To gain information about user interests in Web pages is needed to advance in Web security. An approach to pick up that information includes understanding the client's perusing conduct, examining the Web log records with the procedures of preprocessing and client clustering. Time spent on Web pages and the types of operations show the degree of a Web user's intention. The data set comprises of Web log files obtained by collecting the user logs during a six month period. A new enhanced K-means clustering algorithm proposed in this paper for grouping user based on their preferred Web content and their temporal constraints. The enhanced K-mean clustering calculates initial centroids instead of random choice and uses time intervals to heighten the security and performance. Utilizing this methodology, client access designs with comparable looking practices are assembled into a particular class amid a particular time interval. Also secured communication among the various users groups will be achieved through hill cipher technique.

Enhanced K-Means Clustering Algorithm for Evolving User Groups

The invention discloses a content acquisition method, a content acquisition node and a content network, which belong to the field of communication. The method comprises the following steps of: receiving a content request, wherein the content request at least comprises an identifier of requested contents; judging whether the contents corresponding to the identifier can be locally acquired or not, and if the contents corresponding to the identifier can be locally acquired, locally acquiring the contents corresponding to the identifier; otherwise, querying a name resolution system according to the identifier to obtain a target node corresponding to the identifier, and acquiring the contents corresponding to the identifier from the target node, wherein the name resolution system comprises a hierarchical distributed hash table and a global content resolution structure; and the global content resolution structure supports name aggregation and longest match searching. The node comprises a content agent module and a content acquisition module. The content network comprises the content acquisition node and a transmission network. In the content acquisition method, the content acquisition node and the content network, a content-oriented network architecture is constructed by adopting a distributed scheme, the content delivery efficiency and the safety of the network are improved by introducing an optimization strategy, network investment is reduced, and the network is more adaptive to the rapid increasing of the contents.

Content acquisition method, node and content network

With the explosive growth of web information, search engines have become the mainstream tools of information retrieval (IR). However, a notable problem emerged in the current IR systems is that the input queries are usually too short and too ambiguous to express their actual idea which largely affects the performance of IR systems. In this study, a novel query recommendation technology which suggests a list of related queries is proposed to resolve these problems. The query concepts can be firstly extracted from the web-snippets of the search result returned by the input query. A bipartite graph is subsequently built to identify the related queries, and the query similarity can be calculated by such bipartite graph. Moreover, by analyzing the URLs clicked by users, we find that some tokens appeared in URLs are very meaningful, especial for some typical topic-based pages. Therefore, these potential tokens which can provide a brief description from the subject of the URL are also considered. In order to reveal the real semantics between queries, the approach TF-IQF model is further discussed, and three features of a query, i.e. clicked documents, associated query and reversed query, are utilized in our approach in depth. Such a method could hopefully acquire the comprehensive idea of a query. To investigate how these three features could be used effectively for query recommendation in search engine, we adopt the benchmark evaluation criterions in our experiments, and the experimental results show its promising results in comparison with state of the art methods.

An effective query recommendation approach using semantic strategies for intelligent information retrieval

Ontology matching is an integral part for establishing semantic interoperability. One of the main challenges within the ontology matching operation is semantic heterogeneity, i.e. modeling differences between the two ontologies that are to be integrated. The semantics within most ontologies or schemas are, however, typically incomplete because they are designed within a certain context which is not explicitly modeled. Therefore, external background knowledge plays a major role in the task of (semi-) automated ontology and schema matching. In this survey, we introduce the reader to the general ontology matching problem. We review the background knowledge sources as well as the approaches applied to make use of external knowledge. Our survey covers all ontology matching systems that have been presented within the years 2004–2021 at a well-known ontology matching competition together with systematically selected publications in the research field. We present a classification system for external background knowledge, concept linking strategies, as well as for background knowledge exploitation approaches. We provide extensive examples and classify all ontology matching systems under review in a resource/strategy matrix obtained by coalescing the two classification systems. Lastly, we outline interesting and yet underexplored research directions of applying external knowledge within the ontology matching process.

Background knowledge in ontology matching: A survey

Medical Computational Problem (MCP) solving is related to medical problems and their computerized algorithmic solutions. In this paper, an extension of an ontology-based model to fuzzy logic is presented, as a means to enhance the information retrieval (IR) procedure in semantic management of MCPs. We present herein the methodology followed for the fuzzy expansion of the ontology model, the fuzzy query expansion procedure, as well as an appropriate ontology-based Vector Space Model (VSM) that was constructed for efficient mapping of user-defined MCP search criteria and MCP acquired knowledge. The relevant fuzzy thesaurus is constructed by calculating the simultaneous occurrences of terms and the term-to-term similarities derived from the ontology that utilizes UMLS (Unified Medical Language System) concepts by using Concept Unique Identifiers (CUI), synonyms, semantic types, and broader- narrower relationships for fuzzy query expansion. The current approach constitutes a sophisticated advance for effective, semantics-based MCP-related IR.

Ontology-based Vector Space Model and Fuzzy Query Expansion to Retrieve Knowledge on Medical Computational Problem Solutions

Searching for information by search engines has been gaining popularity in recent years. However, results returned by most Chinese Web search engines usually reach up to thousands or even millions documents, so search results clustering is of critical need for on-line grouping of similar documents to improve user experience while searching collections of Web pages and facilitate browsing Chinese Web pages in a more compact and thematic form. This paper presents a new suffix tree clustering (STC) algorithm for Web search results clustering, which is more suitable for Chinese context. It is built in terms of Chinese words, of which meaningless phrases are ignored by an efficient strategy we proposed. Meanwhile the Chinese synonymy is introduced into the suffix tree to improve the quality of the clusters. Experiments show that the proposed novel STC algorithm has a better performance in precision and speed than original STC.

Search Results Clustering in Chinese Context Based on a New Suffix Tree

Currently, the available architectures for knowledge management are mainly centralized and focus on basic string processing in essence. They tend to ignore that knowledge is distributed and full of semantics in and among complex knowledge-based organizations. Distributed knowledge management appears to solve the issues of distributed knowledge resources. Meanwhile, ontological engineering, which aims at describing the semantics for the knowledge or information, has been increasingly used in artificial intelligence and business in the past several years. Taking this into consideration, an architecture of distributed knowledge management, towards semantic interoperation (SiDKM) by introducing ontological engineering theory and multi-agent system (MAS) for distributed knowledge resources, is proposed in this paper. This architecture involves almost all the core modules in the knowledge management process, and can process large amounts of heterogeneous and distributed knowledge, which enables efficient knowledge sharing, reuse and evolution.

Distributed knowledge management based on ontological engineering and multi-agent system towards semantic interoperation

In this paper, we propose an improved information retrieval model, where the integration of modification-words and head-words is introduced into the representation of user queries and the traditional vector space model. We show how to calculate the weights of combined terms in vectors. We also propose a new strategy to construct the thesaurus in a fuzzy way for query expansion. Through the developed information retrieval system, we can retrieve documents in a relatively narrow search space and meanwhile extend the coverage of the retrieval to the related documents that do not necessarily contain the same terms as the given query. Experiments for testing the retrieval effectiveness have been implemented by using benchmark corpora. Experimental results show that the improved information retrieval system is capable of improving the retrieval performance both in precision and recall rates.

An improved VSM based information retrieval system and fuzzy query expansion

Internet searching seems impossibly difficult with massive various resources available on the Internet. Efficient search strategies are the main concerns for complete and appropriate materials searching. Web search is centralized and depends on crawlers to discover new resources you want, which inevitably encounters with the central node bottleneck problem. So the current search strategy moves toward the peer-to-peer search mechanism, since such search mechanism can be entirely distributed. However, the pure peer-to-peer search is faced with the query message broadcast storm problem as well as the scalable problem. To deal with these problems, a hierarchical peer-to-peer search framework is proposed in the paper. The basic idea includes replacing the central node with a scalable peer-to-peer search sub-network to avoid the bottleneck of the centralized searching. Each node on the search sub-network is a local resource registry, which maintains the index information of some resource description files. For the purpose of obtaining the global knowledge to guide the query routing, the index and routing information distributed at each node of the search sub-network are mapped onto a distributed hash table (DHT). Meanwhile, nodes on the search sub-network are grouped according to resource scopes into a node group and thus the hierarchical characters are introduced. The corresponding query routing scheme also takes the hierarchical and distributed characters into account. The query is firstly propagated to neighboring nodes using a multiple-random-walkers method under the direction of the local registration node, then forwarded to the node group and propagated within group nodes under the instruction of partial hash tables, and finally forwarded between node groups with the supervision of global hash table. A prototype is developed on the common peer-to-peer developing platform JXTA. The testing result on a small-scale network shows the feasibility of the proposed framework.

A query routing scheme based on the hierarchically indexed DHT for hybrid peer-to-peer search

Ontology mapping, with the purpose of finding the semantic correspondences between two existed overlapped ontologies, has been the key technique for solving the integration of ontology-based knowledge systems. Currently, ontology mapping is largely determined manually by domain experts, thus a time-consuming and labor-intensive process. In this paper, we propose an integrated automatic ontology mapping algorithm based on three dimensions of linguistics, structure and instance combining commonsense knowledge with domain knowledge from the systematic point of view. Experimental results on two course ontologies are presented, and show that the algorithm discovers semantic mappings with a high degree of accuracy.

IAOM: an integrated automatic ontology mapping approach towards knowledge integration

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