dataesr/scientific-paragraphs-categorization huggingface dataset:
In addition, the WIQA -Web Information Quality Assessment Framework 24 describe some early work to filter content according to quality features, also introduce WIQA-PL, a vocabulary for modeling content access policies. However, the work appears to be deprecated and not maintained. Also worth to mention is the work in  [32] , where authors use the SPARQL Inferencing Notation (SPIN) -a vocabulary that allows the representation of SPARQL queries -to represent data quality rules. In addition, the Dataset Quality Vocabulary (daQ) 25  and the Data Quality Vocabulary (DQV) 26 provide complementary terms for annotating DCAT dataset de- scriptions with quality aspects and metrics. While both vocabularies provide a general framework for annotating quality information and metadata about associated metrics, several concerns about practical issues are raised as part of the DQV working draft documentation.
Dataset Dynamics & Evolution While there does exist a wealth of methods for assessing characteristics related to dynamics and evolution of datasets, as illustrated in earlier sections of this survey, most vocabularies in the area are dedicated to representing the actual evolution of a dataset, rather than higher level observations about dynamics. The Dataset Dynamics group  27  for instance lists a number of vocabularies for representing dataset changeset and updates. The Talis Changeset vocabulary  28  provides some early, yet discontinued work on representing changeset and specific characteristics, and has a similar approach as the Delta vocabulary  29. The Triplify Update vocabulary  30  provides a very simple RDF schema for capturing dataset updates where each Update or UpdateSet is annotated with provenance information about the updater and the time stamp.
In a similar direction is the recent work of Graube et al. [38]  on R43ples, a revision management approach for RDF datasets using named graphs for capturing revisions and SPARQL for manipulation of the latter. Authors introduce the so-called Revision Management Ontology (RMO) based on PROV-O (cf. 5. 6). While RMO implements baseline revision management notions for data graphs, it is of lesser relevance for the purpose of this section. A more abstract approach is offered by the Dataset Dynamics (DaDy) Vocabulary 31 , which allows the representation of more abstract dynamics-related observations for a specific dataset. It is specifically foreseen to be used in conjunction with VoID, where a void:Dataset is annotated with instantiations of dady:UpdateDynamics. The latter captures information about the update regularity and frequency.
Statistical Dataset Metadata A range of vocabularies exist, which partially support the representation of dataset statistics and can be used in conjunction with general dataset metadata vocabularies such as VoID or DCAT. These include, for instance, the RDF Data Cube Vocabulary 32 , SDMX 33  or SCOVO 34. The VoID guidelines, for instance, recommend the use of SCOVO to share statistical dataset features  [3]. Authors foresee, on the one hand, statistics concerning the whole dataset or linkset, such as triple count, and attributing statistics to a source, to capture where a statistical datum stems from. Inline with some of the authors' concerns about the adequacy of SCOVO, it has been superseded by the Data Cube Vocabulary in the more recent past. The RDF Data Cube vocabulary 35 , currently a W3C Editors Draft developed by the Government Linked Data Working Group 36 is an RDF vocabulary for representing multi-dimensional so-called data cubes in RDF.
SCOVO 39 , also described by Hausenblas et al. [44] , is an earlier, native RDF vocabulary for statistical data, consisting of three main classes, Dataset, Dimension, and Item. While there exist efforts to merge SCOVO and SDMX-RDF  [21] , both approaches are superseded by the Data Cube vocabulary, which represents the state of the art in representing statistical data on the Web. Auer et al. present LODStats  [6] , a framework for dataset analytics, which introduces a set of 32 statistical features and uses the most recommended combination of VoID and the DataCube vocabulary. Links between the Data Cube class qb:Observation and the void:Dataset class are represented using a native property (void-ext:observation). While VoID already represents properties for several statistically described objects (triples, classes, distinctSubjects, etc.
Data and Dataset Provenance A variety of definitions have been given for provenance over the past number of years. One very pragmatic definition comes from the Provenance Working Group 40 of the W3C, especially when thought of in the context of the Web: "Provenance is defined as a record that describes the people, institutions, entities, and activities involved in producing, influencing, or delivering a piece of data or a thing. " On the Web, provenance can pertain to any resource found on the Webdocuments, data, or datasets -but it can also be found in a resource that is used to describe the provenance of an object in the real world. The main aim of the Provenance Working Group was to create standards that could be used to define and work with provenance data. A document from its previous incarnation as an Incubator Group states 39 http://vocab.
voidp builds on and extends the aforementioned VoID linked dataset ontology to describe the provenance relationships of data across linked datasets. Publishers can use a lightweight set of classes and properties to describe the provenance information of data within their linked datasets using voidp. This enables users to find the right data for their tasks based not only on the types of data being sought but also on the origins of that data, e. g. , "given a set of attributes and data authorship conditions, which available resources match a desired set of criteria and where can these resources be found?" 2. From the perspective of archiving and longterm preservation of data, the Data Dictionary for Preservation Metadata (PREMIS) 42 set of terms can be used to describe the provenance of archived, digital objects (e.
g. , files, bitstreams, aggregations and datasets), and therefore has applicability in our scenario. It does not provide provenance information for the descriptive metadata for those objects, and therefore one of the other vocabularies can be used for this. 41   [19]  makes it possible "to represent the attitude of a legal person to an RDF graph. SWP supports two attitudes: claiming the graph is true and quoting the graph without a comment on its truth. These commitments towards the truth can be used to derive a data publisher's or a data creating entity's relation to provided or created artifacts. Furthermore, the SWP allows to describe digests and digital signatures of RDF graphs and to represent public keys. " 6. The Provenance Vocabulary  44  was developed to describe provenance of Linked Data on the Web.
Some of these terms were highlighted by  [41] , and we outline these and others below. Since a dataset can be identified by a resource, we can use many of the properties described below with full datasets as well as individual resources or pieces of data in those datasets. -Dublin Core: dcterms:contributor and dcterms:creator can be used in analyses of the activity of a user in the data creation process, although the type of the user and their role may need to be further specified using other vocabularies. In our case, it could also be used to identify the creator of an entire dataset. dc:source describes the source from which a resource or dataset is derived, and therefore has usefulness as a provenance element. dcterms:created and dcterms:modified can be used to define both the creation of a resource or dataset and the modification of that resource or dataset respectively.
dcterms:publisher can be used to define the provider of a particular resource or dataset, although as  [41]  points out the type of As with Dublin Core, the properties sioc:has_creator, sioc:has_modifier (and their IFPs sioc:creator_of and sioc:modifier_of respectively) can be used to refer to a resource's creators and modifiers (identified by sioc:UserAccounts). sioc:has_owner and its IFP sioc:owner_of indicates who has control over a resource or dataset. sioc:ip_address can be used to link the created data and creator if specified to an Internet address. Also, sioc:last_activity_date can be used to reference the last activity associated with a resource, although this may still be interpreted in different ways (modified, read, etc. ). As with dc:source, a sioc:sibling can be used to define a new resource (or perhaps a dataset) that is very similar to but differs in some small manner from another one.
Finally, sioc:earlier_version, sioc:later_version, sioc:next_version and sioc:previous_version can be used to connect versioned artifacts together as one would find in a provenance graph. -In addition to the "SIOC Core" ontology terms, there are also some SIOC modules which can be used in provenance descriptions for datasets. The most relevant is probably the SIOC Actions  [20]  module, which was designed to represent how users in a community are manipulating the various digital artifacts that constitute the application supporting that community. The main terms in SIOC Actions are sioca:Action, sioca:DigitalArtifact, sioca:byproduct, sioca:creates, sioca:deletes, sioca:modifies, sioca:object, sioca:product, sioca:source and sioca:uses. These have been aligned to OPM and PROV-O in recent work by  [60].
Dataset Licensing We will now examine what vocabularies are available to assist with licensing of data and datasets. These include RDF versions of common licensing frameworks and alignments of multiple licensing frameworks into a combined vocabulary. -Creative Commons (CC) 49   56 is an RDF vocabulary that provides a common metadata modelling scheme for describing projects creating software applications, in order to provide a unified way to represent a software project no matter the source. The main class is Project which has properties such as its licence, the project's maintainers, the URL for subversion access, etc. Many of the concepts in DOAP could also be re-applied to datasets since they share many of the same properties. -Licenses for Linked Open Data (l4lod) 57 was introduced in  [37]  to provide an alignment with many of the licensing vocabularies we have just described.
Observations We use the LOD2 Stats service 58 to give us some context as to how often terms from these vocabularies are being used and within how many datasets. These statistics are shown in Table  3 , where the type refers to the vocabulary type as per the headings above. 59  While we were unable to filter the instances of dataset profiling-specific terms from our suggested vocabularies while examining their usage statistics in LOD2, we can gain some insight into which ones may be more widely adopted by looking at the existing overall statistics and dataset usages, especially over time (i. e. , from 2015 to 2016, we can see which vocabularies are consistently being used and are growing in usage). It is reasonable to assume that users will be more willing to adopt terms from widely-used vocabularies for representing dataset profiles, as long as they are fit for purpose.
251 datasets use RDF syntax, giving us an overall total. From the data in Table  3 , we observe that general metadata about the datasets is readily provided, but that more specific information on provenance and statistics using specialised vocabularies is only available in somewhere around 21%  (52)  and 10% (25) of datasets respectively. Another observation is that none of the quality or dynamics and evolution vocabularies appear in LOD2 Stats. That points to a significant underutilization 57 http://ns. inria. fr/l4lod/ 58 http://stats. lod2. eu/ as accessed on 2nd February 2015 and re-checked again on 19 January 2017 59 Where multiple entries exist for a vocabulary on LOD2 Stats, we use the numbers from the largest entry rather than adding usage figures together, as modules in a vocabulary may be used together in the same dataset (e.
g. , DC Terms and DC Elements, or SDMX Dimension and SDMX Measure). of terms relating to dataset quality, the evolution of a dataset, or the dynamics involved in a changing dataset. The assumption is that dataset creators are more interested in providing the datasets themselves without giving assurances to others who may want to use them about their quality or how they have changed over time. It does not seem from Table  3  that many datasets are explicitly licensed via some machine-readable form, with just 5%  (12)  containing Creative Commons metadata. However, according to work by  [37] , 95% of the datasets in the LOD cloud 60 did indeed express licensing information via the dcterms:license or the dcterms:rights properties of Dublin Core (albeit in human-readable format). Creative Commons represented 51% of all licenses in their analysis, followed by Open Data Commons at 18%.
Application-Driven Dataset Profiles Dataset profiles are highly important for a wide variety of applications in many domains, including, for example, data linking and curation, schema inference, federated query and search, as well as question answering. In this section, we highlight important applications from these domains that use dataset profiles along with their relevant profile features. Some of these applications can use, verify and update dataset profile features (e. g. , including statistical characteristics of datasets) and may in turn generate additional statistics that can become part of the dataset profile. The list of the applications and relevant features presented in this section aims to illustrate the use of dataset profiles by state-of-the-art tools and is not exhaustive.
Data Linking Applications Data linking applications aim to annotate, disambiguate and interlink entities and events in text using Natural Language Processing (NLP) techniques and external sources including Linked Data. In this context, popular services include DBpedia Spotlight  [22] , Illinois Wikifier  [67]  as well as Babelfy  [58]. Example features for data linking applications: Data linking applications typically use the semantic 60 http://lod-cloud. net/ features discussed in Section 3. 1 such as topics, domains, languages (versatility) and location coverage, as well as representative parts of schema/instances, and specifically the key candidates extracted with the key discovery approaches described in Section 4. 1.
Data Curation, Cleansing and Maintenance As linked datasets are often generated from semistructured or unstructured sources using automated extraction approaches, these datasets vary heavily with respect to quality, currentness and completeness of the contained information  [85]. A number of recent works focus on statistical methods for: (1) outlier detection to detect errors in numerical values  [31] ,  [63] ,  [82] ; (2) automatic prediction of missing types of instances  [63] ; and (3) the identification of incorrect links between datasets  [62]. A further line of research in Linked Data quality is related to the discovery of errors in the data based on existing interlinkings (e. g. ,  [16] ,  [84] ). Thereby some works go beyond error detection and attempt to automatically determine correct data values in case of inconsistencies  [16].
As mentioned above, additional statistics generated by these approaches that can become part of the dataset profile. Example features for error detection in numerical values: In  [31]  the authors detect errors in numerical values using outlier detection. To identify the properties to which numerical outlier detection can be applied, the following statistical characteristics (discussed in Section 3. 3) are used: (1) total number of instances, (2) names of the properties used in the dataset, (3) frequency of usage with numerical values in the object position for each property, and (4) total number of distinct numerical values for each property. Example features for conflict resolution in multilingual DBpedia: The features used in conflict resolution in  [16]  include provenance metadata at the statement, property and author levels.
Schema Inference Many existing Linked Data sources do not explicitly specify schemas, or only provide incomplete specifications. However, many real-world applications (e. g. , answering queries over distributed data  [11] ) rely on the schema information. Recently, approaches aimed at the automatic inference of missing schema information have been developed (e. g. ,  [63] ,  [49] ). Example features for type inference: Statistical characteristics of datasets (see Section 3. 3) play an important role in type inference applications. For example, in  [63]  statistics on the completeness of type statements as well as property-specific type distributions are required (i. e. , the types of resources appearing in subject and object positions of each property including their frequencies).
Distributed Query Applications The Linked Data Cloud can be queried either through direct HTTP URI lookups or using distributed SPARQL endpoints  [39]  that can include full-text search extensions (see e. g. , [1]). Also combinations of both query paradigms are possible  [42]. Typically, the first step of query answering over distributed data is the generation of ordered query plans against the mediated schema on a number of data sources  [83] ; In this step, dataset profiling plays an important role. In order to guide distributed query processing, existing applications rely on indexes of varying granularity including Schema-level Indexes and Data Summaries. Schema-level Indexes contain information about properties and classes occurring at certain sources. Data Summaries use a combined description of instanceand schema-level elements to summarise the content of data sources  [39].
Information Retrieval (IR) Applications In IR, Linked Data is mostly used in the context of semantic search, a typical demonstration of which can be found in  [28]. The majority of semantic search applications are domain-oriented; a large number of practical cases have been shown for repositories related to biomedical sciences. For example, the concept-based search mechanism  [51]  allows biologists to describe the topics of interest in a search more specifically and retrieve information with higher precision (in comparison to the usage of keywords only). It should be stressed here that concept-based search requires linking to high-quality external resources (such as, e. g. , UMLS  [13] ), which involves features related to trust, especially verifiability and believability. Datasets providing semantic features enable us to go beyond the standard bag of words representation  [75].
Discussion Overall, we observe that although existing applications make use of the whole spectrum of the dataset profile feature categories, including semantic, qualitative, statistical and temporal features discussed in this survey, the concrete set of features is applicationdependent and the whole set is rarely used within any single application. Whereas some applications rely on the existing metadata, many applications choose generating dataset profile features as a part of their own processing pipelines. This can be attributed to missing dataset profile features in many cases. On the one hand, these applications can thus directly contribute to the dataset profile generation. On the other hand, the burden to generate dataset profile features for each single application hinders usability of the datasets.
Conclusions RDF dataset profiling is perceived as a central challenge in enabling and facilitating dataset discovery in application scenarios such as data linking, data curation, distributed query and search, just to name a few. In this survey, we provide a comprehensive overview for dataset profiling features, methods, tools, vocabularies and applications. Given the complexity of the topic, we first focused on organizing the different dataset profile features in a taxonomy. We then provided a systematic overview of a large set of approaches and tools for assessing and extracting such features from RDF datasets. We reviewed the vocabularies for representing these features, preferably as Linked Data, and finally we discussed several prominent applications of dataset profiles. Wherever feasible, we also provided insights into the adoption and impact of the discussed works; for instance, based on the profile extraction tools distribution in the provided taxonomy, we propose that certain profiles features, notably in the semantic category, should be provided by the data domain experts to ensure high quality profiles.
Another observation concerns the vocabulary usage where some features, such as the quality or the dynamicity of vocabularies do not appear in the evaluated statistics. That leads us to recommend that dataset providers need to guarantee a high confidence with respect to these profile features in order to ensure better access to their quality or how they have changed over time. We observe that although existing applications make use of the whole spectrum of the discussed feature categories, including semantic, qualitative, statistical and temporal features, the concrete set of features is application-dependent and the whole set is rarely used within any single application. Furthemore, many applications generate dataset profile features as a part of their own processing pipelines, which can be attributed to missing dataset profiles or features in many cases.
This leads us to a conclusion that a-priori availability of dataset profiles could facilitate a broader use of profiles and datasets in a variety of application domains. Finally, we strongly recommend that dataset profiles provide representations readable for both humans and machines to open up the Web of Data to a wider variety of users and applications. Given the continuous evolution and expansion of the Web of Data, we assume that the problem of dataset profiling will become an even more prominent one, and corresponding methods will form a crucial building block for enabling reuse and take-up of datasets beyond established and well-understood knowledge bases and reference graphs.
Reviewer R1: R1: The paper gives a comprehensive overview on works for profiling linked data sets. While the sheer amount of works in this area collected and organized in the survey is impressive, the paper would benefit from showing the findings and conclusions in a more concise manner. Response: Thank you for the comment. In the revised version of the survey, we added more discussion and conclusions, in particular in Section 4. 5 that summarises our observations with respect to the dataset profiling methods, in Section 5. 8 that discusses our observations on vocabulary usage in RDF datasets and in Section 6. 6 that discusses dataset profiles in the context of applications. We have also expanded the overall conclusions in Section 7. R1: First, the title is misleading, as it talks about "Dataset Profiling" in general, but is limited to RDF/Linked Data.
g. , (natural text) question answering, information visualization, or semantic annotation, so it is not clear why the five examples shown in the paper have been chosen. Furthermore, it is not clear why the list of features presented for each application should be exhaustive. Response: Thank you for the comment. The application examples and the corresponding dataset profile features presented in the survey (Section 6 of the current survey version) are not exhaustive and serve as examples to illustrate how dataset profiles can support certain types of applications and which features do they typically use based on the current literature (whereas the methodology of the literature review is included in the new Section 2). To address the comment, we adjusted the description at the beginning of Section 6 to clarify the scope of the section.