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Information on the web and web services that are revised by stakeholders is growing incredibly. The presentation of this information has shifted from a representational model of web information with loosely clustered terminology to semi-formal terminology and even to formal ontology. Mediation (i.e., mapping) is required for systems and services to share information. Mappings are established between ontologies in order to resolve terminological and conceptual incompatibilities. Due to new discoveries in the field of information sharing, the body of knowledge has become more structured and refined. The domain ontologies that represent bodies of knowledge need to be able to accommodate new information. This allows for the ontology to evolve from one consistent state to another. Changes in resources cause existing mappings between ontologies to be unreliable and stale. This highlights the need for mapping evolution (regeneration) as it would eliminate the discrepancies from the existing mappings. In order to re-establish the mappings between dynamic ontologies, the existing systems require a complete mapping process to be restructured, and this process is time consuming. This paper proposes a mapping reconciliation approach between the updated ontologies that has been found to take less time to process compared to the time of existing systems when only the changed resources are considered and also eliminates the staleness of the existing mappings. The proposed approach employs the change history of ontology in order to store the ontology change information, which helps to drastically reduce the reconciliation time of the mappings between dynamic ontologies. A comprehensive evaluation of the performance of the proposed system on standard data sets has been conducted. The experimental results of the proposed system in comparison with six existing mapping systems are provided in this paper using 13 different data sets, which support our claims.

Retrieving representative information from large-scale data becomes an important research issue nowadays, especially in the context of mobile business/search where the screen size and navigability are limited. This paper focuses on certain aspects of representativeness in database queries and web search, and proposes an approach to extracting a subset of results from original search results in light of high coverage and low redundancy. In the paper, the notion of λ -represent is introduced, which enables us to describe the λ -represent relationship between the sets of data objects. Then, the λ -representative problem is formulated as an extension of the typical set covering problem, which leads to developing a heuristic approach (namely, LamRep) to coping with the problem effectively and efficiently. Notably, LamRep is incorporated with a “vote” mechanism, enhanced with an algorithmic acceleration strategy. Data experiments on benchmark data and a real-world example show that LamRep outperforms the other approaches.

With the proliferation of social media, the number of active web-users is rapidly increasing these days. They create and maintain their personal web-profiles, and use them to interact with others in the cyber-space. Currently two major problems are being faced to automatically identify these web-users and correlate their web-profiles. First is the presence of namesakes on the Web, and the second is the use of alias names. In this paper, we propose a context-based text mining approach to discover alias names for all the namesakes sharing a common name on the Web, and leave the task of selecting the namesake of interest on part of the user. The proposed method employs a search-engine API to retrieve relevant webpages for a given name. The retrieved webpages are modeled into a graph, and a clustering algorithm is applied to disambiguate the webpages. Thereafter each obtained cluster standing for a namesake is mined for alias identification following a text pattern based statistical technique. The existing research works do not consider the presence of namesakes on the Web to mine aliases, which is impractical. The novelty of the proposed approach lies in discovering this drawback of existing works. Additionally the contribution includes the disambiguation technique that does not need to have a pre-determined number of clusters to be generated and the light-weight text pattern based alias mining technique. The number of clusters in the proposed method is rather determined dynamically by the inflation parameter, the pre-determination of which is comparatively much easier. Experimental results on different components demonstrate the robustness of the proposed alias mining approach. This paper also brings forth the significance of alias mining to the problem of suspect monitoring and tracking on the Web.

In this paper we address the issue of continuous keyword queries on multiple textual streams and explore techniques for extracting useful information from them. The paper represents, to our best knowledge, the first approach that performs keyword search on a multiplicity of textual streams. The scenario that we consider is quite intuitive; let's assume that a research or financial analyst is searching for information on a topic, continuously polling data from multiple (and possibly heterogeneous) text streams, such as RSS feeds, blogs, etc. The topic of interest can be described with the aid of several keywords. Current filtering approaches would just identify single text streams containing some of the keywords. However, it would be more flexible and powerful to search across multiple streams, which may collectively answer the analyst's question. We present such model that takes in consideration the continuous flow of text in streams and uses efficient pipelined algorithms such that results are output as soon as they are available. The proposed model is evaluated analytically and experimentally, where the Enron dataset and a variety of blog datasets are used for our experiments.

This paper explores the development and use of a qualitative reasoning system for describing consistency between different geographic data sets. Consistency is closely related to issues of uncertainty and interoperability in geographic information, and the paper assesses how automated reasoning about consistency can be used to support the integration of heterogeneous geographic data sets. The system developed is based on description logic. The decidability and tractability characteristics of description logic allow consistency checking to be deferred during data integration, so minimizing the information loss that usually accompanies any data integration task. Further, the user interface allows users to negotiate with the system in defining consistency. The results of the research suggest that further work could significantly increase the level of automation for many geographic data integration tasks.

Clustering is a popular data analysis and data mining technique. However, applying traditional clustering algorithms directly to a database is not straightforward due to the fact that a database usually consists of structured and related data; moreover, there might be several object views of the database to be clustered, depending on a data analyst's particular interest. Finally, in many cases, there is a data model discrepancy between the format used to store the database to be analyzed and the representation format that clustering algorithms expect as their input. These discrepancies have been mostly ignored by current research.This paper focuses on identifying those discrepancies and on analyzing their impact on the application of clustering techniques to databases. We are particularly interested in the question on how clustering algorithms can be generalized to become more directly applicable to real-world databases. The paper introduces methodologies, techniques, and tools that serve this purpose. We propose a data set representation framework for database clustering that characterizes objects to be clustered through sets of tuples, and introduce preprocessing techniques and tools to generate object views based on this framework. Moreover, we introduce bag-oriented similarity measures and clustering algorithms that are suitable for our proposed data set representation framework. We also demonstrate that our approach is capable of dealing with relationship information commonly found in databases through the bag-oriented clustering. We also argue that our bag-oriented data representation framework is more suitable for database clustering than the commonly used flat file format and produce better quality of clusters.

Document Information retrieval consists of finding the documents in a collection of documents that are the most relevant to a user query. Information retrieval techniques are widely-used by organizations to facilitate the search for information. However, applying traditional information retrieval techniques is time consuming for large document collections. Recently, cluster-based information retrieval approaches have been developed. Although these approaches are often much faster than traditional approaches for processing large document collections, the quality of the documents retrieved by cluster-based approaches is often less than that of traditional approaches. To address this drawback of cluster-based approaches, and improve the performance of information retrieval both in terms of runtime and quality of retrieved documents, this paper proposes a new cluster-based information retrieval approach named ICIR (Intelligent Cluster-based Information Retrieval). The proposed approach combines k-means clustering with frequent closed itemset mining to extract clusters of documents and find frequent terms in each cluster. Patterns discovered in each cluster are then used to select the most relevant document clusters to answer each user query. Four alternative heuristics are proposed to select the most relevant clusters, and two alternative heuristics for choosing documents in the selected clusters. Thus, eight versions of the proposed approach are obtained. To validate the proposed approach, extensive experiments have been carried out on well-known document collections. Results show that the designed approach outperforms traditional and cluster-based information retrieval approaches both in terms of execution time and quality of the returned documents.

In this paper, we examine the problem of building a user profile from a set of documents. This profile will consist of a subset of the most representative terms in the documents that best represent user preferences or interests. Inspired by the discrete concentration theory we have conducted an axiomatic study of seven properties that a selection function should fulfill: the minimum and maximum uncertainty principle, invariant to adding zeros, invariant to scale transformations, principle of nominal increase, transfer principle and the richest get richer inequality. We also present a novel selection function based on the use of similarity metrics, and more specifically the cosine measure which is commonly used in information retrieval, and demonstrate that this verifies six of the properties in addition to a weaker variant of the transfer principle, thereby representing a good election approach. The theoretical study was complemented with an empirical study to compare the performance of different selection criteria (weight- and unweight-based) using real data in a parliamentary setting. In this study, we analyze the performance of the different functions focusing on the two main factors affecting the selection process: profile size (number of terms) and weight distribution. These profiles are then used in a document filtering task to show that our similarity-based approach performs well in terms not only of recommendation accuracy but also efficiency (we obtain smaller profiles and consequently faster recommendations)., This work has been funded by the Spanish Ministerio de Economía y Competitividad under projects TIN2013-42741-P and TIN2016-77902-C3-2-P, and the European Regional Development Fund ( ERDF-FEDER ).

Collaborative Filtering (CF) is one of the most successful recommendation techniques. Recently, implicit trust-based recommendation approaches have emerged that incorporate implicit trust information into CF in order to improve recommendation performance. Previous implicit trust models assume that all users have the same perception of ratings. However, although all users employ members of the same rating domain (e.g. ratings on a 1–5 scale), each individual has his own interpretations about a rating domain in order to express his preferences. Thus, it is reasonable that a user's rating vector has some degree of uncertainty, depending upon the rating usage trend of that user. In this paper, we present a new approach for confidence modeling in the context of recommender systems. The idea of this modeling is that confidence in a particular user depends not only on the trust in the opinions of that user but also on the certainty of these opinions. Based on this idea, we propose a new Confidence-Based Recommendation (CBR) approach. This approach employs four different confidence models that derive the users’ and items’ confidence values from both local and global perspectives. Experimental results on real-world data sets demonstrate the effectiveness of the proposed approach.

It is well known that recommender systems rely on the similarity between items to be recommended. Most current research projects in this area utilize traditional similarity measurement algorithms, such as cosine distance or derivatives of these. However, the most challenging problem facing these approaches is to quantify the non-numerical attributes of items. This is quite intractable and cannot be solved with regular similarity measurement algorithms. This paper proposes two novel methods, the Taxonomic Trees Similarity Measurement (TTSM) and the Decomposed Structures Similarity Measurement (SDSM), so that the similarities between the textual attributes can be measured using numeric values after they have been quantified. Also, the quantifying process is completely based on the semantic meanings of the textual terms. Furthermore, a maximized term matching (MTM) mechanism is induced and applied to the group-based textual attributes of items in recommender systems. Finally, we evaluate our methods by implementing a recipe recommender system which achieves a 74.4% overall satisfaction rate as evaluated by real users.

Providing very accurate recommendations to end users has been nowadays recognized to be just one of the tasks an effective recommender system should accomplish. While predicting relevant suggestions, attention needs to be paid also to their diversification in order to avoid monotony in the returned list of recommendations. In this paper we focus on modeling user propensity toward selecting diverse items, where diversity is computed by means of content-based item attributes. We then exploit such modeling to present a novel approach to re-arrange the list of Top-N items predicted by a recommendation algorithm, with the aim of fostering diversity in the final ranking. An extensive experimental evaluation proves the effectiveness of the proposed approach as well as its ability to improve also novelty and catalog coverage values.

Microblogging services, such as Twitter, are very popular for information release and dissemination. Analyzing the sentiments in massive microblog messages is useful for sensing the public's opinions on various topics, which has wide applications in both academic and industrial fields. However, microblog sentiment analysis is a challenging task, because microblog messages are short and noisy, and contain massive user-invented acronyms and informal words. It is expensive and time-consuming to manually annotate sufficient samples for training an accurate and robust microblog sentiment classifier. Fortunately, unlabeled microblog messages can provide a lot of useful sentiment knowledge. For example, emoticons are frequently used in microblog messages and they usually indicate sentiment orientations. In this paper, we propose to extract useful sentiment knowledge from massive unlabeled messages to enhance microblog sentiment classification. Three kinds of sentiment knowledge, i.e., contextual similarity knowledge, word-sentiment knowledge, and contextual polarity knowledge, are explored. We propose a unified framework to incorporate the heterogenous sentiment knowledge into the learning of microblog sentiment classifiers. An efficient optimization method based on ADMM is introduced to solve the model of our framework and an accelerated algorithm is proposed to tackle the most time-consuming step. Extensive experiments were conducted on three benchmark Twitter datasets. The experimental results show that our approach can improve the performance of microblog sentiment classification effectively and efficiently.

The skyline query has been extensively explored as one of popular techniques to filter uninteresting data objects, which plays an important role in many real-life applications such as multi-criteria decision making and personalized services. This query has also been incorporated into commercial database systems for supporting preference queries. However, a skyline query may retrieve too many objects to analyze intensively especially for high-dimensional datasets. As a result, k-dominant skyline query has been introduced to control the number of the objects retrieved. Existing algorithms for k-dominant skyline queries only aim at complete data, which is not well-suited for incomplete data, even though incomplete data is pervasive in scientific research and real life, due to delivery failure, no power of battery, accidental loss, etc. In this paper, we systematically study the problem of k-dominant skyline queries on incomplete data (IkDS), where the data objects might miss their attribute values. We formalize the IkDS query and then present three efficient algorithms for finding k-dominant skyline objects over incomplete data. Several novel concepts/techniques are utilized including local skyline, dominance ability, and bitmap index on incomplete data to shrink the search space. In addition, we extend our techniques to tackle two interesting variants, i.e., weighted dominant skyline query and top-ź dominant skyline query, over incomplete data. Extensive experiments using both real and synthetic data sets demonstrate the performance of our proposed algorithms.

In this paper, we discuss a contribution towards the integration of social information in the index structure of an IR system. Since each user has his/her own understanding and point of view of a given document, we propose an approach in which the index model provides a Personalized Social Document Representation (PerSaDoR) of each document per user based on his/her activities in a social tagging system. The proposed approach relies on matrix factorization to compute the PerSaDoR of documents that match a query, at query time. The complexity analysis shows that our approach scales linearly with the number of documents that match the query, and thus, it can scale to very large datasets. PerSaDoR has been also intensively evaluated by an offline study and by a user survey operated on a large public dataset from delicious showing significant benefits for personalized search compared to state of the art methods.

Due to richness of information on web, there is an increasing interest to search for missing attribute values in relational data on web. Web-based relational data imputation has to first extract multiple candidate values from web and then rank them by their matching probabilities. However, effective candidate ranking remains challenging because web documents are unstructured and popular search engines can only provide with relevant but not necessarily semantically matching information.In this paper, we propose a novel probabilistic approach for ranking the web-retrieved candidate values. It can integrate various influence factors, e.g. snippet rank order, occurrence frequency, occurrence pattern, and keyword proximity, in a single framework by semantic reasoning. The proposed framework consists of snippet influence model and semantic matching model. The snippet influence model measures the influence of a snippet, and the semantic matching model measures the semantic similarity between a candidate value in a snippet and a missing relational value in a tuple. We also present effective probabilistic estimation solutions for both models. Finally, we empirically evaluate the performance of the proposed framework on real datasets. Our extensive experiments demonstrate that it outperforms the state-of-the-art techniques by considerable margins on imputation accuracy.

The problem of identifying semantically aligned entities in different ontologies known as ontology mapping is an outstanding research area and lies at the heart of many semantic applications. The overarching goal of ontology mapping is to discover a valid and comprehensive mapping with the aim of maximizing the number of reasonable alignments of ontological entities. Recently many efforts to automate the ontology mapping have been carried out, with some problems such as scalability and efficiency still evident. In this paper, ontology mapping in heterogeneous knowledge bases is formalized as an optimization problem, and an efficient method called Harmony Search based Ontology Mapping (HSOMap) is proposed, that effectively finds a near-optimal mapping for two input ontologies. In this approach, we make use of many kinds of rating functions, which are also called base matchers to evaluate the similarity of entities. Each base matcher captures the similarity between entities from a different perspective and is able to exploit the available side information about the entities effectively. Also, a novel weighted harmonic-mean method is proposed to aggregate different metrics into a single similarity metric among all pairs of entities from two ontologies. After obtaining the combined similarity metric between ontological entities, a discrete harmony search algorithm is proposed to extract the best alignment. To demonstrate the merits and advantages of the HSOMap algorithm, we conduct a set of experiments on benchmark data sets and compare its performance to other state-of-the-other methods. Our experimental results demonstrate that applying harmony search in the context of ontology mapping is a feasible approach and improves the mapping effectiveness significantly.

In this paper we present a new personalized approach, which integrates a social profile into a distributed search system. Most previous studies on distributed information retrieval are based on textual information, and rarely consider any social information. Based on this observation, we propose an approach which exploits the social profile and the different relations between social entities. We believe that this method can: (i) enhance a query expansion, (ii) personalize and improve both the source selection and the result merging process in distributed information retrieval systems.

Many information systems use XML documents to store data and to interact with other systems. Abnormal documents, which can be the result of either an on-going cyber attack or the actions of a benign user, can potentially harm the interacting systems and are therefore regarded as a threat. In this paper we address the problem of anomaly detection and localization in XML documents using machine learning techniques. We present XML-AD - a new XML anomaly detection framework. Within this framework, an automatic method for extraction of feature from XML documents as well as a practical method for transforming XML features into vectors of fixed dimensionality was developed. With these two methods in place, the XML-AD framework makes it possible to utilize general learning algorithms for anomaly detection. The core of the framework consists of a novel multi-univariate anomaly detection algorithm, ADIFA. The framework was evaluated using four XML documents datasets which were obtained from real information systems. It achieved over 89% true positive detection rate with less than 0.2% of false positives.

Scientific workflows have been applied in many scientific areas with the large amount of complex data computation tasks such as life science, astronomy and earth science, etc. However, most existing approaches for scientific workflow retrieval neglect some constraints of quality of services (QoS) that users are really concerned about, and fail to allow users to express and retrieve scientific workflows with arbitrary constraints based on graph structures of workflows. In this paper, we propose a novel approach for scientific workflow retrieval with cost constraints. We present a graph representation model called Cost Constrained Graph (CCG) for representing scientific workflows with cost constraints. A distance measure is defined for accurate workflow retrieval. The CCGs representing candidate workflows can be ranked by comparing the similarity among them. We also theoretically prove that this measure satisfies all the four properties of distance. Furthermore, we develop a prototype system for editing, assignment of weights, and automatic similarity computation of workflows. At last, the related experiments are made to demonstrate the usefulness and efficiency of workflow retrieval based on our approach.

Recent years have witnessed unprecedented volumes of structured data published in RDF format. Taking full advantage of such data has attracted a growing amount of research interest from both academia and industry. However, efficient processing of top-k queries in RDF data is still a new topic. Most existing approaches ignore top-k queries, or only provide a limited number of ranking functions. In this paper, we provide an effective and efficient processing algorithm for top-k queries that consists of a novel tree-style index MS-tree and MS-tree-based filtering and pattern-matching functions. Firstly, candidate entities in RDF data were efficiently ranked and filtered through an MS-tree-based top-down method. Then, query structure patterns are matched in RDF data through graphic exploration. In order to handle more complex scoring functions, a dynamic variable selecting optimization is employed to accelerate the threshold decrease. We evaluate our solutions with both synthetic and real-world datasets. The experimental results show that our model significantly outperforms state-of-the-art approaches.

Preserving data quality is an important issue in data collection management. One of the crucial issues hereby is the detection of duplicate objects (called coreferent objects) which describe the same entity, but in different ways. In this paper we present a method for detecting coreferent objects in metadata, in particular in XML schemas. Our approach consists in comparing the paths from a root element to a given element in the schema. Each path precisely defines the context and location of a specific element in the schema. Path matching is based on the comparison of the different steps of which paths are composed. The uncertainty about the matching of steps is expressed with possibilistic truth values and aggregated using the Sugeno integral. The discovered coreference of paths can help for establishing a mapping between two different XML schemas. In other words, a novel approach for schema matching problem based on paths comparison only is proposed.

In real-world Web applications, information is often vague or ambiguous. Fuzzy XML databases have been proposed to manage data exchanges among different imprecise and uncertain XML documents. However, data collected from different data sources referring to the same real-world object may conflict. It is the task of the data management system to resolve the conflicts and integrate the data without any interference from a user. In order to integrate data from multiple autonomous fuzzy XML databases, an integration of fuzzy XML data has to be performed. In this paper, we take a first step in a fundamental consolidation of the fuzzy XML data model. In particular, we investigate the integration technique, which is designed to integrate fuzzy XML data collected from different data sources. We develop a basic framework for detecting conflicts and overlaps in fuzzy XML documents. In addition, we propose an approach for reconciling the fuzzy XML data collected from different data sources.

Content-based Image Retrieval (CBIR) systems consider only a pairwise analysis, i.e., they measure the similarity between pairs of images, ignoring the rich information encoded in the relations among several images. However, the user perception usually considers the query specification and responses in a given context. In this scenario, re-ranking methods have been proposed to exploit the contextual information and, hence, improve the effectiveness of CBIR systems. Besides the effectiveness, the usefulness of those systems in real-world applications also depends on the efficiency and scalability of the retrieval process, imposing a great challenge to the re-ranking approaches, once they usually require the computation of distances among all the images of a given collection. In this paper, we present a novel approach for the re-ranking problem. It relies on the similarity of top-k lists produced by efficient indexing structures, instead of using distance information from the entire collection. Extensive experiments were conducted on a large image collection, using several indexing structures. Results from a rigorous experimental protocol show that the proposed method can obtain significant effectiveness gains (up to 12.19% better) and, at the same time, improve considerably the efficiency (up to 73.11% faster). In addition, our technique scales up very well, which makes it suitable for large collections.

Extracting tuples from HTML pages has been an important issue in various web applications. Commercial tuple extraction systems have enjoyed some success to extract tuples by regarding HTML pages as tree structures and exploiting XPath queries to find attributes of tuples in the HTML pages. However, such systems would be vulnerable to small changes on the web pages. In this paper, we propose a robust tuple extraction system which utilizes spatial relationships among elements rather than the XPath queries. Spatial information (e.g., 2-D coordinates) of elements are maintained in the DOM tree when a web page is rendered in a browser. Our system regards elements in the rendered page as spatial objects in the 2-D space and executes spatial joins to extract target elements. Since humans also identify an element in a web page by its relative spatial location, our system extracting elements by their spatial relationships could possibly be as robust as manual extraction. To specify and execute spatial joins, we propose a new query language, RAQuery, based on topological relationships between any spatial objects in the 2-D space. We then propose spatial join algorithms that efficiently process the RAQuery using novel notions of group match and prunable relation group. We next propose a tuple construction algorithm to build tuples from the extracted elements obtained by the spatial joins, which can construct tuples even when there are no boundary HTML elements specified for the tuples in the web page. Extensive experimental results using real HTML pages confirm that our solutions are far more robust than existing tuple extraction systems without sacrificing performance.

With the explosive growth of heterogeneous XML sources, data inconsistency has become a serious problem that leads to ineffective business operations and poor decision-making. To address such inconsistency, XML functional dependencies (XFDs) have been proposed to constrain the data integrity of a source. Unfortunately, existing approaches to XFDs have insufficiently addressed data inconsistency arising from both semantic and structural inconsistencies inherent in heterogeneous XML data sources. This paper proposes a novel approach, called SCAD, to discover anomalies from a given source, which is essential to address prevalent inconsistencies in XML data. Our contribution is twofold. First, we introduce a new type of path and value-based data constraint, called XML Conditional Structural Dependency (XCSD), whereby ( i ) the paths in XCSD approximately represent groups of similar paths in sources to express constraints on objects with diverse structures; while (ii) the values bound to particular elements express constraints with conditional semantics. XCSD can capture data inconsistency disregarded by XFDs. Second, our proposed SCAD is used to discover XCSDs from a given source. Our approach exploits the semantics of data structures to detect similar paths from the sources, from which a data summary is constructed as an input for the discovery process. This aims to avoid returning redundant data rules due to structural inconsistencies. During the discovery process, SCAD employs semantics hidden in the data values to discover XCSDs. To evaluate our proposed approach, experiments and case studies were conducted on synthetic datasets which contain structural diversity causing XML data inconsistency. The experimental results show that SCAD can discover more dependencies and the dependencies found convey more meaningful semantics than those of the existing XFDs.

10.1016/j.ins.2013.04.021 Nominal attributes are very common in data sets about individuals, specifically medical data like patient healthcare records. Attributes of this type tend to be sensitive due to their personal nature. If public-use data sets need to be released, e.g. for clinical research purposes, data should be first anonymized. However, since most anonymization methods omit data semantics when dealing with nominal attributes (e.g. in a medical data set diagnosis is a nominal attribute), anonymization results in unnecessary information loss for such attributes, which is especially serious given their analytical importance. In this paper, we present a knowledge-based numerical mapping for nominal attributes that captures and quantifies their underlying semantics. Using this mapping, we show how to compute semantically and mathematically coherent mean, variance and covariance functions for nominal attributes; we also propose a distance measure between records containing numerical and nominal attributes. Thus, the proposed mapping allows adapting to nominal data some statistical disclosure control anonymization methods originally designed for numerical attributes. Evaluation results obtained for one of these methods applied to real patient discharge data shows that the use of our mapping retains better the semantics of original data and, hence, it yields anonymized data with better utility for clinical research.

Some indexing and query evaluation methods have been proposed for accelerating query processing in XML documents. The structural summary approach reduces the portion of the XML to be scanned during query processing. However, most of the methods based on this approach cannot support complex queries efficiently and/or encounter a long index construction time and a huge index size. Many query processing methods focus on processing twig pattern matching and these have developed various structures to store intermediate results. The problem with these query processing methods include generating huge intermediate results, an expensive merging phase, complicated data structures, and a requirement to scan all potential nodes. This paper proposes a compacted index scheme for XML documents called CIS-X, which combines the advantages of the structural summary and query processing methods. The experimental results show that the CIS-X can resolve most of the above problems, and usually outperforms existing techniques.

The World Wide Web is the most rapidly growing and accessible source of information. Its popularity has been largely influenced by the wide availability of the Internet in almost every modern house and even on the go after the wide-spread of the handheld devices. Yet, pages on the Web have an additional template (we call it noisy) information that does not add value to the actual content of the page. Even worse, it can harm the effectiveness of Web mining techniques; these templates could be eliminated by preprocessing. Templates form one popular type of noise on the Internet. In this paper, we introduce Noise Detector (ND) as an effective approach for detecting and removing templates from Web pages. ND segments Web pages into semantically coherent blocks. Then it computes content and structure similarities between these blocks; a presentational noise measure is used as well. ND dynamically calculates a threshold for differentiating noisy blocks. Provided that the investigated website has a single visible template, ND can detect the template with high accuracy using two pages only. However, ND can be expanded to detect multiple templates per website, and the challenge will be to minimize the number of pages to be checked. Further, ND leads to website summarization. The conducted experiments show that ND outperforms existing approaches in space complexity, time complexity (see Section 4.6 for more details on ND's processing time against other algorithms'), minimum requirements to produce acceptable results, and results accuracy.

Nowadays, personal information is being distributed into more and more heterogeneous sources, which presents a huge obstacle to management and retrieval of personal information. To address this problem, this paper presents the blueprint of a novel Personal Information Management (PIM) system named 3SEPIAS (short for Semi-Structured Search Engine for Personal Information in dAtaspace System). 3SEPIAS has three main features, data integration without upfront semantic reconciliation, flexible query model for data having sparse and evolving schema, and efficient best-effort proximity search approach on graphs. For that, we first propose a semi-structured graph data model called Interpreted Object Model (IOM) to uniformly represents a user's heterogeneous personal information and loosely integrates it into a dataspace in a schema-later way. Then, a Semi-Structured Search Engine (3SE) can be used to search over the personal dataspaces. We propose an intuitive 3SE Query Language (3SQL) that enables users to query in a varying degree of structural constraint according to their knowledge of underlying schemas. Moreover, a best-effort top-k proximity search optimization strategy and corresponding graph index structures are proposed to improve the efficiency of query processing. We perform comprehensive experiments to test both effectiveness and efficiency of our proximity search approach. The results reveal that 3SE can beat the previous proximity search systems by a large margin with only a little or even no loss of result quality, especially for large graphs.

In this paper, we propose a dynamical consensus scheme which makes use of an index of comparability and of an index of concordance to regulate the information flow in the discussion among the experts. In addition to the traditional use of a concordance index to identify the discordant expert, who can be invited to review his opinion or to explain his judgment to the other experts, here an index of comparability is utilized to identify the experts that have faced difficulties in expressing their preferences. With this information at hand, a moderator can invite the experts that are confident on their opinions to support the experts that are hesitating in the construction of the fuzzy preference relations.

The importance of incorporating time in databases has been well realized by the research community. Accordingly, temporal databases have been extensively studied by researchers. The main idea is to add a time or temporal dimension to the model and then tag data elements with time in order to keep all values instead of only the last one, and hence allow for time driven queries. This way it becomes possible to retrieve various values of the same element. This leads for better knowledge discovery and trend analysis by looking back into the history to predict for the future. Unfortunately, one disadvantage of the temporal database management system is that it has not been commercialized. The work described in this paper reflects our effort to demonstrate the power and effectiveness of the temporal dimension once it is well integrated into databases. We decided on XML (eXtensible Markup Language) as the underlying data model. The motivation is twofold. First XML is a defacto standard for data exchange; we have already demonstrated the power of XML in our other work described in the literature. Second, nested bitemporal databases form one interesting type of temporal databases. Thus, our purpose is to suggest an automated system that converts a nested bitemporal database to a corresponding fuzzy XML database. Fuzzy query model has been implemented as part of the proposed framework in order to provide flexibility to a wide rang of end users willing to access the database. The implemented temporal operators are database content independent. Fuzzy elements are capable of having different membership functions and varying number of linguistic variables. We have proposed a scheme for determining membership function parameters.

Metadata about information sources (e.g., databases and repositories) can be collected by Query Sampling (QS). Such metadata can include topics and statistics (e.g., term frequencies) about the information sources. This provides important evidence for determining which sources in the distributed information space should be selected for a given user query. The aim of this paper is to find out the semantic relationships between the information sources in order to distribute user queries to a large number of sources. Thereby, we propose an evolutionary approach for automatically conducting QS using multiple crawlers and obtaining the optimized semantic network from the sources. The aim of combining QS and evolutionary methods is to collaboratively extract metadata about target sources and optimally integrate the metadata, respectively. For evaluating the performance of contextualized QS on 122 information sources, we have compared the ranking lists recommended by the proposed method with user feedback (i.e., ideal ranks), and also computed the precision of the discovered subsumptions in terms of the semantic relationships between the target sources.

Concept hierarchies, such as the ACM Computing Classification Scheme and InterPro Protein Sequence Classification, are widely used in categorization and indexing applications. In the Internet and Web 2.0 era, new concepts and terms are emerging on an almost daily basis, so it is essential that such hierarchies maintain up-to-date records of concepts. This paper proposes a mechanism to identify the most suitable position to insert new terms into an existing concept hierarchy. The problem is challenging because there are hundreds or even thousands of candidate positions for insertion. Furthermore, usually there is no training instance available for an insertion; nor is it practical to assume the availability of a detailed description of the target concept, except in the hierarchy itself. To resolve the problem, we exploit the topology, content and social information, and apply a learning approach to identify the underlying construction criteria of the concept hierarchy. We utilize three metrics (namely, accuracy, taxonomic closeness, and ranking) to evaluate the proposed learning-based approach on the ACM CCS, the DOAJ and the InterPro datasets to evaluate the proposed learning-based approach. The results demonstrate that, in all three metrics, our approach outperforms similarity-based approaches, such as the Normalized Google Distance, by a significant margin. Finally, we propose a level-based recommendation scheme as a novel application of our system. The source code, dataset, and other related resources are available at http://www.csie.ntu.edu.tw/~d97944007/refinement/.

Collaborative filtering (CF)-based recommender systems represent a promising solution for the rapidly growing mobile music market. However, in the mobile Web environment, a traditional CF system that uses explicit ratings to collect user preferences has a limitation: mobile customers find it difficult to rate their tastes directly because of poor interfaces and high telecommunication costs. Implicit ratings are more desirable for the mobile Web, but commonly used cardinal (interval, ratio) scales for representing preferences are also unsatisfactory because they may increase estimation errors. In this paper, we propose a CF-based recommendation methodology based on both implicit ratings and less ambitious ordinal scales. A mobile Web usage mining (mWUM) technique is suggested as an implicit rating approach, and a specific consensus model typically used in multi-criteria decision-making (MCDM) is employed to generate an ordinal scale-based customer profile. An experiment with the participation of real mobile Web customers shows that the proposed methodology provides better performance than existing CF algorithms in the mobile Web environment.

Effective ranking algorithms for mobile Web searches are being actively pursued. Due to the peculiar and troublesome properties of mobile contents such as scant text, few outward links, and few input keywords, conventional Web search techniques using bag-of-words ranking functions or link-based algorithms are not good enough for mobile Web searches. Our solution is to use click logs to clarify access-concentrated search results for each query and to utilize the titles and snippets to expand the queries. Many previous works regard the absolute click numbers as the degree of access concentration, but they are strongly biased such that higher-ranked search results are more easily clicked than lower-ranked ones. Therefore, it is considered that only higher-ranked search results are access-concentrated ones and that only terms extracted from them can be used to expand a query. In this paper, we introduce a new measure that is capable of estimating the degree of access concentration. This measure is used to precisely extract access concentration sites from many search results and to expand queries with terms extracted from them. We conducted an experiment using the click logs and data from an actual mobile Web search site. Results obtained show that our proposed method is a more effective way to boost the search precision than using other query expansion methods such as the top K search results or the most-often-clicked search results.

Characterizing user's intent and behaviour while using a retrieval information tool (e.g. a search engine) is a key question on web research, as it hold the keys to know how the users interact, what they are expecting and how we can provide them information in the most beneficial way. Previous research has focused on identifying the average characteristics of user interactions. This paper proposes a stratified method for analyzing query logs that groups queries and sessions according to their hit frequency and analyzes the characteristics of each group in order to find how representative the average values are. Findings show that behaviours typically associated with the average user do not fit in most of the aforementioned groups.

Establishing interschema semantic knowledge between corresponding elements in a cooperating OWL-based multi-information server grid environment requires deep knowledge, not only about the structure of the data represented in each server, but also about the commonly occurring differences in the intended semantics of this data. The same information could be represented in various incompatible structures, and more importantly the same structure could be used to represent data with many diverse and incompatible semantics. In a grid environment interschema semantic knowledge can only be detected if both the structural and semantic properties of the schemas of the cooperating servers are made explicit and formally represented in a way that a computer system can process. Unfortunately, very often there is lack of such knowledge and the underlying grid information servers (ISs) schemas, being semantically weak as a consequence of the limited expressiveness of traditional data models, do not help the acquisition of this knowledge. The solution to overcome this limitation is primarily to upgrade the semantic level of the IS local schemas through a semantic enrichment process by augmenting the local schemas of grid ISs to semantically enriched schema models, then to use these models in detecting and representing correspondences between classes belonging to different schemas. In this paper, we investigate the possibility of using OWL-based domain ontologies both for building semantically rich schema models, and for expressing interschema knowledge and reasoning about it. We believe that the use of OWL/RDF in this setting has two important advantages. On the one hand, it enables a semantic approach for interschema knowledge specification, by concentrating on expressing conceptual and semantic correspondences between both the conceptual (intensional) definition and the set of instances (extension) of classes represented in different schemas. On the other hand, it is exactly this semantic nature of our approach that allows us to devise reasoning mechanisms for discovering and reusing interschema knowledge when the need arises to compare and combine it.