Semantic Web Mining: State of the art and future directions

Semantic Web Mining aims at combining the two fast-developing research areas Semantic Web and Web Mining. This survey analyzes the convergence of trends from both areas: More and more researchers are working on improving the results of Web Mining by exploiting semantic structures in the Web, and they make use of Web Mining techniques for building the Semantic Web. Last but not least, these techniques can be used for mining the Semantic Web itself.The Semantic Web is the second-generation WWW, enriched by machine-processable information which supports the user in his tasks. Given the enormous size even of today’s Web, it is impossible to manually enrich all of these resources. Therefore, automated schemes for learning the relevant information are increasingly being used. Web Mining aims at discovering insights about the meaning of Web resources and their usage. Given the primarily syntactical nature of the data being mined, the discovery of meaning is impossible based on these data only. Therefore, formalizations of the semantics of Web sites and navigation behavior are becoming more and more common. Furthermore, mining the Semantic Web itself is another upcoming application. We argue that the two areas Web Mining and Semantic Web need each other to fulfill their goals, but that the full potential of this convergence is not yet realized. This paper gives an overview of where the two areas meet today, and sketches ways of how a closer integration could be profitable.     

Synthesis and crystal structure of the inter-alkali metal iodide Cs2Li3I5

Cs₂Li₃I₅ was obtained from the reaction of CsI, Li and I₂ in a sealed tantalum tube and colorless single crystals grown from the melt by slow cooling. This first inter-alkali metal iodide crystallizes in the monoclinic crystal system (C2/m, Z= 2) with a= 1666.8(6), b= 472.1(1), c= 1098.7(4) pm, β= 115.73(3)°. As the result of a x-ray crystallographic structure determination (R= 0.069, R_W= 0.066), Li⁺ is surrounded tetrahedrally (two thirds) and octahedrally (one third) by iodide. Cornersharing double chains of [LiI₄] tetrahedra are connected with edge-sharing chains of [LiI₆] octahedra to a layer of the composition [Li₃I₅]. Bicapped trigonal prisms [CsI₃] share faces and edges so that a network of the composition [Cs₂I₅] is formed which contains the necessary tetrahedral and octahedral holes for Li⁺.     

Synthesis and crystal structure of the inter-alkali-metal chloride RbLiCl2

The crystal structure of RbLiCl₂ [a= 1441.3(3), b= 412.57(8), c= 720.9(1) pm, Guinier-Simon data, orthorhombic, Ccmm, No. 63, Z= 4] was determined and refined from single crystal data, R= 0.074, R_W= 0.062. The coordination polyhedron of Rb⁺ is a bicapped trigonal prism, Li⁺ is tetrahedrally coordinated. [LiCl₄] tetrahedra are connected via common corners to undulated layers $\text{∞}\text{2}\text{[}\text{LiCl}{}^{\mathrm{c}}{}_{\mathrm{<mtext>4</mtext><mtext>2</mtext>}}\text{]}{}^{\mathrm{?}}$ parallel to (100). The structure is closely related to that of SrZnO₂.     

UNIQUAC法预报N-异丙基丙烯酰胺凝胶在乙醇水溶液中的溶胀行为

以N-异丙基丙烯酰胺(NIPAAm)为单体,合成了NIPAAm均聚凝胶。研究了25℃时它们在乙醇水溶液中的溶胀平衡。实验发现：当乙醇浓度约为12％时,少量乙醇的进一步加入将使溶涨的凝胶突然剧烈收缩,表明NIPAAm凝胶在乙醇水溶液中有不连续体积相变。随着乙醇不断加入,凝胶又重新溶涨。结合“自由体积”概念,利用UNIQUAC模型的关联和预报了NIPAAm凝胶在乙醇-水溶液中的这种溶涨行为。     

A Note on the Energy Norm for a Singularly Perturbed Model Problem

This paper considers a singularly perturbed reaction diffusion problem. It is investigated whether adaptive approaches are successful to design robust solution procedures. A key ingredient is the a posteriori error estimator. Since robust and mathematically analysed error estimation is possible in the energy norm, the focus is on this choice of norm and its implications. The numerical performance for several model problems confirms that the proposed adaptive algorithm (in conjunction with an energy norm error estimator) produces optimal results. Hence the energy norm is suitable for the purpose considered here. The investigations also provide valuable justification for forthcoming research. Received October 25, 2001; revised July 12, 2002 Published online: October 24, 2002     

Generating a Condensed Representation for Association Rules

Association rule extraction from operational datasets often produces several tens of thousands, and even millions, of association rules. Moreover, many of these rules are redundant and thus useless. Using a semantic based on the closure of the Galois connection, we define a condensed representation for association rules. This representation is characterized by frequent closed itemsets and their generators. It contains the non-redundant association rules having minimal antecedent and maximal consequent, called min-max association rules. We think that these rules are the most relevant since they are the most general non-redundant association rules. Furthermore, this representation is a basis, i.e., a generating set for all association rules, their supports and their confidences, and all of them can be retrieved needless accessing the data. We introduce algorithms for extracting this basis and for reconstructing all association rules. Results of experiments carried out on real datasets show the usefulness of this approach. In order to generate this basis when an algorithm for extracting frequent itemsets—such as Apriori for instance—is used, we also present an algorithm for deriving frequent closed itemsets and their generators from frequent itemsets without using the dataset.     

Single machine scheduling with small operator-non-availability periods

For an industrial application in the chemical industry, we were confronted with the planning of experiments, where human intervention of a chemist is required to handle the starting and termination of each of the experiments. This gives rise to a new type of scheduling problem, namely problems of finding schedules with time periods when the tasks can neither start nor finish. We consider in this paper the natural case of small periods where the duration of the periods is smaller than any processing time. This assumption corresponds to our chemical experiments lasting several days, whereas the operator unavailability periods are typically single days or week-ends. These problems are analyzed on a single machine with the makespan as criterion.     

Automated design of error-resilient and hardware-efficient deep neural networks

Applying deep neural networks (DNNs) in mobile and safety-critical systems, such as autonomous vehicles, demands a reliable and efficient execution on hardware. The design of the neural architecture has a large influence on the achievable efficiency and bit error resilience of the network on hardware. Since there are numerous design choices for the architecture of DNNs, with partially opposing effects on the preferred characteristics (such as small error rates at low latency), multi-objective optimization strategies are necessary. In this paper, we develop an evolutionary optimization technique for the automated design of hardware-optimized DNN architectures. For this purpose, we derive a set of inexpensively computable objective functions, which enable the fast evaluation of DNN architectures with respect to their hardware efficiency and error resilience. We observe a strong correlation between predicted error resilience and actual measurements obtained from fault injection simulations. Furthermore, we analyze two different quantization schemes for efficient DNN computation and find one providing a significantly higher error resilience compared to the other. Finally, a comparison of the architectures provided by our algorithm with the popular MobileNetV2 and NASNet-A models reveals an up to seven times improved bit error resilience of our models. We are the first to combine error resilience, efficiency, and performance optimization in a neural architecture search framework.

     

The social bookmark and publication management system bibsonomy

Social resource sharing systems are central elements of the Web 2.0 and use the same kind of lightweight knowledge representation, called folksonomy. Their large user communities and ever-growing networks of user-generated content have made them an attractive object of investigation for researchers from different disciplines like Social Network Analysis, Data Mining, Information Retrieval or Knowledge Discovery. In this paper, we summarize and extend our work on different aspects of this branch of Web 2.0 research, demonstrated and evaluated within our own social bookmark and publication sharing system BibSonomy, which is currently among the three most popular systems of its kind. We structure this presentation along the different interaction phases of a user with our system, coupling the relevant research questions of each phase with the corresponding implementation issues. This approach reveals in a systematic fashion important aspects and results of the broad bandwidth of folksonomy research like capturing of emergent semantics, spam detection, ranking algorithms, analogies to search engine log data, personalized tag recommendations and information extraction techniques. We conclude that when integrating a real-life application like BibSonomy into research, certain constraints have to be considered; but in general, the tight interplay between our scientific work and the running system has made BibSonomy a valuable platform for demonstrating and evaluating Web 2.0 research.