dataesr/scientific-paragraphs-categorization huggingface dataset:
The consequence is a high level of fragmentation of archaeological data and limited capability for collaborative research across institutional and national as well as disciplinary boundaries  (Aspöck and Geser 2014). This situation is being addressed by ARIADNE: the Advanced Research Infrastructure for Archaeological Dataset Networking in Europe. This einfrastructure initiative is being promoted by a consortium of archaeological institutes, data archives and technology developers, and funded under the European Commission's Seventh Framework Programme  (ARIADNE 2014a; Niccolucci and Richards 2013). ARIADNE enables archaeological data providers, large and small, to register and connect their resources  (datasets, collections)  to the e-infrastructure, and a data portal provides search, access and other services across the integrated resources.
The portal puts into operation a proof of concept exemplar first developed under the ARENA (Archaeological Records of 1. Past cultures and modern political borders rarely correspond, hence researchers carrying out investigations that span sites located in different countries face a number of problems when trying to place their discoveries in a broader context. They would like to easily compare features and items of their site with those of sites in other countries, yet these will usually be documented in a different language and in a different way. 2. Thematic datasets, on the contrary, may span different regions. Yet in many cases they are unrelated to the context (e. g. a pottery database does not enable users to access other data concerning the fieldwork context in which the pottery was found).
3. Harmonisation of vocabularies among different, but similar, datasets is usually modest. When it exists, it is more often the result of good archaeological practice than a design feature of the databases involved. This affects terms, names, geographic names, and time periods. 4. For excavation and artefact datasets, almost all archaeological data are stored using one language and refer mainly, if not exclusively, to one country or a part of it. 5. Metadata structures are usually different, even in datasets with similar content. Providing researchers with the ability to pose questions at a pan-European scale does not mean that there will always be single European answers. The importance of specific historical circumstances should not be underestimated: the limes of the Imperial Roman frontier system in Germania might be culturally and temporally equivalent to the Hadrian's Wall milecastles and the frontier fortlets of the Roman East, but the sheer scale of regional variation means that local factors will influence the particular form that these fortifications take.
However, in order to appreciate the role of local circumstances one also needs to compare datasets that cross modern boundaries. During the Neolithic, many European cultures developed megalithic tombs in order to commemorate their dead. Scholars who limit their research to the monuments of a single country -Britain or Ireland, say, or Denmark, France or Spain -will derive partial answers. Both archaeological and linguistic groups transcend political borders demarcated in the modern world. Nonetheless, the cultural context and different historical traditions within which archaeology has operated in the different European countries highlights the perils, as well as the benefits, of harmonisation. It will no doubt be easier to achieve interoperability in some areas than in others. Some national systems have benefited from decades of investment in thesauri development and controlled vocabularies; others have grown organically and suffer from a lack of standardisation.
Online questionnaire surveys Two international online surveys conducted in November/December 2013 collected needs and requirements of researchers and repository managers  (ARIADNE 2014b, 69-143). The results made it clear that archaeological researchers in most countries lack appropriate data repositories and services for finding and accessing relevant data. The selected results presented below are based on between 470 and 590 survey responses per result. The majority of researchers agreed that they often do not know what is available, because research data are scattered across many places and different databases. Consequently, 95% considered it to be very or rather important to have a good online overview of available datasets. About the same percentage required datasets to be available online and in an uncomplicated way, not 'limited to specific persons/communities' or 'kept in private collections of other researchers'.
Some 75% of respondents thought it important to have easy access to international datasets, suggesting a high interest in data that allows for comparative studies and integrative research. Furthermore, 60% of the researchers said that their organisation (university, research institute or other) does not have an institutional repository that is managed by dedicated staff, and 66% perceived a lack of international archives. Indeed, most institutional repositories manage only documents. Consequently the survey found that data were made available through an institutional repository in a few projects only or not at all by 67% of the researchers. The figures for national and international repositories were 76% and 83%, respectively. Most researchers wanted ARIADNE to create a data portal that allows an overview of existing archaeological data resources and to provide search facilities across the resources, using novel mechanisms for data discovery and access.
Asked which services they would benefit from most ('very helpful'), researchers responded: a portal that makes it more convenient to search for existing archaeological data that is stored in different archives/repositories (79%); innovative and more powerful mechanisms for data discovery and access (63%); a directory of European archaeological databases and repositories (52%); services for geo-integrated data (58%); and data recommendations based on collaborative filtering, rating and similar mechanisms (29%). Thus the capability to search and 'mine' distributed digital archives for relevant data was appreciated most. There was much less interest in typical features of Web 2. 0 platforms, such as content filtered based on tags or ratings provided by other users. Researchers appreciate effective mechanisms that save time in identifying relevant data (e.
ARIADNE has therefore devoted part of its activities to the identification of those key features and the proper encoding using existing and already well-accepted international standards and terminological tools. Definition and encoding of key elements and high-level entities has constituted the basis for the creation of the ARIADNE Catalogue, a core resource intended to store metadata and other valuable information concerning the archaeological archives and services connected to them. The catalogue, and the detailed descriptions it contains, constitutes the core of the whole integration process, since it provides all the support necessary for the retrieval and analysis of integrated archaeological information and the resource discovery facilities. The subjects to which the various datasets relate (e.
ARIADNE has recommended the use or the conversion of the spatial coordinates in WGS84 format to enable the browsing of archives through geographical tools. Specific resources, like the GeoNames gazetteer, were used to obtain spatial coordinates, starting from simple names of places in the case where these were the only geographic information present. As for the use of the historical names that a location may once have had, an invaluable collaboration with the Pelagios project was established in order to get geographic information from Pleiades (a thesaurus of past places built on a bibliographic database) and deploy it in Linked Open Data format to unambiguously identify such places. Of particular interest was the time-based integration (the 'when'), including information concerning dates, times, time intervals and periods abundantly present in archaeological archives.
The sharing of dates expressed in numeric format poses no problem, these being unambiguous. It should, however, be noted that very often time indications in databases only appear as simple names, without any reference to absolute dates; this may give rise to ambiguities in an integrated perspective, e. g. the Iron Age in Anatolia has a very different time span from the Iron Age in the British Isles. It is evident, therefore, that the temporal definition of an 'age' in the absolute sense is impossible without a precise spatial reference. An obvious and immediate solution to the problem of periodisation was to convert each period to absolute time spans by specifying start and end dates. However, this would not solve the semantic overlaps resulting from the need to keep the original time stamps as part of the documentation.
Collaboration with the PeriodO project, whose aim is to manage collections of periods built as intersections of documented events on specific geographical areas, helped to solve this issue. A deeper stage of interoperability has been reached with the integration of individual records coming from the legacy archaeological archives; this is what ARIADNE has defined as 'item-level integration'. Preparatory activities towards this goal include a broad conceptualisation, mappings and conversions of archaeological information and the construction of a repository with semantic capabilities to perform complex queries on aggregated data. The implementation of these features is based on the definition of mappings able to capture and express the semantic richness of archaeological data. Mappings are performed within the project through specific tools that allow individual partners to track complex correspondences between the entities contained in their databases and conceptual classes provided by the CIDOC-CRM and its extensions (CRMarchaeo in primis, see section 3.
The ARIADNE Catalogue data model The main goal of the ARIADNE project is 'to bring together and integrate the existing archaeological research data infrastructures so that researchers can use the various distributed datasets and new and powerful technologies as an integral component of the archaeological research methodology'. In order to achieve this goal, it is necessary (i) to gather information about the existing data resources and services in the archaeological domain, and (ii) to implement advanced search functionalities across this information in order to support the discovery of resources that make good candidates for integration. As a necessary step towards the realisation of the first objective, a data model is needed for representing archaeological resources that come in three different types: Data Resources, including the resources that are containers of data such as databases and collections; Language Resources, including the resources related to the formal languages used in Data Resources, such as vocabularies and metadata schemas; and Services, including the resources offering some kind of functionality in the archaeological domain.
The ACDM was built around the DCAT vocabulary, which was expanded by adding classes and properties that were needed for best describing the ARIADNE assets. Its adoption therefore places ARIADNE in an ideal position to publish archaeological data resources as Open Data, and demonstrates the application of DCAT to research datasets. As illustrated in Figure  2 , the central notion of the model is the class ArchaeologicalResource, which uses terms of the DCAT vocabulary, to which it adds properties for specifying the access policy and the original identifier of the resource. The class, as noted above, is specialised in: 1. DataResource, whose instances represent the various types of data containers owned by the ARIADNE partners and lent to the project for integration. This class is created for the sole purpose of defining the domain and the range of a number of associations.
It is therefore an abstract class, whose instances are inherited from subclasses. 2. LanguageResource, having as instances vocabularies, metadata schemas, gazetteers and mappings (between language resources). As new resources of a linguistic nature are added to the catalogue (such as subject heading systems and thesauri), the corresponding classes will be added to the model as a sub-class of this class. To describe language resources we have again used ISO/IEC 11179 (ISO 2004). 3. Services, whose instances represent the services owned by the ARIADNE partners and lent to the project for integration. (Each of these classes is described in some detail in this Box).
The aggregation infrastructure The ARIADNE Catalogue aggregates metadata, such as descriptions for datasets, metadata schemas, vocabularies, etc. provided by the project partners utilising the Open Archives Initiative Protocol for Metadata Harvesting (OAI-PMH) Content aggregation is inherently a content-driven task. This raises the importance of the data model, which needs to be robust and flexible in order to aggregate information for different domains and schemas. Therefore the metadata and object repository aggregator (MORe) has been utilised and customised  (Isaac et al. 2013)  in ARIADNE. The MORe aggregator has been used effectively in numerous projects and provides an easy and flexible way of aggregating metadata from multiple sources and in multiple formats. MORe aggregates dataset items that consist of seven data streams: 1.
Querying the ARIADNE catalogue Users can discover ARIADNE resources via the ARIADNE portal (see   Elasticsearch also provides a JSONstyle query language used to execute queries on the stored documents. This query language provides facilities such as, among others, full text queries, term-level queries as ranges, exist, wildcards, fuzzy search etc. , and geo-queries. The searchable content is stored as de-normalised documents in a Javascript Object Notation (JSON), ingested into the data store via the aggregation infrastructure described above. The JSON structure has been derived from the ACDM model and structured as to serve the search and discovery interface optimally.
Where -map-based browsing The 'where' section of the browsing interface is realised as a full-screen map layout based upon OpenStreetMap and implemented the help of Leaflet. The main challenge that had to be resolved in the implementation process was to develop a view that would provide both a dynamic visualisation of vast amounts of geographical data and the ability to narrow down the visible resources in order to be able to pick out the specific datasets of interest to the user. Therefore the resources are first visualised as a heatmap that represents resource density. This view dynamically changes to markers representing single locations when the user has reduced the result set by filtering or zooming. The implementation of the dynamic heatmap is realised with the help of Elasticsearch's aggregation feature.
This allows the creation of 'buckets' that cluster similar resources based on indexed field values. The particular index used in this aggregation is based on the geohash representation of geographical coordinates and accelerates access to geographically similar objects. By being able to do this accumulation on the server, based on indexes already present for the search functions, we were able to greatly reduce the cost of data transmission and processing in the browser. This enabled us to visualise millions of datasets without major lag or performance issues for the user.
A scalable solution to this mapping problem is to employ the hub architecture, an intermediate structure where concepts from the ARIADNE data provider source vocabularies can be mapped (ISO 2013). In the portal, retrieval based on a concept from one vocabulary (in a search or browsing operation) can use the hub to connect to subject metadata from other vocabularies, possibly expressed in other languages. In the ACDM, ariadne-subject is used for shared concepts from the hub vocabulary (the AAT), which have been derived via the various mappings from source vocabularies. This underpins the MORe enrichment services augmenting the data imported to the registry with mapped hub concepts (see section 3. 2. 2). These derived subjects in turn make possible concept-based search and browsing in the ARIADNE Portal (see section  3.
2. 3). It is hoped that the mappings will also form one of the stepping stones towards a multilingual capability in the Portal. The AAT was chosen as an appropriate hub vocabulary, following a prototype mapping and retrieval exercise involving five ARIADNE vocabularies (in three different languages). The AAT had recently been made available as Linked Open Data by the Getty Institute, which fit well with ARIADNE's strategy for semantic interoperability. The AAT linked data is expressed in the standard SKOS RDF representation and the appropriate representation for the mappings is via SKOS mapping relationships (http://www. w3. org/TR/skos-reference/#L4138). The next step was to produce the mappings from the subject vocabularies employed to index the various datasets selected for the ARIADNE catalogue.
This is not a trivial exercise. It requires domain experts to make quality mappings, who may not have expertise in computing semantic technologies. The vocabularies themselves vary from a small number of keywords from a picklist for a particular dataset to standard national vocabularies with a large number of concepts. In future work, making the mappings (and mapping services) fully available as outcomes in their own right, with appropriate metadata for the mappings would be desirable, as more than one mapping may be produced for large vocabularies. The information from the mapping tool is passed to MORe, which associates it with the provider of the vocabulary. It updates the property derived-subject and enriches an ACDM record (see Figure  7 ), adding a broader term, or a skos:altLabel to correlate a term via the 'use for' relationship, or adds multilingual labels (skos:prefLabel and skos:altLabel) in order to facilitate multilingual search.
The ARIADNE Landscape Service The Landscape Service is a set of online services for the processing, management and publication of large, multi-resolution 3D interactive terrain datasets within a collaborative workflow (see Figure  8 ). The goals within this service are to: (1) aid and support 3D landscape reconstruction tasks and projects in Virtual Archaeology and (2) provide online services for dissemination of interactive landscapes, through several devices. The Landscape Services are designed for responsiveness, thus adapting to both desktop and mobile devices such as smartphones and tablets. Data management is performed through a cloud service, allowing fine-grained access control on input/output data and collaborative approaches among research institutions and professionals, with specific focus on input DTMs/DEMs, imagery and shape files.
The CIDOC-CRM  (Doerr 2003)   In order to demonstrate the item-level integration process of archaeological datasets, ARIADNE has chosen as a use case the numismatics field, a highly standardised field with widely available data. Five datasets were selected (See Box). Four of them have been mapped to the ARIADNE RM and transformed to RDF using the X3ML framework while the fifth is already in CIDOC-CRM RDF form, compatible with the ARIADNE RM, and was extracted via OAI-PMH. As a common thesaurus for the aggregated knowledge base, the AAT and nomisma. org were adopted as the most appropriate resource in numismatics. The mapping and transformation workflow is presented in Figure  10. The ultimate goal of the integration of the diverse coin datasets is to create an environment where users will be able to specify queries that will be evaluated on the common aggregated repository and will be able to combine results coming from the different datasets.
Natural Language Processing services The archaeological domain generates vast quantities of text, including journal articles and reports of fieldwork or specialist analysis not formally published (grey literature). This text information is frequently difficult to access and opaque to computer-based tools for cross searching or meta analysis. This has become recognised as a significant problem for archaeological research. ARIADNE is addressing this issue, particularly as regards grey literature, by experimenting with text analysis methods based on Natural Language Processing (NLP) techniques for information extraction. The ultimate aim within ARIADNE is to extract additional relevant subject metadata from these reports and express it using the same ontological (CIDOC-CRM) and vocabulary standards as those used to describe archaeological datasets within the catalogue.
This builds upon previous work with the grey literature digital library from the ADS, which proved capable of semantic enrichment of English language grey literature reports conforming both to archaeological thesauri and corresponding CIDOC-CRM ontology classes representing archaeological entities, such as Artefacts, Features, Monuments Types and Periods. The current pilot system has achieved some promising semantic enrichment of Dutch grey literature reports, for example artefacts such as 'pottery/aardewerk' (via the RCE Archeologische artefacttypen vocabulary) and other concepts including time periods. Work extending the techniques to develop a Swedish language pipeline is underway. The resulting NLP tools will be available via the ARIADNE portal. Machine-learning work has focused on an English language user interface that can be used by archaeological practitioners to automatically generate metadata related to uploaded, text-based content on a per file basis, or using batch creation of metadata for multiple files.
English language NLP research has investigated the issue of negation detection in archaeological grey literature reports, with a view to distinguishing a finding of evidence, for example, of Roman activity from statements reporting a lack of evidence, or no sign of Roman remains. A rule-based technique previously used in the biomedical domain was adapted to archaeological vocabulary and writing style. This technique was applied to detecting negated instances of the CIDOC CRM entities, Physical Object, Time Appellation, Place and Material. An evaluation exercise on ten grey literature documents from a range of UK archaeological units gave promising results, with overall Recall at 80% and Precision 89%  (Vlachidis and Tudhope 2015). Further research on the semantic integration of archaeological datasets with grey literature reports, would be valuable, including negation detection (e.
(version 6. 2 available from http://83. 212. 168. 219/CIDOC-CRM/Version/version-6. 2) was chosen as the backbone of the ARIADNE reference model and a suite of extensions was developed to address the complexity of archaeological data integration. The CIDOC-CRM (ISO21127) is a formal ontology intended to facilitate the integration, mediation and interchange of heterogeneous cultural heritage information. It was developed by interdisciplinary teams of experts, coming from fields such as computer science, archaeology, museum documentation, history of arts, natural history, library science, physics and philosophy, under the aegis of the International Committee for Documentation (CIDOC) of the International Council of Museums (ICOM). It started from the bottom up, by re-engineering and integrating the semantic contents of more and more database schemata and documentation structures from all kinds of museum disciplines, archives and, recently, libraries as an empirical base.