dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements. E. Flaounas was supported by the  IMPACT2C program  (funded by the  European Union Seventh Framework Programme , FP7/2007-2013, under the grant agreement  282746 ), and this work was conducted in the  Institut Pierre Simon Laplace group  for regional climate and environment studies. The authors are grateful to  Philippe Drobinski  and  Heini Wernli  for fruitful discussions on the algorithm configuration, and to  Christoph Raible  for his help in plotting the cyclone center density field. Edited by:  K. Gierens.
As the amount of spatial data is growing, there is increased interest in developing tools to explore, visualize and interpret them, with the final aim of informing decision making efficiently. Within the European MIAVITA project, we examined this issue in the case of volcanic areas, where existing geospatial databases are particularly complex due to the number of threats to be considered, including volcanic (e. g. lava flows, ash fall) and non-volcanic hazards, such as landslides or tsunamis. We involved a group of hazard and risk analysts and managers, civil security officers, GIS analysts and system developers to design a Web-based geographical information system (WebGIS). We tested the system at the Mount Cameroon volcano, taking advantage of a complex hazard and risk geographical database.
We first organized workshops to envision idealized geospatial information dissemination and management practices. Then, following the approach of  Darke and Shanks (1997) , we formalized progressively the data and workflows to finally design an evolving prototype, which was improved, tested and validated by a broad range of potential users involved in the MIAVITA project. Through this method, the group identified key requirements for a risk and hazard GIS data repository. A territorial GIS risk repository should contain, at least, a multi-hazard map (e. g. Kappes et al. , 2012; Neri et al. , 2013) , an inventory of exposed elements together with attributes such as their physical vulnerability or their importance during disaster crisis or recovery, and the associated risk maps. From the risk management perspective, the completeness of data is even more important than the detailed accuracy of a particular data set.
Secondly, while a stand-alone GIS application may resolve the previous difficulty, existing GIS tools remain specialized tools and many potential users are unfamiliar with them. Finally, as a growing amount of geographical resources is being made available from multiple sources (see e. g. the Earth observation data disseminated through the Global Earth Observation System of Systems (GEOSS), which is under development,  Percivall et al. , 2013) , the group envisioned that incorporating interoperability features in the system should reinforce its ability to manage new data, thus increasing its sustainability. This supported the idea that, beside more specialized tools for managing geo-information databases or designing eruption scenarios (e. g. Gehl et al. , 2013) , an interoperable Web-based tool is required to facilitate the visualization, by non-GIS experts, of complex hazard and risk databases  (Salvi et al.
, 1999). Functionalities for updating the data layers through the WebGIS application were not consid-ered to be a priority here. There are however no technical barriers to developing such editing tools: for example,  Huang et al. (2013)  provide an example of a landslide database and WebGIS that incorporates the possibility of updating and editing the landslide inventory. As no solution incorporating all the features listed above was available at the time of the project, we developed a new tool using open source libraries such as OpenLayers, GeoExt and ExtJS. The final tool incorporates two user interfaces (Fig. 3):  (1) the browsing component, which is commonly referred to as a WebGIS; and (2) the database and user profile management interface. The WebGIS architecture itself includes four interdependent subsystems.
This subsystems are the following: (1) a Web server; (2) an authentication, authorization and audit server, which provides security services aimed at applying the access and control rules; (3) a GIS publisher enabling the final data provisioning and distribution; and (4) repositories that includes applications requested to configure the GIS repository and the authentication, authorization and audit server. For reinforcing reliability, each component can be hosted on an independent platform and repositories can be hosted on high-availability infrastructure that goes from a redundant array of independent disks (RAID) of type 1 (mirroring without parity or striping) up to a RAID of type 5 (block-level striping with distributed parity) architecture.
In particular, the area east of Limbe is under very high hazard. An important finding of this study was the significant exposure of the towns of Limbe and, to a lesser extent, Buea to geological hazards. In the context of our study, the main point is that all the findings of  Thierry et al. (2008)  have been incorporated into a GIS database, which we used to test and validate our tool. The GIS database contains geographical layers describing the exposure and elements at risks as well as their strategic importance, single-hazard and multihazard maps, "risk maps" (which actually consist of hazard maps superimposed with the assets vulnerable to these assets), topographic maps, other background information such 14 334 Figure  2. Use-case scheme of information flows between the database management system, 335 data providers and users.
This scheme shows the different possibilities for data insertion, 336 validation and access. All data providers are also considered as potential users of the system. 337 Here, a key role is given to the National Volcano Laboratory in managing the information 338 flow. 339 340 Figure 2. Use-case scheme of information flows between the database management system, data providers and users. This scheme shows the different possibilities for data insertion, validation and access. All data providers are also considered as potential users of the system. Here, a key role is given to the National Volcano Laboratory in managing the information flow. as optical satellite data and the geological map. Owing to the extensiveness of this database, we could use this case study to develop a controlled vocabulary describing the content of the hazard and risk geographical database and organized in a hierarchical form (i.
The WebGIS application was found rather easy to use by a board of users who tested the tool. This testing confirmed that the tool is able to manage any kind of hazard and risk information in an organized way, to display it simply and to manage priorities among the different users during the prevention, preparedness and crisis phases. The potential of using the WebGIS during the different phases of disasters was discussed with the initial group of users. It was found that the system is able to display the key anticipated scientific and regulatory information in support to prevention and recovery (maps of elements at risks and regulatory hazard and risk maps), preparedness (pre-determined hazards or risk scenarios aiming at testing the response capabilities), crisis management and response (maps of strategic infrastructures for the evacuation of people and for sustaining life in non-evacuated areas and possibly near-real-time The GIS database can be visualized through the WebGIS interface.
GIS data can be uploaded and managed through the data management interface, through which each user receives privileges that are a function of the status of the volcano (here: inactive, moderate activity and eruption). The WebGIS itself is divided in several components: one Web server manages the exchanges of information through the Web; one Authentication Authorization & Audit server provides security services (e. g. , ensuring that each user receives the relevant privileges according to the status of the volcano); the GIS publisher is the component that finally supplies the data; finally, the GIS database and the rules are stored in the database management system, which is the repository of authentication, authorization and audit rules defined through the data management interface. Figure  3.
Scheme of the WebGIS application and architecture developed within this study. The GIS database can be visualized through the WebGIS interface. GIS data can be uploaded and managed through the data management interface, through which each user receives privileges that are a function of the status of the volcano (here: inactive, moderate activity and eruption). The WebGIS itself is divided in several components: one Web server manages the exchanges of information through the Web; one authentication authorization & audit server provides security services (e. g. ensuring that each user receives the relevant privileges according to the status of the volcano); the GIS publisher is the component that finally supplies the data; finally, the GIS database and the rules are stored in the database management system, which is the repository of authentication, authorization and audit rules defined through the data management interface.
(Thierry et al. , 2008) , topographical isolines (bins of 250 m) and the location of main towns. This hazard maps combines threats due to lava flows, ground instability, ash fall, tsunamis and earthquakes. The map highlights that besides volcanic hazards, the hilly areas in the east of Limbe are subject to very high hazard of landslides. information provided by field civil security agents or scientists informing about the ongoing volcanic activity and security operations). We acknowledge that there are barriers to the operational use of such a tool. First, the database and user profile management interface still requires GIS and IT skills to be managed, in particular on open source development environment for building spatial internet applications (here: MapServer), on relational database management system (here: MySQL) and on cartographic projections libraries (here: ExtJS, Open-Layers and QuantumGIS).
For this use case, the application was judged beneficial during the validation phase, and other members of the stakeholder's group requested copies of the system for their own implementation plans at volcanoes in the Philippines and at Fogo (Cape Verde). Finally, this application at Mount Cameroon confirms that the approach focused on users' existing and potential workflows, although recognized a long process  (Steen et al. , 2007; Nies and Pelayo, 2010) , is necessary to improve existing hazard and risk geoinformation management practices. It demonstrates that this type of boundary object is potentially efficient in facilitating mutual exchange of knowledge between science and disaster risk reduction  (Cash et al. , 2003). The users can search the GIS database using the meta-information stored along with the geographical data -screen bottom three panels.
The selected GIS layers can be saved as independent scenarios and later retrieved from the server, as well as user's vector features (polygons, lines and points) can be added to the visualization area, saved on the server for further use and shared with the other WebGIS users. (B) shows the server GIS layer manager component. This module allows the management of all the GIS data contained in the database, including the promotion/demotion of the "validation level" associated with each GIS layer. On the left side the system shows all the management modules that allow the management and configuration of the whole system, including the switch from one alert level to another. Each alert level can be independently configured giving GIS data access priority to the more appropriate users in case of emergency.
Conclusions Many efforts are currently focusing on developing large geoinformation architectures and services based on modelling tools or Earth observation instruments, in particular through GEOSS (e. g. Hollingsworth et al. , 2005; Percivall et al. , 2013; Plag et al. , 2012). The development of these systems remains fundamentally driven by new scientific and technological developments. Here, we started from the procedures that use spatial information in risk management to design a Web-based application for managing and displaying a complex geographical database dedicated to hazard and risks in volcanic areas. We applied and tested the application for Mount Cameroon; other volcano observatories requested copies of the tool. Importantly, current interoperability features make it possible to visualize locally owned data layers together with the remote geoinformation provided through large geoinformation infrastructures.
Acknowledgements. This study was undertaken as part of the  MIAVITA  project (financed by the  European Commission  under the 7th  Framework Programme for Research and Technological Development, Area "Environment", Activity 6. 1  " Climate Change, Pollution and Risks ") and of the  BRGM 'Multirisk' programme (Prg 25 ). We thank a group of experts who participated to the design of the WebGIS. Particular thanks are owed to  A. Salvati ,  A. Canestro ,  F. Doumaz ,  C. Christiani ,  V. Bosi ,  L. Colini ,  J. Loiseau ,  B. Faria ,  J. Berger ,  A. Daag ,  R. Solidum  and  C. Bignami  for their most active participation. J. Douglas  proofread a draft. Edited by:  M. Neri Reviewed  by:  L. Becerril ,  V. Bosi , and three anonymous referees.
Since smooth parallel planes were used for constructing the SF in their experiment, the fracture roughness and the hold-up dispersion were negligible. Bodin et al. (2007)  developed the SOLFRAC program, which performs fast simulations of solute transport in complex 2-D fracture networks using the time domain random walk (TDRW) approach  (Delay and Bodin, 2001)  that makes use of a pipe network approximation. The code accounts for advection and hydrodynamic dispersion in channels, matrix diffusion, diffusion into stagnant zones within the fracture planes, mass sharing at fracture intersections, and other mechanisms such as sorption reactions and radioactive decay. Comparisons between numerical results and analytical BTCs for synthetic test problems have proven the accuracy of the model. Zafarani and Detwiler (2013)  presented an alternate approach for efficiently simulating transport through fracture intersections.
The interplay of data extractors and verification extractors ensures that all facts that make it into the final layer of the architecture have been checked for consistency and uniqueness. Together, the themes of the final layer constitute the YAGO KB. Applications. The new architecture allows us to add new extractors easily. To exemplify this, we have added a new module to YAGO, which extracts flight information from Wikipedia. Thanks to this module, YAGO now knows which airports are connected by direct flights to which other airports. Since YAGO also has vast data on geographic entities, users can now ask YAGO for flights between any two cities. Our interface will determine all airports in the vicinity of the departure city, all airports in the vicinity of the arrival city, and all direct flights between them  [20].
PARIS: Aligning Instances and Schemas KB Alignment. The Semantic Web is a large collection of publicly available knowledge bases (KBs). YAGO is only one of them: there are other KBs, such as DBpedia, Freebase, or domain-specific KBs, which cover music, movies, geographical data, scientific publications, and medical or government data. Many of these KBs are complementary. For instance, a general KB may know who discovered a certain enzyme, whereas a biological database may know its function and properties. However, since the KBs often use different identifiers for an entity, their information cannot be joined or queried across KBs. In the example, we cannot ask who discovered which enzyme with which properties. In addition, the KBs generally use different relations. For example, YAGO will say that Elvis Presley wasBornIn Tupelo, whereas another KB could say that Tupelo is the placeOfBirth of Elvis.
What is more, it is parameterfree: Unlike its competitors, the system has no thresholds to tune, no similarity functions to design, and no settings to be tried out. In our experiments, the system was run on all different datasets with the default settings. Results. To show the practical viability of our approach, we have run PARIS on the benchmark matching problems of the Ontology Alignment Evaluation Initiative (OAEI). PARIS outperformed the previously leading system on the instance alignment test data. In addition, it also computed the alignment between relations and classes, which was not even requested by the task. We also conducted large-scale experiments with real KBs on the Semantic Web. PARIS is able to align YAGO and DBpedia with a precision of 94% on the instances, 84 % on the classes, and 100% on the relations (weighted by their number of occurrences).
This alignment revealed interesting correspondences between the KBs, such as different naming policies, different design decisions, or redundancies within one KB. PARIS was also able to align YAGO with an ontology built from IMDb (the Internet Movie Database). All data, alignments, and results, as well as our implementation, are available on the Web site of the project 6. Our alignments have become part of the Semantic Web, and thus contribute to the vision of a large Web of linked data, where the KBs truly complement each other.
AMIE: Mining Logical Rules Rules. Knowledge bases can show us which facts are typically true about entities. For example, we could find: motherOf (m, c) ∧ marriedTo(m, f ) ⇒ fatherOf (f, c) This rule says that the husband of a mother is often the father of her children. Such a rule does not always hold. Still, such rules can be interesting for several reasons. First, by applying such rules on the data, new facts can be derived that make the KB more complete. For example, if we know the mother of a child and her husband, we can infer the father. Second, such rules can identify potential errors in the knowledge base. If, for instance, the KB claims that a totally unrelated person is the father of a child, then maybe this statement is wrong. Finally, rules about general tendencies can help us understand the data better.
We can, e. g. , find out that countries often trade with countries speaking the same language, that marriage is a symmetric relationship, that musicians who influence each other often play the same instrument, and so on. While mining logical rules has been well studied in the Inductive Logic Programming (ILP) community, mining logical rules in KBs is different in two aspects: First, current rule mining systems are easily overwhelmed by the amount of data (state-of-the art systems cannot even run on today's KBs). Second, ILP usually requires counterexamples. KBs, however, implement the open world assumption (OWA), meaning that absent data cannot be used as counterexamples. On that ground, we developed the AMIE approach  [9]. AMIE learns a set of meaningful logical rules from a KB. She uses a new mining model and a confidence metric, suitable for potentially incomplete KBs under the OWA.
Furthermore, such punishment is even counter-productive, because we want to use the rule to predict new facts. To address this problem, AMIE resorts to the Partial Completeness Assumption (PCA). The PCA is the assumption that if the database knows r(x, y) for some x and r, then it knows all facts r(x, y ). This assumption is sound for functional relations (such as wasBornOnDate, hasCapital ). If we reverse all inverse functional relations (such as creates and owns), the assumption holds also for them. Even for non-functional relations, the PCA is still reasonable for KBs that have been extracted from a single source (such as DBpedia and YAGO). These usually contain either all objects or none for a given entity and a given relation. The PCA can be used to infer negative information. For instance, if the database knows the citizenship of a person, then any other statement about her citizenship is assumed to be false.
The PCA confidence normalizes the support of the rule only against the facts that are known to be true or false according to the PCA. pcaconf (B ⇒ r(x, y)) := supp(B ⇒ r(x, y)) #(x, y) : ∃z 1 ,. , z m , y : B ∧ r(x, y ) The denominator of the PCA confidence expression includes all pairs x, y for which the r values of x are in the KB. If the relation is incomplete, those gaps are not used as counter-evidence. Results. Our experiments compare the usability and running times of AMIE against two state-of-the-art systems: WARMR and ALEPH  7. Since these approaches are designed to serve more general purposes, they require additional input such as a language bias and, in some cases, output post-processing. In contrast, AMIE runs out of the box with her default setting. Furthermore, AMIE runs several orders of magnitude faster.
SUSIE: Integrating Web Services Web Services. A growing number of data providers let us access their data through Web services. There are Web services about books (isbndb. org, librarything. com, Amazon, AbeBooks), about movies (api. internetvideoarchive. com), about music (musicbrainz. org, lastfm. com), and about a large variety of other topics. We have studied Web services under a variety of aspects  [14, 4, 16]. The API of a Web service restricts the types of queries that the service can answer. For example, a Web service might provide a method that returns the songs of a given singer, but it might not provide a method that returns the singers of a given song. If the user asks for the singer of some specific song, then the Web service cannot be called -even though the underlying database might have the desired piece of information.
Watermarking Licensing. Most KBs on the Internet are available for free. However, in most cases, their use is governed by a license: If a user re-publishes the data or part of the data, he has to give credit to the creators of the original KB. If he does not, then this constitutes plagiarism. In some cases, re-publication may be prohibited completely (e. g. , for commercially licensed KBs). This raises the question of how we can prove if someone re-published the data. Since ontological statements are usually world knowledge, there is no way we can show that someone took the data from us. The other person might as well have taken the data from a different source. He might even claim that we took the data from him. We propose to address this problem through watermarking. We developed two approaches: Additive Watermarking and Subtractive Watermarking.
Additive Watermarking. Additive Watermarking works by adding a small number of wrong statements to the KB ("fake facts"). If these fake facts appear in another KB, then the other KB most likely took the data from our KB. The fake facts have to be plausible enough in order not to be spotted by a machine or by a human. At the same time, they may not be so plausible that they are correct. We provide a theoretical analysis of how many facts we have to add in order to ensure plausibility and security at the same time. The main objection to this approach is that it compromises the data quality of the KB. It is true that watermarking is always a trade-off between data quality and the ability to prove provenance. However, our technique has to add only very few fake facts, usually a handful or a dozen.
Large, automatically constructed KBs contain anyway several thousands of wrong facts. YAGO, for example, one of the KBs with a particularly rigorous quality assessment, has a guaranteed correctness of 95%. Since YAGO contains millions of facts, thousands are wrong. Adding a few more might be a valuable trade-off. We show in a system demonstration of our approach how fake facts can be generated in such a way that most of them go undetected by a human  [18]. Subtractive Watermarking. Subtractive Watermarking works by removing a small number of statements from the KB. The KB is then published without these statements. This creates a pattern of "holes" in the KB, which we can imagine like holes in a cheese. If this pattern of holes appears in another KB, then the data has likely been taken from the source KB.