For a mass commercialization of Li-S chemistry the gravimetric energy density must be clearly above that of state-of-theart lithium-ion cells (with the Panasonic NCR18650B as current energy density champion) to compensate for the much lower cycle stability. The number 18650 describes the cell's shape with a diameter of ≈18 mm and a height of ≈65 mm. The NCR18650B provides a capacity of ≈3.3Ah with a nominal voltage of 3.6 V resulting in a gravimetric energy density of ≈240 Wh kg −1 and a volumetric energy density of ≈670 Wh L −1 . Additionally, the corresponding cell type can achieve several hundred cycles until 80% of the initial capacity is reached. By contrast, although high cycle numbers are reported for Li-S cells in the literature, the fl aw is that these high cycle numbers are only obtained because of an excess of lithium, an excess of electrolyte and low sulfur areal loads, [ 7 ] resulting in very poor potential gravimetric energy density. Figure 1 shows the gravimetric and volumetric energy density of various electrochemical energy storage systems. The Li-S cell manufacturers Sion Power and Oxis Energy expect that future Li-S cells will have a volumetric energy density comparable to that of state-of-the-art Li-ion cells (≈700 Wh L −1 ) but more than twice the gravimetric energy density with values of 400-600 Wh kg −1 .The scope of this article can be summarized as follows:• A Li-S review will be provided focusing on statistical information like sulfur load and sulfur electrode fraction which determine the energy density and discussing the state-of-theart of the worldwide Li-S research.• By opening an NCR18650B we obtained information about the passive weight distribution of state-of-the-art high energy 18650 cells. With this information we were able to calculate the possible energy densities and prices of future Li-S cells for various sulfur loads, sulfur utilizations, and electrolyte/ sulfur (E/S) ratios. Keeping in mind that a Li-S cell must have a superior gravimetric energy density to the NCR18650B these results provide insights into which electrode properties and electrochemical results must be obtained. Additionally, they allow an evaluation of the state of the art of international scientifi c Li-S research.• Finally, an electrode that meets important demands for high gravimetric energy densities is introduced. Li-S cells

As an increasing amount of RDF data is published as Linked Data, intuitive ways of accessing this data become more and more important. Question answering approaches have been proposed as a good compromise between intuitiveness and expressivity. Most question answering systems translate questions into triples which are matched against the RDF data to retrieve an answer, typically relying on some similarity metric. However, in many cases, triples do not represent a faithful representation of the semantic structure of the natural language question, with the result that more expressive queries can not be answered. To circumvent this problem, we present a novel approach that relies on a parse of the question to produce a SPARQL template that directly mirrors the internal structure of the question. This template is then instantiated using statistical entity identification and predicate detection. We show that this approach is competitive and discuss cases of questions that can be answered with our approach but not with competing approaches.

Abstract. Over the last decades, several billion Web pages have been made available on the Web. The ongoing transition from the current Web of unstructured data to the Web of Data yet requires scalable and accurate approaches for the extraction of structured data in RDF (Resource Description Framework) from these websites. One of the key steps towards extracting RDF from text is the disambiguation of named entities. While several approaches aim to tackle this problem, they still achieve poor accuracy. We address this drawback by presenting AGDIS-TIS, a novel knowledge-base-agnostic approach for named entity disambiguation. Our approach combines the Hypertext-Induced Topic Search (HITS) algorithm with label expansion strategies and string similarity measures. Based on this combination, AGDISTIS can efficiently detect the correct URIs for a given set of named entities within an input text. We evaluate our approach on eight different datasets against state-of-theart named entity disambiguation frameworks. Our results indicate that we outperform the state-of-the-art approach by up to 29% F-measure.

Direct current (DC) power distribution has recently gained traction in buildings research due to the proliferation of on-site electricity generation and battery storage, and an increasing prevalence of internal DC loads. The research discussed in this paper uses Modelica-based simulation to compare the efficiency of DC building power distribution with an equivalent alternating current (AC) distribution. The buildings are all modeled with solar generation, battery storage, and loads that are representative of the most efficient building technology. A variety of parametric simulations determine how and when DC distribution proves advantageous. These simulations also validate previous studies that use simpler approaches and arithmetic efficiency models.This work shows that using DC distribution can be considerably more efficient: a medium sized office building using DC distribution has an expected baseline of 12% savings, but may also save up to 18%. In these results, the baseline simulation parameters are for a zero net energy (ZNE) building that can island as a microgrid. DC is most advantageous in buildings with large solar capacity, large battery capacity, and high voltage distribution.

The vision behind the Web of Data is to extend the current document-oriented Web with machine-readable facts and structured data, thus creating a representation of general knowledge. However, most of the Web of Data is limited to being a large compendium of encyclopedic knowledge describing entities. A huge challenge, the timely and massive extraction of RDF facts from unstructured data, has remained open so far. The availability of such knowledge on the Web of Data would provide significant benefits to manifold applications including news retrieval, sentiment analysis and business intelligence. In this paper, we address the problem of the actuality of the Web of Data by presenting an approach that allows extracting RDF triples from unstructured data streams. We employ statistical methods in combination with deduplication, disambiguation and unsupervised as well as supervised machine learning techniques to create a knowledge base that reflects the content of the input streams. We evaluate a sample of the RDF we generate against a large corpus of news streams and show that we achieve a precision of more than 85%.

One of the main tasks when creating and maintaining knowledge bases is to validate facts and provide sources for them in order to ensure correctness and traceability of the provided knowledge. So far, this task is often addressed by human curators in a three-step process: issuing appropriate keyword queries for the statement to check using standard search engines, retrieving potentially relevant documents and screening those documents for relevant content. The drawbacks of this process are manifold. Most importantly, it is very time-consuming as the experts have to carry out several search processes and must often read several documents. In this article, we present DeFacto (Deep Fact Validation)an algorithm for validating facts by finding trustworthy sources for it on the Web. DeFacto aims to provide an effective way of validating facts by supplying the user with relevant excerpts of webpages as well as useful additional information including a score for the confidence DeFacto has in the correctness of the input fact.

Abstract-The search for information on the Web of Data is becoming increasingly difficult due to its dramatic growth. Especially novice users need to acquire both knowledge about the underlying ontology structure and proficiency in formulating formal queries (e. g. SPARQL queries) to retrieve information from Linked Data sources. So as to simplify and automate the querying and retrieval of information from such sources, we present in this paper a novel approach for constructing SPARQL queries based on user-supplied keywords. Our approach utilizes a set of predefined basic graph pattern templates for generating adequate interpretations of user queries. This is achieved by obtaining ranked lists of candidate resource identifiers for the supplied keywords and then injecting these identifiers into suitable positions in the graph pattern templates. The main advantages of our approach are that it is completely agnostic of the underlying knowledge base and ontology schema, that it scales to large knowledge bases and is simple to use. We evaluate 17 possible valid graph pattern templates by measuring their precision and recall on 53 queries against DBpedia. Our results show that 8 of these basic graph pattern templates return results with a precision above 70%. Our approach is implemented as a Web search interface and performs sufficiently fast to return instant answers to the user even with large knowledge bases.

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