anchor.diff:  research_krotzsch_24,24,Markus Krötzsch and Veronika Thost,krotzsch,Ontologies for Knowledge Graphs: Breaking the Rules,research,Knowledge Representation,2,Markus Krötzsch ,research_krotzsch_24.pdf,,502,2016-10-19T11:00:00,2016-10-19T12:20:00,
   research_suchanek_10,10,"Fabian M. Suchanek, Colette Menard, Meghyn Bienvenu and Cyril Chapellier",suchanek,Can you imagine... a language for combinatorial creativity?,research,Knowledge Representation,3,Fabian M. Suchanek,research_suchanek_10.pdf,,502,2016-10-19T11:00:00,2016-10-19T12:20:00,
   research_brasileiro_11,11,"Freddy Brasileiro, Joao Paulo Almeida, Victorio Albani Carvalho and Giancarlo Guizzardi",brasileiro,Expressive Multi-Level Modeling for the Semantic Web,research,Knowledge Representation,4,Freddy Brasileiro,research_brasileiro_11.pdf,,502,2016-10-19T11:00:00,2016-10-19T12:20:00,
  -application_arcan_33,33,"Mihael Arcan, Mauro Dragoni and Paul Buitelaar",arcan,Translating Ontologies in a Real-World Setting with ESSOT,application,Multilinguality,2,Mihael Arcan,application_arcan_33.pdf,,501,2016-10-19T14:00:00,2016-10-19T15:20:00,
  +application_arcan_33,33,"Mihael Arcan, Mauro Dragoni and Paul Buitelaar",arcan,Translating Ontologies in Real-World Settings,application,Multilinguality,2,Mihael Arcan,application_arcan_33.pdf,,501,2016-10-19T14:00:00,2016-10-19T15:20:00,
   journal_mccrae_7,7,"John P. McCrae, Mihael Arcan, Kartik Asooja, Jorge Gracia, Paul Buitelaar and Philipp Cimiano",mccrae,Domain Adaptation for Ontology Localization,journal,Multilinguality,4,John P. McCrae,journal_mccrae_7.pdf,,501,2016-10-19T14:00:00,2016-10-19T15:20:00,"Ontology localization is the task of adapting an ontology to a different cultural context, and has been identified as an important task in the context of the Multilingual Semantic Web vision. The key task in ontology localization is translating the lexical layer of an ontology, i.e., its labels, into some foreign language. For this task, we hypothesize that the translation quality can be improved by adapting a machine translation system to the domain of the ontology. To this end, we build on the success of existing statistical machine translation (SMT) approaches, and investigate the impact of different domain adaptation techniques on the task. In particular, we investigate three techniques: (i) enriching a phrase table by domain-specific translation candidates acquired from existing Web resources, (ii) relying on Explicit Semantic Analysis as an additional technique for scoring a certain translation of a given source phrase, as well as (iii) adaptation of the language model by means of weighting n-grams with scores obtained from topic modelling. We present in detail the impact of each of these three techniques on the task of translating ontology labels. We show that these techniques have a generally positive effect on the quality of translation of the ontology and that, in combination, they provide a significant improvement in quality. "
   resource_rebele_2,2,"Thomas Rebele, Fabian M. Suchanek, Johannes Hoffart, Joanna Biega, Erdal Kuzey and Gerhard Weikum",rebele,"YAGO: a multilingual knowledge base from Wikipedia, Wordnet, and Geonames",resource,Multilinguality,1,Thomas Rebele,resource_rebele_2.pdf,,501,2016-10-19T14:00:00,2016-10-19T15:20:00,"YAGO is a large knowledge base that is built automatically from Wikipedia, WordNet and GeoNames. The project combines information from 10 Wikipedias of different languages, thus giving the knowledge a multilingual dimension. It also attaches spatial and temporal information to many facts, and thus allows the user to query the data over space and time. YAGO focuses on extraction quality and achieves a manually evaluated precision of 95%. In this paper, we explain from a general perspective how YAGO is built from its sources, how its quality is evaluated, how a user can access it, and how other projects utilize it."
   resource_wang_9,9,"Haofen Wang, Zhijia Fang, Jorge Gracia, Julia Bosque-Gil and Tong Ruan",wang,Zhishi.lemon：On Publishing Zhishi.me as Linguistic Linked Open Data,resource,Multilinguality,3,Haofen Wang,resource_wang_9.pdf,,501,2016-10-19T14:00:00,2016-10-19T15:20:00,"Recently, a growing number of linguistic resources in different languages have been published and interlinked as part of the Linguistic Linked Open Data (LLOD) cloud. However, in comparison to English and other prominent languages, the presence of Chinese in such a cloud is still limited, despite the fact that Chinese is the most spoken language worldwide. Publishing more Chinese language resources in the LLOD cloud can benefit both academia and industry to better understand the language itself and to further build multilingual applications that will improve the flow of data and services across countries. In this paper, we describe Zhishi.lemon, a newly developed dataset based on the lemon model that constitutes the lexical realization of Zhishi.me, one of the largest Chinese datasets in the Linked Open Data (LOD) cloud. Zhishi.lemon combines the lemon core with the lemon translation module in order to build a linked data lexicon in Chinese with translations into Spanish and English. Links to BabelNet (a vast multilingual encyclopedic resource) have been provided as well. We also present a showcase of this module along with the technical details of transforming Zhishi.me to Zhishi.lemon. We have made the dataset accessible on the Web for both humans (via a Web interface) and software agents (with a SPARQL endpoint)."
  
positive.code: research_carral_6,6,"David Carral, Cristina Feier and Pascal Hitzler",carral,Practical Acyclicity Notions for Query Answering Horn-SRIQ Ontologies,research,Reasoning,1,David Carral,research_carral_6.pdf,,503,2016-10-21T15:30:00,2016-10-21T16:50:00,
  research_degiacomo_12,12,"Giuseppe De Giacomo, Xavier Oriol, Riccardo Rosati and Domenico Fabio Savo",degiacomo,Updating DL-Lite Ontologies through First-Order Queries,research,Reasoning,2,Giuseppe De Giacomo,research_degiacomo_12.pdf,,503,2016-10-21T15:30:00,2016-10-21T16:50:00,
  resource_parsia_19,19,"Bijan Parsia, Nicolas Matentzoglu, Rafael S. Gonçalves, Birte Glimm and Andreas Steigmiller",parsia,The OWL Reasoner Evaluation (ORE) 2015 Resources,resource,Reasoning,3,Bijan Parsia,resource_parsia_19.pdf,,503,2016-10-21T15:30:00,2016-10-21T16:50:00,"The OWL Reasoner Evaluation (ORE) Competition is an annual competition (with an associated workshop) which pits OWL 2 compliant reasoners against each other on various standard reasoning tasks over naturally occurring problems. The 2015 competition was the third of its sort and had 14 reasoners competing in six tracks comprising three tasks (consistency, classification, and realisation) over two profiles (OWL 2 DL and EL). In this paper, we outline the design of the competition and present the infrastructure used for its execution: the corpora of ontologies, the competition framework, and the submitted systems. All resources are publicly available on the Web, allowing users to easily re-run the 2015 competition, or reuse any of the ORE infrastructure for reasoner experiments or ontology analysis."
  resource_link_24,24,"Vincent Link, Steffen Lohmann and Florian Haag",link,OntoBench: Generating Custom OWL 2 Benchmark Ontologies,resource,Reasoning,4,Vincent Link,resource_link_24.pdf,,503,2016-10-21T15:30:00,2016-10-21T16:50:00,"A variety of tools for visualizing, editing, and documenting OWL ontologies have been developed in the last couple of years. The OWL coverage and conformance of these tools usually needs to be tested during development or for evaluation and comparison purposes. However, in particular for the testing of special OWL concepts and concept combinations, it can be tedious to find suitable ontologies and test cases. We have developed OntoBench, a generator for OWL 2 benchmark ontologies that can be used to test and compare ontology visualizers and related tools. In contrast to existing OWL benchmarks, OntoBench does not focus on scalability and performance but OWL coverage and concept combinations. Consistent benchmark ontologies are dynamically generated based on OWL 2 language constructs selected in a graphical user interface. OntoBench is available on GitHub and as a public service, making it easy to use the tool and generate custom ontologies or ontology fragments."
negative.code: journal_lephuoc_9,9,Danh Le-Phuoc,lephuoc,Operator-aware approach for boosting performance in RDF stream processing,journal,Streams,3,Danh Le-Phuoc,journal_lephuoc_9.pdf,,501,2016-10-20T13:30:00,2016-10-20T14:50:00,"To enable efficiency in stream processing, the evaluation of a query is usually performed over bounded parts of (potentially) unbounded streams, i.e., processing windows “slide” over the streams. To avoid inefficient re-evaluations of already evaluated parts of a stream in respect to a query, incremental evaluation strategies are applied, i.e., the query results are obtained incrementally from the result set of the preceding processing state without having to re-evaluate all input buffers. This method is highly efficient but it comes at the cost of having to maintain processing state, which is not trivial, and may defeat performance advantages of the incremental evaluation strategy. In the context of RDF streams the problem is further aggravated by the hard-to-predict evolution of the structure of RDF graphs over time and the application of sub-optimal implementation approaches, e.g., using relational technologies for storing data and processing states which incur significant performance drawbacks for graph-based query patterns. To address these performance problems, this paper proposes a set of novel operator-aware data structures coupled with incremental evaluation algorithms which outperform the counterparts of relational stream processing systems. This claim is demonstrated through extensive experimental results on both simulated and real datasets."
  research_gao_35,35,"Shen Gao, Daniele Dell'Aglio, Soheila Dehghanzadeh, Abraham Bernstein, Emanuele Della Valle and Alessandra Mileo",gao,Planning Ahead: Stream-Driven Linked-Data Access under Update-Budget Constraints,research,Streams,1,Shen Gao,research_gao_35.pdf,,501,2016-10-20T13:30:00,2016-10-20T14:50:00,
  research_siow_9,9,"Eugene Siow, Thanassis Tiropanis and Wendy Hall",siow,SPARQL-to-SQL on Internet of Things Databases and Streams,research,Streams,2,Eugene Siow,research_siow_9.pdf,,501,2016-10-20T13:30:00,2016-10-20T14:50:00,
  resource_mauri_18,18,"Andrea Mauri, Jean-Paul Calbimonte, Daniele Dell'Aglio, Marco Balduini, Marco Brambilla, Emanuele Della Valle and Karl Aberer",mauri,TripleWave: Spreading RDF Streams on the Web,resource,Streams,4,Andrea Mauri,resource_mauri_18.pdf,,501,2016-10-20T13:30:00,2016-10-20T14:50:00,"Processing data streams is increasingly gaining a momentum, given the need to process these flows of information in real time and at Web scale.
  In this context, RDF Stream Processing (RSP) and Stream Reasoning (SR) have emerged as solutions to combine semantic technologies with stream and event processing techniques.
  Research in these areas has proposed an ecosystem of solutions to query, reason and perform real time processing over heterogeneous and distributed data streams on the Web.
  However, so far one basic building block has been missing: a mechanism to disseminate and exchange RDF streams on the Web.
  In this work we close this gap, proposing TripleWave, a reusable and generic tool that enables the publication of RDF streams on the Web.
  The features of TripleWave have been derived from requirements of real use-cases, and consider a diverse set of scenarios, independent of any specific RSP implementation.
  TripleWave can be fed with existing Web streams (e.g. Twitter and Wikipedia streams) or time-annotated RDF datasets (e.g. the LinkedSensorData set), and it can be invoked through both pull- and push-based mechanisms, thus also enabling RSP engines to automatically register and receive data from TripleWave."
  research_chen_17,17,"Jiaqiang Chen, Niket Tandon, Charles Darwis Hariman and Gerard de Melo",chen,WebBrain: Joint Neural Learning of Large-Scale Commonsense Knowledge,research,Embeddings & Neural Approaches,1,Jiaqiang Chen,research_chen_17.pdf,,502,2016-10-20T15:30:00,2016-10-20T16:50:00,
  