pattern mining algorithms are proposed recently which cost too many system resources in sequence compare process. A sequential compare method based on Levenshtein-Automata is proposed in this paper. This method build state conversion model with pretreatment which can finish computing the sequences' similarity in linear time. A combination of Levenshtein-Automata computation and common computation of edit distance is presented in allusion to the Levenshtein-Automata's problem of using too much memory, so a tradeoff between time cost and space cost is implemented. The experiment result shows this method is effective and efficient.
The skyline query is frequently used to find a set of dominating data points (called skyline points) in a multidimensional dataset It is one of the most important query methods for database, datastream, P2P networks. ...
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The skyline query is frequently used to find a set of dominating data points (called skyline points) in a multidimensional dataset It is one of the most important query methods for database, datastream, P2P networks. However, it has not been implemented in sensor networks due to limited energy of the sensor nodes. This paper presents an energy-efficient approximate skyline query scheme for sensor networks. According to the experiments, this scheme can greatly improve the lifetime of sensor networks compared to the naive skyline query.
In domain ontologies, there is usually no weight assigned to the link between two concepts. This has been considered as one of main obstacles in using ontologies. Semantic Association (SA) is to depict the correlation...
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In domain ontologies, there is usually no weight assigned to the link between two concepts. This has been considered as one of main obstacles in using ontologies. Semantic Association (SA) is to depict the correlation of two concepts, and can be measured as the weight of the link. In this paper, we defined Degree of Association (DOA) to measure SA from a concept to its direct-related concept in domain ontology, and proposed a Language-Model-Based Method (LMBM) to compute DOA. Our idea comes from the intuition that the semantic relationship between two concepts implies certain semantic association of them. We took probabilistic model for computing DOA, and used Maximum Likelihood Estimation to estimate parameters. We tested the proposed method on two different domain ontologies, and applied it in experiments of semantic query expansion. Experimental results show the benefit of our approach and demonstrate the promising effectiveness over semantic query expansion.
In contextual information retrieval, the retrieval of information depends on the time and place of submitting query, history of interaction, task in hand, and many other factors that are not given explicitly but impli...
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In contextual information retrieval, the retrieval of information depends on the time and place of submitting query, history of interaction, task in hand, and many other factors that are not given explicitly but implicitly lie in the interaction and surroundings of searching, namely the context. User's cognition is one of important contextual factors for understanding his or her personal needs. We propose a model called DOSAM to get user's individual cognitive structure on domain knowledge. DOSAM is developed from the spreading-activation model of psychology and is established on the domain ontology. The cost analysis of algorithm shows that it is feasible to get cognitive structure by DOSAM. Personalized search experimental results on digital library indicate that DOSAM can help improve the search effectiveness and user's satisfaction.
In this paper, based on concept lattices and dual concept lattices, we introduced a pair of rough set approximation operators within formal contexts. The proposed approximations operators don't require the equival...
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In this paper, based on concept lattices and dual concept lattices, we introduced a pair of rough set approximation operators within formal contexts. The proposed approximations operators don't require the equivalence relation any more. The properties of the proposed approximation operators are discussed in details.
In this paper, we propose a new Dynamic datacentric Storage (DDS) mechanism in wireless sensor network. DDS, which is aware of the data distributions of the network, dynamically adjusts the mappings from sensor readin...
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In this paper, we propose a new Dynamic datacentric Storage (DDS) mechanism in wireless sensor network. DDS, which is aware of the data distributions of the network, dynamically adjusts the mappings from sensor readings to the storage points to reduce the cost of storing these readings, as well as to balance the storage and workload in the network. Moreover, it takes advantage of the GPSR routing protocol to store multiple copies of readings to improve the robustness of the network with little overhead. Simulation results show that the approach is more energy-efficient and robust than other data-centric schemes.
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