Given the access score vector on leaf nodes {v'1 the user likes the i(lsisn-th concept leaf node, and use the variable t denotes the number of leaf nodes in ontology concept hierarchy tree. The variable v can be calculated as Figure 1. Part of the rock and mineral fossils meta-data classification ∑F) Automatic construction procedure of the rock and ∑log2①∑aF) mineral fossils domain ontology is shown as follows. Firstly we read the codes of rock and mineral fossils resources from database. In the database, we store the code for every Here the variable FR means how many times the user esource. For example, we use 002317 to represent the fossil Ise 00231713 to represent the ancient spinal animal, and visits the resourcer belonging to concept leaf node /.So 0023171321 to represent the amphibian. Secondly, we far. we have obtained the access score vector on leaf nodes extract the resources'names and relationships according to As is known to all there are semantic relationships the codes. Thirdly, we use the resources'name and between father-child nodes in ontology concept hierarchy relationships to build the meta-data classification hierarchy tree, so we can make use of ontology reasoning technology tree. At last, we build the rock and mineral fossils domain to get the access scores on non-leaf nodes according to the ontology by using the meta-data classification hierarchy tree scores on leaf nodes. Given the hierarchy tree has t leaf according to the OwL language grammar Through the automatic construction of the rock and nodes, we use P, P2,",P, to define all shortest paths mineral fossils domain ontology we get the transmission from root to leaf nodes, and use the node set properties of this ontology. In the user modeling process we mainly use the father-child relationship to compute scores for n,u,"-,ni,) to signify the path P, from the root upper concept nodes according to lower nodes, so the n;o to leaf node n, in hierarchy tree. The score of the node transmission properties basically meet the requirements of the user modeling. The more complete the properties of the ni(0 Sxsy) in the path P, is defined as s(ni ),which concept nodes are, the better the query extension is, so we is calculated as following eed to add symmetry properties, inverse properties and function properties to the concept nodes. In this paper, we get some additional properties from network resources such as wikipedia and add these to the domain hierarchy tree by hx)+10≤x<y hand. After that, we have built an approving domain ontology which contains 493 concept nodes x=y In this section we present a new ontology-based user Here the variable ni(+l) denotes the son node of ni in modeling method. The following three steps take place to path P,, b(ni(+n)means the number of ni(+)'s brother to obtain the users' access scores on the leaf nodes of in the whole tree, and a is a reasoning factor which is ascertained in applications(the parameter a in this paper is ontology reasoning technology and access scores on leaf equal to 1. 8). We can compute for all paths according to the nodes in the ontology tree to get the access scores on non- same way. The score of the node n the user get is given by leaf nodes, finally we merge the access score vector on leaf nodes and score vector on non -leaf nodes build th ontology-based user model denoted by v=v, v2, ". v,) )=∑s(n) (3) The variable v, dlsiss) in the above expression denotes how much the user likes the I-th concept node. The variable After that we can get the access score vector on non-leaf s denotes the number of the nodes in ontology concept odes denoted by v hierarchy tree. We will elaborate on how to build the leaf nodes score vector and non-leaf nodes score vector in the So far. we have obtained score vector y on leaf nodes and vector von non-leaf nodes. After that, we combine vector v with v to generate the ontology-basedLayer One Layer Two Layer Three Figure I. Part of the rock and mineral fossils meta-data classification hierarchy tree. Automatic construction procedure of the rock and mineral fossils domain ontology is shown as follows. Firstly, we read the codes of rock and mineral fossils resources from database. In the database, we store the code for every resource. For example, we use 002317 to represent the fossil, use 00231713 to represent the ancient spinal animal, and use 0023171321 to represent the amphibian. Secondly, we extract the resources' names and relationships according to the codes. Thirdly, we use the resources' name and relationships to build the meta-data classification hierarchy tree. At last, we build the rock and mineral fossils domain ontology by using the meta-data classification hierarchy tree according to the OWL language grammar. Through the automatic construction of the rock and mineral fossils domain ontology we get the transmission properties of this ontology. In the user modeling process we mainly use the father-child relationship to compute scores for upper concept nodes according to lower nodes, so the transmission properties basically meet the requirements of the user modeling. The more complete the properties of the concept nodes are, the better the query extension is, so we need to add symmetry properties, inverse properties and function properties to the concept nodes. In this paper, we get some additional properties from network resources such as Wikipedia and add these to the domain hierarchy tree by hand. After that, we have built an approving domain ontology which contains 493 concept nodes. III. USER MODELING In this section we present a new ontology-based user modeling method. The following three steps take place to build the user models: firstly, we analyze the web server logs to obtain the users' access scores on the leaf nodes of ontology concept hierarchy tree, secondly, we make use of ontology reasoning technology and access scores on leaf nodes in the ontology tree to get the access scores on non￾leaf nodes, finally we merge the access score vector on leaf nodes and score vector on non-leaf nodes to build the ontology-based user model denoted by V = {vI ' v2,", vJ. The variable Vi (1 � i � s) in the above expression denotes how much the user likes the i -th concept node. The variable s denotes the number of the nodes in ontology concept hierarchy tree. We will elaborate on how to build the leaf nodes score vector and non-leaf nodes score vector in the following content. 364 Given the access score vector on leaf nodes v' = {v\, V'2,···, V'I}, we use V'i to represent how much the user likes the i(1 � i � t) -th concept leaf node, and use the variable t denotes the number of leaf nodes in ontology concept hierarchy tree. The variable V'i can be calculated as: V'. = I I Llog2(LRElo FR) j=l J (1) Here the variable FR means how many times the user visits the resource R belonging to concept leaf node Ii . So far, we have obtained the access score vector on leaf nodes. As is known to all there are semantic relationships between father-child nodes in ontology concept hierarchy tree, so we can make use of ontology reasoning technology to get the access scores on non-leaf nodes according to the scores on leaf nodes. Given the hierarchy tree has t leaf nodes, we use PI' P2' .. , PI to define all shortest paths from root to leaf nodes, and use the node set ( niO' nil" .. , niy) to signify the path Pi from the root niO to leaf node niy in hierarchy tree. The score of the node nix (0 � x � y) in the path Pi is defined as s( niJ, which is calculated as following: (2) Here the variable ni(x+l) denotes the son node of nix m path Pi' b(ni(x+l)) means the number of ni(x+l) 's brother in the whole tree, and a is a reasoning factor which is ascertained in applications(the parameter a in this paper is equal to 1.8). We can compute for all paths according to the same way. The score of the node nx the user get is given by I s(nJ = Ls(niJ (3) i = l After that we can get the access score vector on non-leaf nodes denoted by v" = {v'\, V"2"'" v"r} . So far, we have obtained score vector V I on leaf nodes and vector v" on non-leaf nodes. After that, we can combine vector V I with v" to generate the ontology-based