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Session search is an information retrieval task that involves a sequence of queries for a complex information need. It is characterized by rich user-system interactions and temporal dependency between queries and between consecutive user behaviors. Recent efforts have been made in modeling session search using the Partially Observable Markov Decision(More)
In this paper, we present a novel deterministic heuristic and a new genetic algorithm to solve the problem of optimal triangulation of Bayesian networks. The heuristic, named MinFillWeight, aims to select variables minimizing the multiplication of the weights on nodes of fill-in edges. The genetic algorithm, named GA-MFW, uses a new rank-reserving crossover(More)
According to the characteristics of the optimal elimination ordering problem in Bayesian networks, a heuristic-based genetic algorithm, a cooperative coevolutionary genetic framework and five grouping schemes are proposed. Based on these works, six cooperative coevolutionary genetic algorithms are constructed. Numerical experiments show that these(More)
This year we participate in the TREC Session Track Task 1. We adopt the Query Change Model (QCM), weighted QCM, re-ranking, clustering, and error analysis in our approaches. The QCM retrieval model is employed to combine all queries in a session. QCM allows documents that are relevant to any query in a session to appear in the final retrieval list. Weighted(More)
In this paper, we present a novel triangulation heuristic and a new genetic algorithm to solve the problem of optimal tree decomposition of Bayesian networks. The heuristic, named MinFillWeight, aims to select variables minimizing the multiplication of the weights on nodes of fill-in edges. The genetic algorithm, named IDHGA, employs a new order-reserving(More)
To solve the problem of searching for an optimal elimination ordering of Bayesian networks, a novel effective heuristic, MinSum Weight, and an ACS approach incorporated with multi-heuristic mechanism are proposed. The ACS approach named MHC-ACS utilizes a set of heuristics to direct the ants moving in the search space. The cooperation of multiple heuristics(More)
Recently, only RDF Schema is considered because storage and query for other Semantic Web data modeling formalisms, especially OWL, is still an open research issue. Persistent storage and query of expressive ontology is a challenging task for the Semantic Web technology. In this paper, by combining reasoning with relational database management system, a(More)
For the optimization problem about triangulation of Bayesian networks, a novel genetic algorithm, DHGA, is proposed in this paper. DHGA employs a heuristic-based mutation operation. Moreover, it uses population diversity to identify stagnation and convergence as well as to guide the search procedure. Experiments on representative benchmarks show that DHGA(More)
To find an optimal elimination ordering for Bayesian networks, a multi-heuristic-based ant colony system named MHC-HS-ACS is proposed. MHC-HS-ACS uses a set of heuristics to guide the ants to search solutions. The heuristic set can evolve with the searching procedure in an adaptive way. MHC-HS-ACS also utilizes a heuristic-based local search to accelerate(More)