dataesr/scientific-paragraphs-categorization huggingface dataset:
Dataset Profiling Features and Taxonomy This section provides an inventory of dataset features of relevance to dataset profiling. Features are derived from the literature, in particular, from available dataset profiling methods and vocabularies presented in the following sections. Identified features are clustered and arranged in a feature taxonomy, which provides a categorisation system for the purpose of this survey. We would like to highlight that this taxonomy is extensible and provides one of several feasible ways to categorise profiling features. In particular, based on an extensive literature overview, we propose to organise features into seven categories: general, quality, provenance, licensing, links, statistical and dynamics. This categorisation mirrors the profiling vocabularies distribution, as described in Section 5.
General Features General features are dataset profile features carrying high-level semantic information that do not fit to any of the more specific categories defined in this survey. 1. Domain/Topic A domain refers to the field of life or knowledge that the dataset treats (e. g. , music, people). It describes and englobes the topics covered by a dataset (e. g. , life sciences or media), understood as more granular, structured metadata descriptions of a dataset, as the one found in  [29].
Contextual Connectivity We identify two members of this group: (a) connectivity properties, meaning the set of entities shared with other datasets, and (b) domain/topical overlap with other datasets. Important information, especially with regard to user queries, can be made available by the overlap of the domains or topics covered by a dataset and other datasets. This overlap can be expressed, for instance, by the presence of shared topics between two datasets  [79]. 3. Index Elements Index models have been introduced in order to retrieve information from the LOD graph. An index is defined as a set of key elements (e. g. , types), which are used to lookup and retrieve RDF data items. A dataset, therefore, can be inversely described by the set of index elements that are pointing to it in a given index or a set of indices.
In that sense, a set of index elements is viewed as a descriptive general dataset feature. These elements can be defined at the schema level (e. g. ,  [49] ) or at the instance level (e. g. ,  [39] ). 4. Representative Samples This group of features is found at the schema and at the instance level. On the one hand, representative schema elements can be understood as: (i) the most descriptive set of types (schema concepts)  [27] , or (ii) the set of schema properties that can be used as a keys (almost keys) in instance identification. On the other hand, representative instances are understood as a group of selected data that accurately portrays the whole dataset  [26].
Zaveri et al. [86]  identified further dimensions and reorganized the quality dimension into four groups: (i) Accessibility; (ii) Intrinsic; (iii) Contextual; and (iv) Representational. Yet another approach of metadata quality assessment can be found in  [77]  that monitors and assesses the quality of 82 active Open Data portals classified in six groups: retrievability, usage, completeness, accuracy, openness and contactability. In this work, we collected commonly used quality features and re-ordered them in a manner that matches the global dataset profile features taxonomy that we introduce giving rise to the following groups of quality features: (1) Trust; (2) Accessibility; (3) Context; (4) Degree of connectivity; and (5) Representation. 1. Trust Trust is a major concern when dealing with Linked Data.
Data trustworthiness can be expressed by the following features. (a) verifiability: the "degree and ease with which the information can be checked for correctness", according to  [8]. (b) believability: the "degree to which the information is accepted to be correct, true, real and credible"  [64]. This can be verified by the presence of the provider/contributor in a list of trusted providers. (c) reputation: a judgement made by a user to determine the integrity of a source  [86]. Two aspects are to take into consideration: i. reputation of the data publisher: a score coming from a survey in a community that determines the reputation of a source; and ii. reputation of the dataset: scoring the dataset on the basis of the references to it on the Web. 2. Accessibility This family of features regards various aspects of the process of accessing the data.
Representativity The features included in this group provide information in terms of noisiness, redundancy or missing information in a given dataset. (a) completeness: the degree to which all required information regarding schema, properties and interlinking is present in a given dataset  [86]. In the Linked Data context, the following sub-features are defined in  [8] : i. schema completeness (/ontology completeness) -the degree to which the classes and properties of a schema are represented in the dataset. ii. property completeness -measure of the missing values for a specific property. iii. population completeness -the percentage of all real-world objects of a particular type that are represented in the dataset. iv. interlinking completeness -refers to the degree to which links are missing in a dataset.
(b) understandability: refers to expression, or, as defined by  [64] , the extent to which data is easily comprehended. (c) accuracy / correctness: the equivalence between an instance value in a dataset and the actual real-world value corresponding to that instance. (d) conciseness: the degree of redundancy of the information contained in a dataset. (e) consistency: the presence of contradictory information. (f) versatility: whether data is available in different serialization formats, or in different formal and/or natural languages.
4. Context/task specificity This category comprises features that tell something about data quality with respect to a specific task. (a) relevance: the degree to which the data needed for a specific task is appropriate (applicable and helpful)  [64] , or the importance of data to the user query  [8]. (b) sufficiency: the availability of enough data for a particular task (  [8]  uses the term "amountof-data"). (c) timeliness: the availability of timely information in a dataset with regard to a given application.
Dynamics Features This class of features concerns the dynamicity of a dataset. In principle, every dataset feature can be dynamic, i. e. changing over time (take for example data quality). Inversely, the dynamics of a dataset can be seen as a feature of, for example, quality. For that reason, this family of features is seen as transversal (spanning over the three groups of features described above). (d) vocabulary changes: a measure of the impact of a change in a vocabulary to the dataset that uses it. (e) stability of index models: the level of change in the original data after the data has been indexed.
Orthogonal Features Here, we draw the reader's attention to the fact that some quality features may be orthogonal in the distribution of profiles features, notably to general categories. As orthogonal profile features we consider licensing, provenance and links, described as follows: 1. Licensing Here, we adopt the recommendation of Heath et al. [45] ; "in order to enable information consumers to use your data under clear legal terms, each RDF document should contain a license under which the content can be used". In other words, the type of license under which a dataset is published indicates whether reproduction, distribution, modification, redistribution are permitted. This can have a direct impact on data quality, both in terms of trust and accessibility. Hence, the importance of the existence of license in both human-readable and machinereadable profiles (i.
e, including the description in a license vocabulary cf. Section 5. 7). 2. Provenance the contextual metadata that provides indicators about timelines, currency and update cycles of datasets, which are necessary to know the origin of data, trace errors and notably establish trust. Hence, the provenance is a profile feature used to determine the believability of a dataset. An example use case scenario is to determine some trust score for SPARQL query results in a data sharing triple-store with different provenance. 3. Links Links here is understood as the number of datasets, with which a dataset is interlinked, or as the number of triples in which either the subject or the object come from another dataset. Two datasets can be linked through: (i) explicit links when they have linked instances, for example using owl:sameAs 10 when sharing identical instances, and (ii) implicit links when sharing topic profiles or context profiles, where explicit links like rdfs:seeAlso 11 can be also used.
The FluidOps Data Portal tool enables the retrieval of the "Context" features. Linked Data Observatory  [29]  provides an explorative way to browse and search through existing datasets in the LOD Cloud according to the topics they cover. By deploying entity recognition, sampling and ranking techniques, the Linked Data Observatory allows to find datasets providing data for a given set of topics or to discover datasets covering similar fields. This Structured Dataset Topic Profiles are represented in RDF using the VoID vocabulary in tandem with the Vocabulary of Links (VoL) (the vocabularies will 11 https://www. w3. org/TR/rdf-schema/#ch_ seealso be reviewed in Section 5 in more detail). The Linked Data Observatory allows the extraction of the "Domain/Topic" dataset profile features. voiDge is a tool that automatically generates VoID descriptions for large datasets.
This tool allows users to compute various VoID information and statistics on dumps of LOD as illustrated in  [14]. Additionally, the tool identifies (sub)datasets and annotates the derived subsets according to the VoID specification. The voiDge describes the "Schema/Instances" dataset profile features. The keys discovery approaches aim at selecting the smallest set of relevant predicates representing the RDF dataset within the context of link discovery. In other words, a key represents a set of schema properties that uniquely identifies every instance of a given schema concept. We cite two main keys discovery approaches: (i) SAKey  [73]  -an approach to discover almost keys in datasets where erroneous data or duplicates exist. SAKey is an extension of KD2R  [74] , which aims to derive exact composite keys from a set of non keys discovered on RDF data sources.
(ii) ROCKER -  [70]  key discovery approach that uses a refinement operator. This operator is able to detect sets of properties that describe any instance of a given class in a unique manner. Reportedly, ROCKER is more suited to large scale data than SAKey. Keys can be seen as a "Representative Schema/Instances" dataset profile feature. RDF QTree structure  [39]  is an approximate multidimensional indexing structure to store descriptions of the content of RDF data sources. A Qtree is a combination of histograms and an R-tree multidimensional structure. The method identifies relevant RDF data sources for a given query that incorporates instancelevel information by adding triples to the corresponding buckets in the QTree. The QTree structure allows the extraction of the "Index Elements" dataset profile feature.
Quality Features Extraction As discussed in Section 3, in this survey we focus on selected groups of quality features such as trust, accessibility, context and representation, most relevant in the context of dataset profiling. In the following we discuss a selection of relevant tools for these groups. Note that a broader overview of the quality assessement approaches in the context of Linked Data in general is provided by Zaveri et al. [86] , who conducted an extensive survey of 21 works. TRELLIS  [34]  is an interactive environment that examines the degree of trust of datasets based on user annotations. The user can provide Trellis with semantic markup of annotations through the interaction with the ACE tool 12   [12]. The tool allows several users to add and store their observations and viewpoints.
The annotations made by the users with ACE can be used in TRELLIS to detect conflicting information or handle incomplete information. Trellis provides description for the "Trust" feature in a dataset profile. tRDF  [40]  is a framework that provides tools to represent, determine, and manage trust values that represent the trustworthiness of RDF statements and RDF graphs. It contains a query engine for tSPARQL, a trust-aware query language. tSPARQL is an extension of the RDF query language SPARQL in two clauses: TRUST AS clause and the ENSURE TRUST clause. The trust values are based on subjective perceptions about the query object. While TRELLIS is based on users annotation, tRDF extracts the "Trust" feature by allowing users to query the dataset and access the trust values associated to the query solutions in a declarative manner.
WIQA  [9]  is a set of components to evaluate the trust of a dataset using a wide range of different filtering policies based on quality indicators like prove-nance information, ratings, and background information about information providers. This framework is composed of two components: a Named Graph Store for representing information together with quality related meta-information, and an engine, which enables applications to filter information and to retrieve explanations about filtering decisions. WIQA policies are expressed using the WIQA-PL syntax, which is based on the SPARQL query language. WIQA is a generic qualitative tool which can provide description about the "Trust", "Provenance" and the "Representations" dataset profile features. LiQuate  [68]  is a tool to assess the quality related to both incompleteness of links, and ambiguities among labels and links.
This quality evaluation is based on queries to a Bayesian Network that models RDF data and dependencies among properties. LiQuate enables the retrieval of the "Links" dataset profile features. RDFUnit  [50]  is a framework for the data quality that tests RDF knowledge based on Data Quality Test Pattern, DQTP. A pattern can be: (i) a resource of a specific type should have a certain property, (ii) a literal value should contain at most one literal for a certain language. The user can select and instantiate existing DQTPs. If the adequate test pattern for a given dataset is not available, the user has to write his own DQTPs, which can then become part of a central library to facilitate later re-use. RDFUnit provides "Representations" dataset profile features in form of DQTPs. Open Data Portal Watch (ODPW)  [77]  is a publicly available dashboard component that displays quality metrics for different data portals using various views and charts.
Statistical Features Extraction Statistical features discussed in Section 3. 3 comprise schema-level and instance-level statistics. LODStats  [5]  is a statement-stream-based tool and framework for gathering comprehensive statistics about datasets adhering RDF. The tool calculates 32 different statistical criteria on LOD such as those covered by the VoID Vocabulary. It computes descriptive statistics such as the frequencies of property usage and datatype usage, the average length of literals, or the number of namespaces appearing at the subject URI position. It is available for integration with CKAN 13  metadata repository, either as a patch or as an external web application using CKAN's API. LODStats provides descriptions for "schema-level" and "instancelevel" statistical dataset profile features.
ExpLOD  [48]  creates usage summaries from RDF graphs including metadata about the structure of an RDF graph, such as the sets of instantiated RDF classes of a resource or the sets of used properties. This structure information is aggregated with statistics like the number of instances per class or the number of property used. ExpLOD provides description about the "schema-level" statistical features for a given dataset. ProLOD++  [2]  is an interactive user interface, which is divided into a cluster tree view and a detailed view. The cluster view enables users to explore the cluster tree and to select a cluster for further investigation for statistics. ProLOD ++ is an extension of Pro-LOD  [15] , which generates basic statistics. In addition to the mining and the cleansing tasks, the tool generates dataset profiling features related to key analysis, predicate and value distribution, string pattern analysis, link analysis and data type analysis.
Temporal Features Extraction sparqlPuSH  [61]  is an interface that can be plugged in any SPARQL endpoint and that broadcasts notifications to clients interested in what is happening in the store using the PubSubHubbub 14  protocol  [30]  i. e. SP ARQL + pubsubhubbub = sparqlP uSH. Practically, this means that one can be notified in realtime of any change happening in a SPARQL endpoint. A resource can ping a PubSubHubbub hub when it changes, then, the notifications will be broadcasted to interested parties. sparqlPuSH consists in two steps: (i) register the SPARQL queries related to the updates that must be monitored in an RDF store, (ii) broadcast changes when data mapped to these queries are updated in the store. sparqlPuSH extracts "global" dataset profile features in the temporal dataset profile category.
The Semantic Pingback  [76]  is a mechanism that allows users and publishers of RDF content, of weblog entries or of a scientific article to obtain immediate feedback when other people establish a reference to them or their work, thus facilitating social interactions. It also allows to publish backlinks automatically from the original WebID profile (or other content, e. g. status messages) to comments or references of the WebID (or other content) elsewhere on the Web, thus facilitating timeliness and coherence of datasets. It is based on the advertisement of a lightweight RPC (Remote Procedure Call) service. This system is particularly useful for detecting the stability of links/backlinks. This mechanism provides feedback about "instancespecific" features of a dataset profile. Memento  [23]  is a protocol-based time travel that can be used to access archived representations of a resources identified by a given URI.
DSNotify relates to the "instance-specific" features in the dataset temporal profiling category. The Dynamic Linked Data Observatory (Dyldo)  [47] , is a framework to achieve a comprehensive overview of how LOD changes and evolves on the Web. It is an observatory of the dynamicity on the Web of Data over time. The observatory provides weekly crawls of LOD data sources starting from 02/11/2008 and contains 550K RDF/XML documents with a total of 3. 3M unique subjects with 2. 8M locally defined entities. The system examines, firstly, the usage of Etag and Last-Modified HTTP header fields, followed by an analysis of the various dynamic aspects of a dataset (change frequency, change volume, etc). Dyldo provides temporal dataset profile features in terms of both "global" and "semantics-specific" features.
A Note on Dataset Profiling Methods Here, we discuss several issues regarding dataset profile extraction methods that we observed in the survey process. We begin by the most sensitive profile representations, the semantic features, which typically require domain knowledge with respect to the content of the dataset. As best practice, we recommend that the semantic category should be provided by the data domain experts (e. g. data providers or maintainers) to ensure high quality of the semantic profile. On the other hand, we consider that qualitative, statistical and temporal profile features would in general require less domain expertise and can be extracted automatically by applications in many cases. Furthermore, we observe an obvious need for more semantic profile extraction tools, notably for the "domain/topic" and "context" features, where only few approaches allow automatic extraction of such profiles features.
Further on the dynamic aspect, in order to facilitate up-to-date dataset profiles, these profiles need to be regenerated periodically, based on the dataset dynamicity. The dataset versioning/archiving also requires versioning/archiving of the corresponding dataset profiles in order to ensure coherence between the dataset snapshots and their profile versions. Finally, we stress the fact that RDF dataset profiles need to provide representations for both human and machine reading. Hence, in Table  1 , we provide an overview of the dataset profiling methods including representation formats. In other words, we check for each method if the extracted profile features are designed for human reading or machine reading. In addition the table provides links to the homepages of each extraction method.
General Dataset Metadata Vocabularies A range of vocabularies exist which can be used to provide more general metadata of datasets or ontologies. While the Ontology Metadata Vocabulary (OMV)  [43]  is aimed at providing descriptive information about ontologies -specifically their creators, contributors, reviewers, and creation/modification dates - 15 http://dublincore. org/documents/dces/ here we focus specifically on dataset metadata vocabularies. The Vocabulary of Interlinked Datasets (VoID)  [3]  provides a core vocabulary for describing datasets and their links. The schema 16   18. The Data Catalog Vocabulary (DCAT) 19 follows a similar rationale and has been created based on a survey of government data catalogues  [53]. Key classes include Catalog, Dataset, CatalogRecord where the latter has a similar scope as the VoID DatasetDescription, i.
Dataset Links Links as important features of Linked Data datasets are represented through a variety of means, covering both schema-level and entity-level links. VoID, for instance, includes specific linksets which can be instantiated to define metadata about dataset's links. SKOS 20 , the Simple Knowledge Organization System, on the other hand provides a formal vocabulary for defining 17  A more specific approach is followed by the Vocabulary of Links (VoL) 21 , which provides a general vocabulary to describe metadata about links or linksets, within or accross specific datasets. VoL was designed specifically to represent additional metadata about computed links which cannot be expressed with default RDF(S) expressions and enable a qualification of a link or linkset. This includes, for instance, the description of linking scores or linking provenance, for instance, through a specific linking method.
Dataset Quality Early works by Supelar et al. in  [72]  define a set of knowledge quality features applicable for knowledge graphs, respectively ontologies, and a corresponding ontology. Their features are classified into quantifiable and non-quantifiable characteristics and include 21 http://data. linkededucation. org/vol/index. htm 22 http://alignapi. gforge. inria. fr/edoal. html characteristics such as usability, availability, accuracy, or complexity. The suggested ontology, however, only includes a higher level taxonomy, but neither a fully fledged vocabulary for annotation nor a specific set of metrics to quantify the quantifiable metrics. Fürber et al. [33]  describe the DQM Ontology 23 , a general vocabulary for representing data quality features, to some extent also covering statistical information, such as notions of property completeness or property uniqueness.