641:). While some approaches try to extract the information from the structure inherent in the SQL schema (analysing e.g. foreign keys), others analyse the content and the values in the tables to create conceptual hierarchies (e.g. a columns with few values are candidates for becoming categories). The second direction tries to map the schema and its contents to a pre-existing domain ontology (see also:
1810:), which should make the semantics of contained terms machine-understandable. At this process, which is generally semi-automatic, knowledge is extracted in the sense, that a link between lexical terms and for example concepts from ontologies is established. Thus, knowledge is gained, which meaning of a term in the processed context was intended and therefore the meaning of the text is grounded in
637:. From a conceptual view, approaches for extraction can come from two directions. The first direction tries to extract or learn an OWL schema from the given database schema. Early approaches used a fixed amount of manually created mapping rules to refine the 1:1 mapping. More elaborate methods are employing heuristics or learning algorithms to induce schematic information (methods overlap with
250:
1610:
of extraction. In the following, natural language sources are understood as sources of information, where the data is given in an unstructured fashion as plain text. If the given text is additionally embedded in a markup document (e. g. HTML document), the mentioned systems normally remove the markup elements automatically.
572:
foreign keys. Each table typically defines a particular class of entity, each column one of its attributes. Each row in the table describes an entity instance, uniquely identified by a primary key. The table rows collectively describe an entity set. In an equivalent RDF representation of the same entity set:
1609:
is rather a challenge for knowledge extraction, more sophisticated methods are required, which generally tend to supply worse results compared to structured data. The potential for a massive acquisition of extracted knowledge, however, should compensate the increased complexity and decreased quality
628:
The 1:1 mapping mentioned above exposes the legacy data as RDF in a straightforward way, additional refinements can be employed to improve the usefulness of RDF output respective the given Use Cases. Normally, information is lost during the transformation of an entity-relationship diagram (ERD) to
571:
When building a RDB representation of a problem domain, the starting point is frequently an entity-relationship diagram (ERD). Typically, each entity is represented as a database table, each attribute of the entity becomes a column in that table, and relationships between entities are indicated by
1829:
At the terminology extraction level, lexical terms from the text are extracted. For this purpose a tokenizer determines at first the word boundaries and solves abbreviations. Afterwards terms from the text, which correspond to a concept, are extracted with the help of a domain-specific lexicon to
1752:
Coreference resolution identifies equivalent entities, which were recognized by NER, within a text. There are two relevant kinds of equivalence relationship. The first one relates to the relationship between two different represented entities (e.g. IBM Europe and IBM) and the second one to the
339:) has to provide the URI of the created entity. Normally the primary key is used. Every other column can be extracted as a relation with this entity. Then properties with formally defined semantics are used (and reused) to interpret the information. For example, a column in a user table called
657:
is one example of an approach that uses RDF blank nodes and transforms XML elements and attributes to RDF properties. The topic however is more complex as in the case of relational databases. In a relational table the primary key is an ideal candidate for becoming the subject of the extracted
1797:
Ontology learning is the automatic or semi-automatic creation of ontologies, including extracting the corresponding domain's terms from natural language text. As building ontologies manually is extremely labor-intensive and time consuming, there is great motivation to automate the process.
1717:
is a technology of natural language processing, which extracts information from typically natural language texts and structures these in a suitable manner. The kinds of information to be identified must be specified in a model before beginning the process, which is why the whole process of
1845:
as understood in natural language processing (also referred to as "semantic annotation"): Semantic parsing aims a complete, machine-readable representation of natural language, whereas semantic annotation in the sense of knowledge extraction tackles only a very elementary aspect of that.
3647:
Hu et al. (2007), "Discovering Simple
Mappings Between Relational Database Schemas and Ontologies", In Proc. of 6th International Semantic Web Conference (ISWC 2007), 2nd Asian Semantic Web Conference (ASWC 2007), LNCS 4825, pages 225â238, Busan, Korea, 11â15 November 2007.
1837:
is established. For this, candidate-concepts are detected appropriately to the several meanings of a term with the help of a lexicon. Finally, the context of the terms is analyzed to determine the most appropriate disambiguation and to assign the term to the correct concept.
4356:
Dill, Stephen; Eiron, Nadav; Gibson, David; Gruhl, Daniel; Guha, R.; Jhingran, Anant; Kanungo, Tapas; Rajagopalan, Sridhar; Tomkins, Andrew; Tomlin, John A.; Zien, Jason Y. (2003). "SemTag and Seeker: Bootstraping the
Semantic Web via Automated Semantic Annotation",
3765:
Hellmann, Sebastian; Lehmann, Jens; Auer, Sören; BrĂŒmmer, Martin (2013). "Integrating NLP Using Linked Data". In Alani, Harith; Kagal, Lalana; Fokoue, Achille; Groth, Paul; Biemann, Chris; Parreira, Josiane Xavier; Aroyo, Lora; Noy, Natasha; Welty, Chris (eds.).
1679:
In NLP, such data is typically represented in TSV formats (CSV formats with TAB as separators), often referred to as CoNLL formats. For knowledge extraction workflows, RDF views on such data have been created in accordance with the following community standards:
1763:
Template relation construction identifies relations, which exist between the template elements. These relations can be of several kinds, such as works-for or located-in, with the restriction, that both domain and range correspond to entities.
1632:
tools. Individual modules in an NLP workflow normally build on tool-specific formats for input and output, but in the context of knowledge extraction, structured formats for representing linguistic annotations have been applied.
4374:
Uren, Victoria; Cimiano, Philipp; Iria, José; Handschuh, Siegfried; Vargas-Vera, Maria; Motta, Enrico; Ciravegna, Fabio (2006). "Semantic annotation for knowledge management: Requirements and a survey of the state of the art",
3700:
Farid Cerbah (2008). "Learning Highly
Structured Semantic Repositories from Relational Databases", The Semantic Web: Research and Applications, volume 5021 of Lecture Notes in Computer Science, Springer, Berlin / Heidelberg
2389:(multi-)word NIF or EarMark annotation, predicates, instances, compositional semantics, concept taxonomies, frames, semantic roles, periphrastic relations, events, modality, tense, entity linking, event linking, sentiment
4191:
3288:(KDM) which defines an ontology for the software assets and their relationships for the purpose of performing knowledge discovery in existing code. Knowledge discovery from existing software systems, also known as
3705:
3958:
Vossen, Piek; Agerri, Rodrigo; Aldabe, Itziar; Cybulska, Agata; van Erp, Marieke; Fokkens, Antske; Laparra, Egoitz; Minard, Anne-Lyse; Palmero
Aprosio, Alessio; Rigau, German; Rospocher, Marco (2016-10-15).
1748:
is to recognize and to categorize all named entities contained in a text (assignment of a named entity to a predefined category). This works by application of grammar based methods or statistical models.
4989:
176:
The tool is able to reuse existing vocabularies in the extraction. For example, the table column 'firstName' can be mapped to foaf:firstName. Some automatic approaches are not capable of mapping vocab.
1783:, instances and relations of the used ontologies in the text, which will be structured to an ontology after the process. Thus, the input ontologies constitute the model of information to be extracted.
608:
for each column that is neither part of a primary or foreign key, construct a triple containing the primary key IRI as the subject, the column IRI as the predicate and the column's value as the object.
1767:
In the template scenario production events, which are described in the text, will be identified and structured with respect to the entities, recognized by NER and CO and relations, identified by TR.
1666:(see coreference resolution in IE below, but seen here as the task to create links between textual mentions rather than between the mention of an entity and an abstract representation of the entity)
1760:
During template element construction the IE system identifies descriptive properties of entities, recognized by NER and CO. These properties correspond to ordinary qualities like red or big.
168:
Is the knowledge extraction process executed once to produce a dump or is the result synchronized with the source? Static or dynamic. Are changes to the result written back (bi-directional)
298:). Counter examples: Methods that only recognize entities or link to Knowledge articles and other targets that do not provide further retrieval of structured data and formal knowledge.
4207:
Gangemi, Aldo; Presutti, Valentina; Reforgiato
Recupero, Diego; Nuzzolese, Andrea Giovanni; Draicchio, Francesco; MongiovĂŹ, Misael (2016). "Semantic Web Machine Reading with FRED",
3660:
R. Ghawi and N. Cullot (2007), "Database-to-Ontology
Mapping Generation for Semantic Interoperability". In Third International Workshop on Database Interoperability (InterDB 2007).
254:
3688:
Tirmizi et al. (2008), "Translating SQL Applications to the
Semantic Web", Lecture Notes in Computer Science, Volume 5181/2008 (Database and Expert Systems Applications).
1290:
4188:
4063:
Erdmann, M.; Maedche, Alexander; Schnurr, H.-P.; Staab, Steffen (2000). "From Manual to Semi-automatic
Semantic Annotation: About Ontology-based Text Annotation Tools",
3702:
3689:
1321:
3672:
Li et al. (2005) "A Semi-automatic
Ontology Acquisition Method for the Semantic Web", WAIM, volume 3739 of Lecture Notes in Computer Science, page 209-220. Springer.
1779:
is used to guide the process of information extraction from natural language text. The OBIE system uses methods of traditional information extraction to identify
4167:
2182:
1166:
3620:
Knowledge has a Linked Data twin called DBpedia. DBpedia has the same structured information as
Knowledge â but translated into a machine-readable format.
3276:. Usually the knowledge obtained from existing software is presented in the form of models to which specific queries can be made when necessary. An
4498:
4474:
69:(data warehouse), the main criterion is that the extraction result goes beyond the creation of structured information or the transformation into a
1605:
The largest portion of information contained in business documents (about 80%) is encoded in natural language and therefore unstructured. Because
269:
to extend a tax break for students included in last year's economic stimulus package, arguing that the policy provides more generous assistance.
238:
143:
The following criteria can be used to categorize approaches in this topic (some of them only account for extraction from relational databases):
1833:
In entity linking a link between the extracted lexical terms from the source text and the concepts from an ontology or knowledge base such as
3312:, such as process flows (e.g. data flows, control flows, & call maps), architecture, database schemas, and business rules/terms/process.
4183:
Mendes, Pablo N.; Jakob, Max; Garcia-SĂlva, AndrĂ©s; Bizer; Christian (2011). "DBpedia Spotlight: Shedding Light on the Web of Documents",
5076:
4189:
http://www.wiwiss.fu-berlin.de/en/institute/pwo/bizer/research/publications/Mendes-Jakob-GarciaSilva-Bizer-DBpediaSpotlight-ISEM2011.pdf
3723:
Wimalasuriya, Daya C.; Dou, Dejing (2010). "Ontology-based information extraction: An introduction and a survey of current approaches",
919:
308:
3703:
http://www.tao-project.eu/resources/publications/cerbah-learning-highly-structured-semantic-repositories-from-relational-databases.pdf
3690:
http://citeseer.ist.psu.edu/viewdoc/download;jsessionid=15E8AB2A37BD06DAE59255A1AC3095F0?doi=10.1.1.140.3169&rep=rep1&type=pdf
3588:
3272:, weakness discovery and compliance which involves understanding existing software artifacts. This process is related to a concept of
3070:
concepts, concept hierarchy, non-taxonomic relations, lexical entities referring to concepts, lexical entities referring to relations
1042:
3961:"NewsReader: Using knowledge resources in a cross-lingual reading machine to generate more knowledge from massive streams of news"
1349:
658:
triples. An XML element, however, can be transformed - depending on the context- as a subject, a predicate or object of a triple.
1425:
319:
RDF Views are tools that transform relational databases to RDF. During this process they allow reusing existing vocabularies and
4782:
4716:
4576:
97:
74:
54:
34:
4855:
4281:
Missikoff, Michele; Navigli, Roberto; Velardi, Paola (2002). "Integrated Approach to Web Ontology Learning and Engineering",
4101:
3900:
3863:
3785:
3649:
1660:
shallow syntactic parsing (CHUNK): if performance is an issue, chunking yields a fast extraction of nominal and other phrases
4392:
Cimiano, Philipp; Völker, Johanna (2005). "Text2Onto - A Framework for Ontology Learning and Data-Driven Change Discovery",
5081:
4726:
4231:
Adrian, Benjamin; Maus, Heiko; Dengel, Andreas (2009). "iDocument: Using Ontologies for Extracting Information from Text",
1506:
630:
4430:
4248:
3127:
1628:
As a preprocessing step to knowledge extraction, it can be necessary to perform linguistic annotation by one or multiple
17:
4450:
4340:
3173:
3464:
857:
193:
A pre-existing ontology is needed to map to it. So either a mapping is created or a schema is learned from the source (
4164:
3846:. In Gracia, Jorge; Bond, Francis; McCrae, John P.; Buitelaar, Paul; Chiarcos, Christian; Hellmann, Sebastian (eds.).
2577:
3242:
4080:
Rao, Delip; McNamee, Paul; Dredze, Mark (2011). "Entity Linking: Finding Extracted Entities in a Knowledge Base",
3257:
that can be used for further usage and discovery. Often the outcomes from knowledge discovery are not actionable,
949:
3917:"The Language Application Grid | A web service platform for natural language processing development and research"
3879:
Verhagen, Marc; Suderman, Keith; Wang, Di; Ide, Nancy; Shi, Chunqi; Wright, Jonathan; Pustejovsky, James (2016).
3604:
2488:
English, Arabic Chinese (Simplified and Traditional), French, Korean, Persian (Farsi and Dari), Russian, Spanish
124:, much research has been conducted in the area, especially regarding transforming relational databases into RDF,
1104:
1073:
5157:
4869:
3850:. Lecture Notes in Computer Science. Vol. 10318. Cham: Springer International Publishing. pp. 74â88.
3475:
3258:
1776:
320:
117:
85:
53:) sources. The resulting knowledge needs to be in a machine-readable and machine-interpretable format and must
4409:
Maedche, Alexander; Volz, Raphael (2001). "The Ontology Extraction & Maintenance Framework Text-To-Onto",
3887:. Lecture Notes in Computer Science. Vol. 9442. Cham: Springer International Publishing. pp. 33â47.
588:
Each row (entity instance) is represented in RDF by a collection of triples with a common subject (entity ID).
3458:
3285:
2384:
1814:
with the ability to draw inferences. Semantic annotation is typically split into the following two subtasks.
1854:
The following criteria can be used to categorize tools, which extract knowledge from natural language text.
4933:
4807:
4619:
4394:
Proceedings of the 10th International Conference of Applications of Natural Language to Information Systems
4121:
2064:
653:
As XML is structured as a tree, any data can be easily represented in RDF, which is structured as a graph.
2265:
592:
So, to render an equivalent view based on RDF semantics, the basic mapping algorithm would be as follows:
5152:
4924:
4861:
4614:
3470:
2951:
1629:
1619:
62:
4495:
4471:
4298:
McDowell, Luke K.; Cafarella, Michael (2006). "Ontology-driven Information Extraction with OntoSyphon",
2836:
named entity extraction, entity resolution, relationship extraction, attributes, concepts, multi-vector
1775:
Ontology-based information extraction is a subfield of information extraction, with which at least one
1718:
traditional Information Extraction is domain dependent. The IE is split in the following five subtasks.
356:
5147:
4984:
4569:
1927:
Which types of entities (e.g. named entities, concepts or relationships) can be extracted by the tool?
160:
How is the extracted knowledge made explicit (ontology file, semantic database)? How can you query it?
3803:"Towards Adaptation of Linguistic Annotations to Scholarly Annotation Formalisms on the Semantic Web"
3262:
1969:
The following table characterizes some tools for Knowledge Extraction from natural language sources.
1672:(SRL, related to relation extraction; not to be confused with semantic annotation as described below)
1648:
617:
605:
assign an rdf:type predicate for each row, linking it to an RDFS class IRI corresponding to the table
316:
4528:
4303:
4264:
Fortuna, Blaz; Grobelnik, Marko; Mladenic, Dunja (2007). "OntoGen: Semi-automatic Ontology Editor",
3770:. Lecture Notes in Computer Science. Vol. 7908. Berlin, Heidelberg: Springer. pp. 98â113.
2623:
784:
automatic (domain-specific, for use cases in language technology, preserves relations between rows)
246:
4762:
3443:
1806:
During semantic annotation, natural language text is augmented with metadata (often represented in
1745:
1722:
234:
218:
137:
66:
4414:
1841:
Note that "semantic annotation" in the context of knowledge extraction is not to be confused with
185:
The degree to which the extraction is assisted/automated. Manual, GUI, semi-automatic, automatic.
4979:
4742:
3281:
2841:
1675:
discourse parsing (relations between different sentences, rarely used in real-world applications)
1669:
3880:
3843:
4523:
4362:
4220:
3326:
3269:
2906:
1818:
1754:
1714:
1640:
133:
58:
46:
4380:
4321:
Proceedings of the 2007 international conference on Computational science and its applications
4269:
4969:
4562:
4232:
1811:
1623:
121:
4397:
291:
4777:
3661:
3585:
92:. Another popular example for knowledge extraction is the transformation of Knowledge into
89:
38:
3728:
3650:
http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.97.6934&rep=rep1&type=pdf
645:). Often, however, a suitable domain ontology does not exist and has to be created first.
8:
4701:
4137:
The University of Sheffield (2011). "ANNIE: a Nearly-New Information Extraction System",
3358:
3300:, key for the evaluation and evolution of software systems. Instead of mining individual
3277:
3273:
634:
129:
125:
105:
1871:
Can the tool query the data source or requires a whole dump for the extraction process?
4797:
4686:
4676:
4541:
4068:
4046:
3540:
3399:
3336:
2837:
1663:
642:
4138:
3215:
Knowledge discovery describes the process of automatically searching large volumes of
2102:
566:
5040:
4827:
4747:
4706:
4696:
4660:
4286:
4038:
3982:
3896:
3859:
3781:
3532:
1792:
1606:
1197:
1135:
638:
380:
279:
266:
230:
214:
194:
4545:
4466:
Frawley William. F. et al. (1992), "Knowledge Discovery in Databases: An Overview",
4446:
Inxight Federal Systems (2008). "Inxight ThingFinder and ThingFinder Professional",
4324:
2761:
826:
5020:
4624:
4533:
4212:
4165:
http://www.attensity.com/products/technology/semantic-server/exhaustive-extraction/
4151:
4050:
4030:
3972:
3888:
3851:
3771:
3673:
3544:
3524:
3498:
3493:
3363:
3296:, since existing software artifacts contain enormous value for risk management and
3234:
domain, and is closely related to it both in terms of methodology and terminology.
2790:
named entities, concepts, relations, concepts that categorize the text, enrichments
2142:
1936:
1842:
368:
4098:
4097:
Rocket Software, Inc. (2012). "technology for extracting intelligence from text",
4085:
1259:
323:
during the conversion process. When transforming a typical relational table named
4896:
4875:
4822:
4792:
4711:
4691:
4681:
4502:
4490:
Fayyad U. et al. (1996), "From Data Mining to Knowledge Discovery in Databases",
4478:
4454:
4434:
4344:
4252:
4195:
4171:
4125:
4105:
3977:
3960:
3709:
3592:
3481:
3394:
3387:
3348:
3305:
3289:
2531:
1863:
Which input formats can be processed by the tool (e.g. plain text, HTML or PDF)?
980:
795:
613:
348:
295:
287:
93:
70:
57:
in a manner that facilitates inferencing. Although it is methodically similar to
3892:
3855:
3776:
3280:
is a frequent format of representing knowledge obtained from existing software.
733:
4629:
4427:
4245:
3741:
3448:
3375:
3341:
3297:
1895:
How automated is the extraction process (manual, semi-automatic or automatic)?
1823:
1687:
662:
can be used a standard transformation language to manually convert XML to RDF.
84:
The RDB2RDF W3C group is currently standardizing a language for extraction of
4034:
3635:
3528:
1704:
NLP Annotation Format (NAF, used in the NewsReader workflow management system)
5141:
4948:
4903:
4721:
4447:
4337:
4316:
4019:"Ontology-based prediction and prioritization of gene functional annotations"
3986:
3513:"Ontology-based prediction and prioritization of gene functional annotations"
3453:
3404:
2860:
2669:
888:
4018:
3809:. Jeju, Republic of Korea: Association for Computational Linguistics: 75â84.
3802:
3569:
3512:
1947:
Which model is used to represent the result of the tool (e. g. RDF or OWL)?
1613:
1568:
654:
352:
5010:
4964:
4585:
4042:
3536:
3433:
3382:
2357:
1935:
Which techniques are applied (e.g. NLP, statistical methods, clustering or
1757:(e.g. it and IBM). Both kinds can be recognized by coreference resolution.
702:
379:
is 2, the entry belongs to class Teacher ) or by (semi)-automated methods (
262:
4004:
3820:
1411:
764:
364:
312:
140:(ETL), which transform the data from the sources into structured formats.
5061:
5025:
4787:
4655:
4537:
3370:
3353:
3321:
3293:
3246:
3238:
3231:
3230:
knowledge from the input data. Knowledge discovery developed out of the
3085:
1728:
1228:
132:
and ontology learning. The general process uses traditional methods from
3677:
1011:
116:
After the standardization of knowledge representation languages such as
5071:
4974:
4757:
4752:
4645:
4514:
Cao, L. (2010). "Domain driven data mining: challenges and prospects".
4319:(2007). "ontoX - A Method for Ontology-Driven Information Extraction",
3584:
LOD2 EU Deliverable 3.1.1 Knowledge Extraction from Structured Sources
3423:
3411:
3268:
Another promising application of knowledge discovery is in the area of
3026:
concepts, concept hierarchy, non-taxonomic relations, instances, axioms
2997:
275:
78:
4216:
3608:
1879:
Is the result of the extraction process synchronized with the source?
5102:
4917:
4910:
4812:
4650:
4604:
4496:
http://www.aaai.org/ojs/index.php/aimagazine/article/viewArticle/1230
4472:
http://www.aaai.org/ojs/index.php/aimagazine/article/viewArticle/1011
3565:
3220:
3043:
226:
152:
Which data sources are covered: Text, Relational Databases, XML, CSV
4082:
Multi-source, Multi-lingual Information Extraction and Summarization
3936:
5112:
4802:
4609:
4599:
4300:
Proceedings of the 5th international conference on The Semantic Web
4185:
Proceedings of the 7th International Conference on Semantic Systems
3438:
3428:
3331:
3309:
3301:
1684:
NLP Interchange Format (NIF, for many frequent types of annotation)
1186:
automatic, the user furthermore has the chance to fine-tune results
567:
1:1 Mapping from RDB Tables/Views to RDF Entities/Attributes/Values
283:
278:
resource, further information can be retrieved automatically and a
4359:
Proceedings of the 12th international conference on World Wide Web
3265:, aims to discover and deliver actionable knowledge and insights.
3245:(KDD). Just as many other forms of knowledge discovery it creates
623:
5122:
5117:
4889:
4882:
4817:
4377:
Web Semantics: Science, Services and Agents on the World Wide Web
4304:
http://turing.cs.washington.edu/papers/iswc2006McDowell-final.pdf
4023:
IEEE/ACM Transactions on Computational Biology and Bioinformatics
3517:
IEEE/ACM Transactions on Computational Biology and Bioinformatics
2744:
annotation to entities, annotation to events, annotation to facts
1834:
1780:
1693:
CoNLL-RDF (for annotations originally represented in TSV formats)
1537:
1475:
1444:
612:
Early mentioning of this basic or direct mapping can be found in
410:
242:
101:
4118:
3999:
Cunningham, Hamish (2005). "Information Extraction, Automatic",
2019:
426:
5035:
4938:
4554:
4415:
http://users.csc.calpoly.edu/~fkurfess/Events/DM-KM-01/Volz.pdf
4411:
Proceedings of the IEEE International Conference on Data Mining
3742:"NLP Interchange Format (NIF) 2.0 - Overview and Documentation"
3168:
English, German, Spanish, French, Portuguese, Italian, Russian
2517:
concepts, concept hierarchy, non-taxonomic relations, instances
2406:
1770:
282:
can for example infer that the mentioned entity is of the type
222:
3573:
3029:
NLP, statistical methods, machine learning, rule-based methods
1657:
syntactic parsing, often adopting syntactic dependencies (DEP)
27:
Creation of knowledge from structured and unstructured sources
5107:
5097:
5015:
4994:
4767:
4426:
Machine Linking. "We connect to the Linked Open Data cloud",
4266:
Proceedings of the 2007 conference on Human interface, Part 2
2715:
2311:
50:
4363:
http://www2003.org/cdrom/papers/refereed/p831/p831-dill.html
4221:
http://www.semantic-web-journal.net/system/files/swj1379.pdf
3916:
1636:
Typical NLP tasks relevant to knowledge extraction include:
561:
5066:
5056:
5030:
4850:
4381:
http://staffwww.dcs.shef.ac.uk/people/J.Iria/iria_jws06.pdf
4270:
http://analytics.ijs.si/~blazf/papers/OntoGen2_HCII2007.pdf
4131:
3405:
Learning from time-varying data streams under concept drift
3216:
1963:
Which languages can be processed (e.g. English or German)?
1807:
1600:
659:
4233:
http://www.dfki.uni-kl.de/~maus/dok/AdrianMausDengel09.pdf
3957:
3764:
1708:
576:
Each column in the table is an attribute (i.e., predicate)
81:) or the generation of a schema based on the source data.
4943:
4398:
http://www.cimiano.de/Publications/2005/nldb05/nldb05.pdf
4144:
2807:
2447:
1919:
Which approach (IE, OBIE, OL or SA) is used by the tool?
1614:
Linguistic annotation / natural language processing (NLP)
42:
3662:
http://le2i.cnrs.fr/IMG/publications/InterDB07-Ghawi.pdf
3586:
http://static.lod2.eu/Deliverables/deliverable-3.1.1.pdf
2493:
2219:
4440:
3878:
3844:"CoNLL-RDF: Linked Corpora Done in an NLP-Friendly Way"
3807:
Proceedings of the Sixth Linguistic Annotation Workshop
3729:
http://ix.cs.uoregon.edu/~dou/research/papers/jis09.pdf
1955:
Which domains are supported (e.g. economy or biology)?
3156:
annotation to proper nouns, annotation to common nouns
3073:
NLP, machine learning, clustering, statistical methods
1701:
LAPPS Interchange Format (LIF, used in the LAPPS Grid)
1380:
1105:
OntoWiki CSV Importer Plug-in - DataCube & Tabular
579:
Each column value is an attribute value (i.e., object)
4238:
4244:
SRA International, Inc. (2012). "NetOwl Extractor",
4069:
http://www.ida.liu.se/ext/epa/cis/2001/002/paper.pdf
3630:
3628:
2340:
annotation to each word, annotation to non-stopwords
2294:
annotation to each word, annotation to non-stopwords
582:
Each row key represents an entity ID (i.e., subject)
343:
can be defined as symmetrical relation and a column
4516:
IEEE Transactions on Knowledge and Data Engineering
4139:
http://gate.ac.uk/sale/tao/splitch6.html#chap:annie
4091:
3597:
3253:obtained through the process may become additional
1903:Does the tool need an ontology for the extraction?
599:
convert all primary keys and foreign keys into IRIs
371:(in form of an ontology) could be created from the
4287:http://wwwusers.di.uniroma1.it/~velardi/IEEE_C.pdf
3800:
4325:http://publik.tuwien.ac.at/files/pub-inf_4769.pdf
3625:
1654:named entity recognition (NER, also see IE below)
1326:Multidimensional statistical data in spreadsheets
5139:
4152:http://www-ai.ijs.si/~ilpnet2/systems/asium.html
3801:Verspoor, Karin; Livingston, Kevin (July 2012).
1911:Does the tool offer a graphical user interface?
1887:Does the tool link the result with an ontology?
4420:
4336:semanticweb.org (2011). "PoolParty Extractor",
4330:
4099:http://www.rocketsoftware.com/products/aerotext
4086:http://www.cs.jhu.edu/~delip/entity-linking.pdf
4016:
3719:
3717:
3510:
624:Complex mappings of relational databases to RDF
2059:English, Spanish, Arabic, Chinese, indonesian
302:
4570:
3842:Chiarcos, Christian; FĂ€th, Christian (2017).
4494:(Vol 17, No 3), 37-54 (online full version:
4470:(Vol 13, No 3), 57-70 (online full version:
4460:
4428:http://thewikimachine.fbk.eu/html/index.html
4246:http://www.sra.com/netowl/entity-extraction/
3841:
3714:
1771:Ontology-based information extraction (OBIE)
665:
4163:Attensity (2012). "Exhaustive Extraction",
3883:. In Murakami, Yohei; Lin, Donghui (eds.).
2812:plain text, HTML, XML, SGML, PDF, MS Office
2452:plain text, HTML, XML, SGML, PDF, MS Office
1801:
1651:(WSD, related to semantic annotation below)
1480:structured and semi-structured data sources
629:relational tables (Details can be found in
363:table can be made an instance of the class
73:. It requires either the reuse of existing
4577:
4563:
4484:
4448:http://inxightfedsys.com/products/sdks/tf/
4338:http://semanticweb.org/PoolParty_Extractor
4150:ILP Network of Excellence. "ASIUM (LRI)",
3993:
3636:"Relational Databases on the Semantic Web"
2793:NLP, machine learning, statistical methods
2297:NLP, statistical methods, machine learning
703:A Direct Mapping of Relational Data to RDF
274:As President Obama is linked to a DBpedia
4527:
4386:
4275:
4157:
4117:Orchestr8 (2012): "AlchemyAPI Overview",
4111:
3976:
3885:Worldwide Language Service Infrastructure
3775:
3570:http://www.w3.org/2009/08/rdb2rdf-charter
2401:English, other languages via translation
1786:
1753:relationship between an entity and their
1697:Other, platform-specific formats include
562:Extraction from structured sources to RDF
4292:
4001:Encyclopedia of Language and Linguistics
1645:lemmatization (LEMMA) or stemming (STEM)
1601:Extraction from natural language sources
347:can be converted to a property from the
4403:
4258:
4201:
4057:
4005:http://gate.ac.uk/sale/ell2/ie/main.pdf
2992:English, German, French, Dutch, polish
1709:Traditional information extraction (IE)
375:, either by manually created rules (if
14:
5140:
4783:Knowledge representation and reasoning
4717:Semantic service-oriented architecture
4309:
4225:
4177:
3682:
3578:
3572:, R2RML: RDB to RDF Mapping Language:
3558:
3241:is knowledge discovery, also known as
3210:
2889:instances, property values, RDFS types
2606:concepts, concept hierarchy, instances
2560:concepts, concept hierarchy, instances
2392:NLP, machine learning, heuristic rules
2383:IE, OL, SA, ontology design patterns,
1025:proprietary xml based mapping language
585:Each row represents an entity instance
496:<http://example.org/Peters_page>
237:and then disambiguates candidates via
4558:
3694:
3195:named entities, relationships, events
2476:named entities, relationships, events
2205:named entities, relationships, events
2047:named entities, relationships, events
602:assign a predicate IRI to each column
383:). Here is an example transformation:
4368:
4350:
3654:
3607:. www.opencalais.com. Archived from
3226:the data. It is often described as
3219:for patterns that can be considered
631:object-relational impedance mismatch
367:(Ontology Population). Additionally
331:) or an aggregation of columns (e.g.
241:and links the found entities to the
4513:
4074:
2698:instances, datatype property values
1737:Template relation construction (TR)
1690:(WA, often used for entity linking)
596:create an RDFS class for each table
24:
4844:Syntax and supporting technologies
3566:http://www.w3.org/2001/sw/rdb2rdf/
3504:
3465:Business Process Modeling Notation
3284:(OMG) developed the specification
2956:plain text, HTML, PDF, DOC, e-Mail
1734:Template element construction (TE)
25:
5169:
3666:
3641:
3417:
3048:plain text, HTML, PDF, PostScript
2802:English, German, Spanish, French
2520:NLP, machine learning, clustering
1740:Template scenario production (ST)
208:
96:and also the mapping to existing
4584:
4017:Chicco, D; Masseroli, M (2016).
3638:. Retrieved: February 20, 2011.
3564:RDB2RDF Working Group, Website:
3511:Chicco, D; Masseroli, M (2016).
3243:knowledge discovery in databases
715:
4507:
4010:
3951:
3929:
3909:
3872:
3835:
3813:
3794:
3758:
3734:
3605:"Life in the Linked Data Cloud"
3110:concepts, relations, hierarchy
2183:Attensity Exhaustive Extraction
292:Presidents of the United States
86:resource description frameworks
3881:"The LAPPS Interchange Format"
3725:Journal of Information Science
3476:Resource Description Framework
3259:actionable knowledge discovery
3237:The most well-known branch of
2652:concepts, relations, instances
1830:link these at entity linking.
411:http://example.org/Peters_page
13:
1:
4958:Schemas, ontologies and rules
4119:http://www.alchemyapi.com/api
3848:Language, Data, and Knowledge
3552:
3459:Knowledge Discovery Metamodel
3315:
3286:Knowledge Discovery Metamodel
920:Google Refine's RDF Extension
427:http://example.org/Claus_page
3978:10.1016/j.knosys.2016.07.013
3768:The Semantic Web â ISWC 2013
2855:Multilingual 200+ Languages
1641:part-of-speech (POS) tagging
1322:The RDF Data Cube Vocabulary
1118:The RDF Data Cube Vocaublary
138:extract, transform, and load
7:
3893:10.1007/978-3-319-31468-6_3
3856:10.1007/978-3-319-59888-8_6
3777:10.1007/978-3-642-41338-4_7
3746:persistence.uni-leipzig.org
3574:http://www.w3.org/TR/r2rml/
3487:
3471:Intermediate representation
2631:dump, search engine queries
2168:concepts, concept hierarchy
2023:plain text, HTML, XML, SGML
1620:Natural language processing
357:inverse functional property
355:, thus qualifying it as an
303:Relational databases to RDF
203:
190:Requires a domain ontology
111:
10:
5174:
4985:Semantic Web Rule Language
4383:, (retrieved: 18.06.2012).
3132:plain text, HTML, PDF, DOC
2766:plain text, HTML, DOC, ODT
2482:XML, JSON, RDF-OWL, others
2432:instances, property values
1790:
1617:
1538:XLWrap: Spreadsheet to RDF
251:DBpedia Spotlight web demo
5090:
5049:
5003:
4957:
4843:
4836:
4735:
4669:
4638:
4592:
4065:Proceedings of the COLING
4035:10.1109/TCBB.2015.2459694
3634:Tim Berners-Lee (1998),
3529:10.1109/TCBB.2015.2459694
3263:domain driven data mining
3038:English, German, Spanish
2756:English, French, Spanish
1649:word sense disambiguation
1136:Poolparty Extraktor (PPX)
666:Survey of methods / tools
359:. Then each entry of the
5091:Microformat vocabularies
4763:Information architecture
4457:(retrieved: 18.06.2012).
4437:(retrieved: 18.06.2012).
4417:(retrieved: 18.06.2012).
4400:(retrieved: 18.06.2012).
4365:(retrieved: 18.06.2012).
4347:(retrieved: 18.06.2012).
4327:(retrieved: 18.06.2012).
4306:(retrieved: 18.06.2012).
4289:(retrieved: 18.06.2012).
4272:(retrieved: 18.06.2012).
4255:(retrieved: 18.06.2012).
4235:(retrieved: 18.06.2012).
4198:(retrieved: 18.06.2012).
4174:(retrieved: 18.06.2012).
4154:(retrieved: 18.06.2012).
4141:(retrieved: 18.06.2012).
4128:(retrieved: 18.06.2012).
4108:(retrieved: 18.06.2012).
4088:(retrieved: 18.06.2012).
4071:(retrieved: 18.06.2012).
4007:(retrieved: 18.06.2012).
3941:, NewsReader, 2020-05-25
3731:(retrieved: 18.06.2012).
3444:Knowledge representation
2655:NLP, statistical methods
2609:NLP, statistical methods
2563:NLP, statistical methods
2248:named entities, concepts
1849:
1802:Semantic annotation (SA)
1746:named entity recognition
1723:Named entity recognition
436:
255:PoolParty Extractor Demo
235:named-entity recognition
77:(reusing identifiers or
4980:Rule Interchange Format
4743:Collective intelligence
3965:Knowledge-Based Systems
3727:, 36(3), p. 306 - 323,
3282:Object Management Group
3249:of the input data. The
2842:language identification
2701:heuristic-based methods
2352:English, German, Dutch
2130:finite state algorithms
1670:semantic role labelling
4396:, 3513, p. 227 - 238,
4285:, 35(11), p. 60 - 63,
3292:is closely related to
3270:software modernization
1819:Terminology extraction
1787:Ontology learning (OL)
1729:Coreference resolution
1715:information extraction
1489:Virtuoso PL & XSLT
648:
271:
247:Dandelion dataTXT demo
245:knowledge repository (
233:analyze free text via
173:Reuse of vocabularies
134:information extraction
59:information extraction
5158:Information economics
3002:plain text, HTML, PDF
2983:NLP, machine learning
2946:language-independent
2892:NLP, machine learning
2846:NLP, machine learning
2747:NLP, machine learning
2720:plain text, HTML, XML
2710:language-independent
2224:plain text, HTML, URL
1812:machine-readable data
1624:Linguistic Annotation
616:'s comparison of the
260:
4778:Knowledge management
4773:Knowledge extraction
4538:10.1109/tkde.2010.32
4379:, 4(1), p. 14 - 28,
4209:Semantic Web Journal
3822:acoli-repo/conll-rdf
2849:XML, JSON, POJO, RDF
2014:Supported Languages
1987:Uses Output Ontology
1984:Data Synchronization
1884:Uses Output Ontology
1876:Data Synchronization
1755:anaphoric references
1458:Meta Schema Language
1333:Data Cube Vocabulary
694:Req. Domain Ontology
682:Data Synchronisation
265:called Wednesday on
90:relational databases
45:) and unstructured (
39:relational databases
31:Knowledge extraction
5050:Common vocabularies
5004:Semantic annotation
4702:Semantic publishing
4323:, 3, p. 660 - 673,
4003:, 2, p. 665 - 677,
3825:, ACoLi, 2020-05-27
3678:10.1007/11563952_19
3359:Configuration files
3278:entity relationship
3274:reverse engineering
3211:Knowledge discovery
3035:deomain-independent
2762:PoolParty Extractor
2312:EntityClassifier.eu
2251:statistical methods
1960:Supported Languages
800:Delimited text file
772:SPARQL/ RDF stream
231:PoolParty Extractor
225:, the Zemanta API,
130:knowledge discovery
126:identity resolution
55:represent knowledge
33:is the creation of
18:Knowledge discovery
5153:Knowledge transfer
4798:Digital humanities
4687:Semantic computing
4677:Semantic analytics
4661:Rule-based systems
4501:2016-05-04 at the
4477:2016-03-04 at the
4453:2012-06-29 at the
4433:2012-07-19 at the
4343:2016-03-04 at the
4251:2012-09-24 at the
4194:2012-04-05 at the
4170:2012-07-11 at the
4124:2016-05-13 at the
4104:2013-06-21 at the
3708:2011-07-20 at the
3591:2011-08-27 at the
3400:Data stream mining
3337:Document warehouse
3165:domain-independent
2989:domain-independent
2943:domain-independent
2898:domain-independent
2838:sentiment analysis
2799:domain-independent
2753:domain-independent
2707:domain-independent
2661:domain-independent
2615:domain-independent
2578:OntoLearn Reloaded
2569:domain-independent
2440:personal, business
2398:domain-independent
2349:domain-independent
2343:rule-based grammar
2303:domain-independent
2257:domain-independent
2056:domain-independent
2005:Applied Techniques
2002:Extracted Entities
1990:Mapping Automation
1932:Applied Techniques
1924:Extracted Entities
1892:Mapping Automation
1664:anaphor resolution
1445:Virtuoso RDF Views
865:own query language
643:ontology alignment
635:reverse engineered
620:to the RDF model.
327:, one column (e.g.
5148:Knowledge economy
5135:
5134:
5131:
5130:
5041:Facebook Platform
4928:
4927:(no W3C standard)
4920:
4913:
4906:
4899:
4892:
4885:
4878:
4864:
4828:Web Science Trust
4748:Description logic
4707:Semantic reasoner
4697:Semantic matching
4625:Semantic networks
4217:10.3233/SW-160240
3902:978-3-319-31468-6
3865:978-3-319-59888-8
3787:978-3-642-41338-4
3208:
3207:
3119:multiple domains
3113:NLP, proprietary
2266:DBpedia Spotlight
2011:Supported Domains
1993:Requires Ontology
1967:
1966:
1952:Supported Domains
1900:Requires Ontology
1793:Ontology learning
1607:unstructured data
1598:
1597:
1350:TopBraid Composer
639:ontology learning
475:SymmetricProperty
435:
434:
381:ontology learning
280:Semantic Reasoner
223:Dandelion dataTXT
215:DBpedia Spotlight
201:
200:
195:ontology learning
71:relational schema
37:from structured (
16:(Redirected from
5165:
4923:
4916:
4909:
4902:
4895:
4888:
4881:
4874:
4860:
4841:
4840:
4579:
4572:
4565:
4556:
4555:
4550:
4549:
4531:
4511:
4505:
4488:
4482:
4464:
4458:
4444:
4438:
4424:
4418:
4407:
4401:
4390:
4384:
4372:
4366:
4361:, p. 178 - 186,
4354:
4348:
4334:
4328:
4313:
4307:
4302:, p. 428 - 444,
4296:
4290:
4279:
4273:
4268:, p. 309 - 318,
4262:
4256:
4242:
4236:
4229:
4223:
4205:
4199:
4181:
4175:
4161:
4155:
4148:
4142:
4135:
4129:
4115:
4109:
4095:
4089:
4078:
4072:
4061:
4055:
4054:
4014:
4008:
3997:
3991:
3990:
3980:
3955:
3949:
3948:
3947:
3946:
3933:
3927:
3926:
3924:
3923:
3913:
3907:
3906:
3876:
3870:
3869:
3839:
3833:
3832:
3831:
3830:
3817:
3811:
3810:
3798:
3792:
3791:
3779:
3762:
3756:
3755:
3753:
3752:
3738:
3732:
3721:
3712:
3698:
3692:
3686:
3680:
3670:
3664:
3658:
3652:
3645:
3639:
3632:
3623:
3622:
3617:
3616:
3601:
3595:
3582:
3576:
3562:
3548:
3499:Data archaeology
3494:Cluster analysis
3482:Software metrics
3261:, also known as
3159:machine learning
3128:The Wiki Machine
2937:machine learning
2901:English, German
2865:plain text, HTML
2852:multiple domains
2582:plain text, HTML
2536:plain text, HTML
2485:multiple domains
2448:NetOwl Extractor
2316:plain text, HTML
2270:plain text, HTML
2069:plain text, HTML
2050:linguistic rules
1972:
1971:
1937:machine learning
1857:
1856:
1843:semantic parsing
1476:Virtuoso Sponger
691:Mapping Automat.
688:Vocabulary Reuse
685:Mapping Language
670:
669:
633:) and has to be
557:
554:
551:
548:
545:
542:
539:
536:
533:
530:
527:
524:
521:
518:
515:
512:
509:
506:
503:
500:
497:
494:
491:
488:
485:
482:
479:
476:
473:
470:
467:
464:
461:
458:
455:
452:
449:
446:
443:
440:
387:
386:
369:domain knowledge
165:Synchronization
146:
145:
75:formal knowledge
21:
5173:
5172:
5168:
5167:
5166:
5164:
5163:
5162:
5138:
5137:
5136:
5127:
5086:
5045:
4999:
4953:
4832:
4823:Web engineering
4793:Digital library
4731:
4712:Semantic search
4692:Semantic mapper
4682:Semantic broker
4665:
4634:
4588:
4583:
4553:
4529:10.1.1.190.8427
4512:
4508:
4503:Wayback Machine
4489:
4485:
4479:Wayback Machine
4465:
4461:
4455:Wayback Machine
4445:
4441:
4435:Wayback Machine
4425:
4421:
4408:
4404:
4391:
4387:
4373:
4369:
4355:
4351:
4345:Wayback Machine
4335:
4331:
4315:Yildiz, Burcu;
4314:
4310:
4297:
4293:
4280:
4276:
4263:
4259:
4253:Wayback Machine
4243:
4239:
4230:
4226:
4206:
4202:
4196:Wayback Machine
4182:
4178:
4172:Wayback Machine
4162:
4158:
4149:
4145:
4136:
4132:
4126:Wayback Machine
4116:
4112:
4106:Wayback Machine
4096:
4092:
4079:
4075:
4062:
4058:
4015:
4011:
3998:
3994:
3956:
3952:
3944:
3942:
3935:
3934:
3930:
3921:
3919:
3915:
3914:
3910:
3903:
3877:
3873:
3866:
3840:
3836:
3828:
3826:
3819:
3818:
3814:
3799:
3795:
3788:
3763:
3759:
3750:
3748:
3740:
3739:
3735:
3722:
3715:
3710:Wayback Machine
3699:
3695:
3687:
3683:
3671:
3667:
3659:
3655:
3646:
3642:
3633:
3626:
3614:
3612:
3603:
3602:
3598:
3593:Wayback Machine
3583:
3579:
3563:
3559:
3555:
3507:
3505:Further reading
3490:
3420:
3388:Molecule mining
3327:Relational data
3318:
3306:software mining
3290:software mining
3213:
2940:database record
2385:frame semantics
2171:NLP, clustering
1981:Access Paradigm
1868:Access Paradigm
1852:
1804:
1795:
1789:
1773:
1711:
1626:
1618:Main articles:
1616:
1603:
707:Relational Data
679:Data Exposition
668:
651:
626:
614:Tim Berners-Lee
569:
564:
559:
558:
555:
552:
549:
546:
543:
540:
537:
534:
531:
528:
525:
522:
519:
516:
513:
510:
507:
504:
501:
498:
495:
492:
489:
486:
483:
480:
477:
474:
471:
468:
465:
462:
459:
456:
453:
450:
447:
444:
441:
438:
349:FOAF Vocabulary
305:
288:FOAF (software)
263:President Obama
239:name resolution
211:
206:
182:Automatization
114:
94:structured data
28:
23:
22:
15:
12:
11:
5:
5171:
5161:
5160:
5155:
5150:
5133:
5132:
5129:
5128:
5126:
5125:
5120:
5115:
5110:
5105:
5100:
5094:
5092:
5088:
5087:
5085:
5084:
5079:
5074:
5069:
5064:
5059:
5053:
5051:
5047:
5046:
5044:
5043:
5038:
5033:
5028:
5023:
5018:
5013:
5007:
5005:
5001:
5000:
4998:
4997:
4992:
4987:
4982:
4977:
4972:
4967:
4961:
4959:
4955:
4954:
4952:
4951:
4946:
4941:
4936:
4931:
4930:
4929:
4921:
4914:
4907:
4900:
4893:
4886:
4879:
4867:
4866:
4865:
4853:
4847:
4845:
4838:
4834:
4833:
4831:
4830:
4825:
4820:
4815:
4810:
4805:
4800:
4795:
4790:
4785:
4780:
4775:
4770:
4765:
4760:
4755:
4750:
4745:
4739:
4737:
4736:Related topics
4733:
4732:
4730:
4729:
4724:
4719:
4714:
4709:
4704:
4699:
4694:
4689:
4684:
4679:
4673:
4671:
4667:
4666:
4664:
4663:
4658:
4653:
4648:
4642:
4640:
4636:
4635:
4633:
4632:
4630:World Wide Web
4627:
4622:
4617:
4612:
4607:
4602:
4596:
4594:
4590:
4589:
4582:
4581:
4574:
4567:
4559:
4552:
4551:
4522:(6): 755â769.
4506:
4483:
4459:
4439:
4419:
4402:
4385:
4367:
4349:
4329:
4317:Miksch, Silvia
4308:
4291:
4274:
4257:
4237:
4224:
4200:
4176:
4156:
4143:
4130:
4110:
4090:
4073:
4056:
4029:(2): 248â260.
4009:
3992:
3950:
3938:newsreader/NAF
3928:
3908:
3901:
3871:
3864:
3834:
3812:
3793:
3786:
3757:
3733:
3713:
3693:
3681:
3665:
3653:
3640:
3624:
3596:
3577:
3556:
3554:
3551:
3550:
3549:
3523:(2): 248â260.
3506:
3503:
3502:
3501:
3496:
3489:
3486:
3485:
3484:
3479:
3473:
3468:
3462:
3456:
3451:
3449:Knowledge tags
3446:
3441:
3436:
3431:
3426:
3419:
3418:Output formats
3416:
3415:
3414:
3409:
3408:
3407:
3402:
3392:
3391:
3390:
3380:
3379:
3378:
3376:Concept mining
3368:
3367:
3366:
3361:
3356:
3346:
3345:
3344:
3342:Data warehouse
3339:
3334:
3329:
3317:
3314:
3298:business value
3212:
3209:
3206:
3205:
3202:
3200:
3198:
3196:
3193:
3190:
3188:
3186:
3184:
3182:
3180:
3178:
3176:
3170:
3169:
3166:
3163:
3160:
3157:
3154:
3151:
3148:
3145:
3142:
3139:
3136:
3133:
3130:
3124:
3123:
3120:
3117:
3114:
3111:
3108:
3106:
3103:
3101:
3098:
3096:
3094:
3091:
3088:
3082:
3081:
3078:
3076:
3074:
3071:
3068:
3065:
3062:
3059:
3058:semi-automatic
3056:
3054:
3052:
3049:
3046:
3040:
3039:
3036:
3033:
3030:
3027:
3024:
3021:
3018:
3015:
3014:semi-automatic
3012:
3009:
3006:
3003:
3000:
2994:
2993:
2990:
2987:
2984:
2981:
2980:named entities
2978:
2975:
2972:
2969:
2966:
2963:
2960:
2957:
2954:
2948:
2947:
2944:
2941:
2938:
2935:
2933:
2930:
2927:
2924:
2921:
2918:
2915:
2912:
2909:
2903:
2902:
2899:
2896:
2893:
2890:
2887:
2884:
2881:
2878:
2875:
2872:
2869:
2866:
2863:
2857:
2856:
2853:
2850:
2847:
2844:
2840:, geotagging,
2834:
2831:
2828:
2825:
2822:
2819:
2816:
2813:
2810:
2804:
2803:
2800:
2797:
2794:
2791:
2788:
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2782:
2779:
2776:
2773:
2770:
2767:
2764:
2758:
2757:
2754:
2751:
2748:
2745:
2742:
2739:
2736:
2733:
2730:
2727:
2724:
2721:
2718:
2712:
2711:
2708:
2705:
2702:
2699:
2696:
2693:
2690:
2687:
2686:semi-automatic
2684:
2681:
2678:
2675:
2672:
2666:
2665:
2662:
2659:
2656:
2653:
2650:
2647:
2644:
2641:
2638:
2635:
2632:
2629:
2628:HTML, PDF, DOC
2626:
2620:
2619:
2616:
2613:
2610:
2607:
2604:
2601:
2598:
2595:
2592:
2589:
2586:
2583:
2580:
2574:
2573:
2570:
2567:
2564:
2561:
2558:
2555:
2552:
2549:
2546:
2543:
2540:
2537:
2534:
2528:
2527:
2525:
2523:
2521:
2518:
2515:
2512:
2509:
2507:
2506:semi-automatic
2504:
2502:
2500:
2498:
2496:
2490:
2489:
2486:
2483:
2480:
2477:
2474:
2471:
2468:
2465:
2462:
2459:
2456:
2453:
2450:
2444:
2443:
2441:
2438:
2436:
2433:
2430:
2427:
2424:
2422:
2420:
2417:
2415:
2412:
2411:HTML, PDF, DOC
2409:
2403:
2402:
2399:
2396:
2393:
2390:
2387:
2381:
2378:
2375:
2372:
2369:
2366:
2365:dump, REST API
2363:
2360:
2354:
2353:
2350:
2347:
2344:
2341:
2338:
2335:
2332:
2329:
2326:
2323:
2320:
2317:
2314:
2308:
2307:
2304:
2301:
2298:
2295:
2292:
2289:
2286:
2283:
2280:
2277:
2274:
2271:
2268:
2262:
2261:
2258:
2255:
2252:
2249:
2246:
2243:
2240:
2237:
2234:
2231:
2228:
2225:
2222:
2216:
2215:
2213:
2211:
2209:
2206:
2203:
2200:
2198:
2196:
2193:
2191:
2189:
2187:
2185:
2179:
2178:
2176:
2174:
2172:
2169:
2166:
2163:
2160:
2158:
2157:semi-automatic
2155:
2153:
2151:
2148:
2145:
2139:
2138:
2135:
2133:
2131:
2128:
2126:
2123:
2120:
2117:
2115:
2113:
2111:
2108:
2105:
2099:
2098:
2095:
2093:
2091:
2089:
2087:
2084:
2081:
2079:
2076:
2074:
2072:
2070:
2067:
2061:
2060:
2057:
2054:
2051:
2048:
2045:
2042:
2039:
2036:
2033:
2030:
2027:
2024:
2021:
2016:
2015:
2012:
2009:
2006:
2003:
2000:
1997:
1994:
1991:
1988:
1985:
1982:
1979:
1976:
1965:
1964:
1961:
1957:
1956:
1953:
1949:
1948:
1945:
1941:
1940:
1933:
1929:
1928:
1925:
1921:
1920:
1917:
1913:
1912:
1909:
1905:
1904:
1901:
1897:
1896:
1893:
1889:
1888:
1885:
1881:
1880:
1877:
1873:
1872:
1869:
1865:
1864:
1861:
1851:
1848:
1827:
1826:
1824:Entity linking
1821:
1803:
1800:
1791:Main article:
1788:
1785:
1772:
1769:
1742:
1741:
1738:
1735:
1732:
1726:
1710:
1707:
1706:
1705:
1702:
1695:
1694:
1691:
1688:Web Annotation
1685:
1677:
1676:
1673:
1667:
1661:
1658:
1655:
1652:
1646:
1643:
1615:
1612:
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1577:
1574:
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1564:
1561:
1558:
1555:
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1549:
1546:
1543:
1540:
1534:
1533:
1530:
1527:
1524:
1521:
1518:
1515:
1512:
1509:
1503:
1502:
1499:
1496:
1495:semi-automatic
1493:
1490:
1487:
1484:
1481:
1478:
1472:
1471:
1468:
1465:
1464:semi-automatic
1462:
1459:
1456:
1453:
1450:
1447:
1441:
1440:
1437:
1434:
1433:semi-automatic
1431:
1428:
1423:
1420:
1417:
1414:
1408:
1407:
1404:
1401:
1398:
1395:
1392:
1389:
1386:
1383:
1377:
1376:
1373:
1370:
1369:semi-automatic
1367:
1364:
1361:
1358:
1355:
1352:
1346:
1345:
1343:
1340:
1337:
1334:
1331:
1329:
1327:
1324:
1318:
1317:
1314:
1311:
1308:
1305:
1302:
1299:
1296:
1293:
1287:
1286:
1283:
1280:
1277:
1274:
1271:
1268:
1265:
1262:
1260:Relational.OWL
1256:
1255:
1252:
1249:
1246:
1243:
1240:
1237:
1234:
1231:
1225:
1224:
1221:
1218:
1215:
1212:
1209:
1206:
1203:
1200:
1194:
1193:
1190:
1187:
1184:
1181:
1178:
1175:
1172:
1169:
1163:
1162:
1159:
1156:
1155:semi-automatic
1153:
1150:
1147:
1144:
1141:
1138:
1132:
1131:
1128:
1125:
1124:semi-automatic
1122:
1119:
1116:
1113:
1110:
1107:
1101:
1100:
1097:
1094:
1091:
1088:
1085:
1082:
1079:
1076:
1070:
1069:
1066:
1063:
1060:
1057:
1054:
1051:
1048:
1045:
1039:
1038:
1035:
1032:
1029:
1026:
1023:
1020:
1017:
1014:
1008:
1007:
1004:
1001:
998:
995:
992:
989:
986:
983:
977:
976:
973:
970:
967:
964:
961:
958:
955:
952:
946:
945:
942:
939:
938:semi-automatic
936:
934:
931:
928:
925:
922:
916:
915:
912:
909:
906:
903:
900:
897:
894:
891:
885:
884:
881:
878:
875:
872:
869:
866:
863:
860:
854:
853:
850:
847:
844:
841:
838:
837:bi-directional
835:
832:
829:
823:
822:
819:
816:
813:
810:
807:
804:
801:
798:
792:
791:
788:
785:
782:
779:
776:
773:
770:
767:
761:
760:
757:
754:
751:
748:
745:
742:
739:
736:
730:
729:
726:
723:
720:
717:
714:
711:
708:
705:
699:
698:
695:
692:
689:
686:
683:
680:
677:
674:
667:
664:
650:
647:
625:
622:
610:
609:
606:
603:
600:
597:
590:
589:
586:
583:
580:
577:
568:
565:
563:
560:
437:
433:
432:
429:
424:
421:
417:
416:
413:
408:
405:
401:
400:
397:
394:
391:
385:
384:
311:, D2R Server,
304:
301:
300:
299:
290:) and of type
259:
258:
210:
209:Entity linking
207:
205:
202:
199:
198:
191:
187:
186:
183:
179:
178:
174:
170:
169:
166:
162:
161:
158:
154:
153:
150:
113:
110:
26:
9:
6:
4:
3:
2:
5170:
5159:
5156:
5154:
5151:
5149:
5146:
5145:
5143:
5124:
5121:
5119:
5116:
5114:
5111:
5109:
5106:
5104:
5101:
5099:
5096:
5095:
5093:
5089:
5083:
5080:
5078:
5075:
5073:
5070:
5068:
5065:
5063:
5060:
5058:
5055:
5054:
5052:
5048:
5042:
5039:
5037:
5034:
5032:
5029:
5027:
5024:
5022:
5019:
5017:
5014:
5012:
5009:
5008:
5006:
5002:
4996:
4993:
4991:
4988:
4986:
4983:
4981:
4978:
4976:
4973:
4971:
4968:
4966:
4963:
4962:
4960:
4956:
4950:
4949:Semantic HTML
4947:
4945:
4942:
4940:
4937:
4935:
4932:
4926:
4922:
4919:
4915:
4912:
4908:
4905:
4901:
4898:
4894:
4891:
4887:
4884:
4880:
4877:
4873:
4872:
4871:
4868:
4863:
4859:
4858:
4857:
4854:
4852:
4849:
4848:
4846:
4842:
4839:
4835:
4829:
4826:
4824:
4821:
4819:
4816:
4814:
4811:
4809:
4806:
4804:
4801:
4799:
4796:
4794:
4791:
4789:
4786:
4784:
4781:
4779:
4776:
4774:
4771:
4769:
4766:
4764:
4761:
4759:
4756:
4754:
4751:
4749:
4746:
4744:
4741:
4740:
4738:
4734:
4728:
4725:
4723:
4722:Semantic wiki
4720:
4718:
4715:
4713:
4710:
4708:
4705:
4703:
4700:
4698:
4695:
4693:
4690:
4688:
4685:
4683:
4680:
4678:
4675:
4674:
4672:
4668:
4662:
4659:
4657:
4654:
4652:
4649:
4647:
4644:
4643:
4641:
4637:
4631:
4628:
4626:
4623:
4621:
4618:
4616:
4613:
4611:
4608:
4606:
4603:
4601:
4598:
4597:
4595:
4591:
4587:
4580:
4575:
4573:
4568:
4566:
4561:
4560:
4557:
4547:
4543:
4539:
4535:
4530:
4525:
4521:
4517:
4510:
4504:
4500:
4497:
4493:
4487:
4480:
4476:
4473:
4469:
4463:
4456:
4452:
4449:
4443:
4436:
4432:
4429:
4423:
4416:
4412:
4406:
4399:
4395:
4389:
4382:
4378:
4371:
4364:
4360:
4353:
4346:
4342:
4339:
4333:
4326:
4322:
4318:
4312:
4305:
4301:
4295:
4288:
4284:
4278:
4271:
4267:
4261:
4254:
4250:
4247:
4241:
4234:
4228:
4222:
4218:
4214:
4210:
4204:
4197:
4193:
4190:
4186:
4180:
4173:
4169:
4166:
4160:
4153:
4147:
4140:
4134:
4127:
4123:
4120:
4114:
4107:
4103:
4100:
4094:
4087:
4083:
4077:
4070:
4066:
4060:
4052:
4048:
4044:
4040:
4036:
4032:
4028:
4024:
4020:
4013:
4006:
4002:
3996:
3988:
3984:
3979:
3974:
3970:
3966:
3962:
3954:
3940:
3939:
3932:
3918:
3912:
3904:
3898:
3894:
3890:
3886:
3882:
3875:
3867:
3861:
3857:
3853:
3849:
3845:
3838:
3824:
3823:
3816:
3808:
3804:
3797:
3789:
3783:
3778:
3773:
3769:
3761:
3747:
3743:
3737:
3730:
3726:
3720:
3718:
3711:
3707:
3704:
3697:
3691:
3685:
3679:
3675:
3669:
3663:
3657:
3651:
3644:
3637:
3631:
3629:
3621:
3611:on 2009-11-24
3610:
3606:
3600:
3594:
3590:
3587:
3581:
3575:
3571:
3567:
3561:
3557:
3546:
3542:
3538:
3534:
3530:
3526:
3522:
3518:
3514:
3509:
3508:
3500:
3497:
3495:
3492:
3491:
3483:
3480:
3477:
3474:
3472:
3469:
3466:
3463:
3460:
3457:
3455:
3454:Business rule
3452:
3450:
3447:
3445:
3442:
3440:
3437:
3435:
3432:
3430:
3427:
3425:
3422:
3421:
3413:
3410:
3406:
3403:
3401:
3398:
3397:
3396:
3393:
3389:
3386:
3385:
3384:
3381:
3377:
3374:
3373:
3372:
3369:
3365:
3364:Build scripts
3362:
3360:
3357:
3355:
3352:
3351:
3350:
3347:
3343:
3340:
3338:
3335:
3333:
3330:
3328:
3325:
3324:
3323:
3320:
3319:
3313:
3311:
3307:
3303:
3299:
3295:
3291:
3287:
3283:
3279:
3275:
3271:
3266:
3264:
3260:
3256:
3252:
3248:
3244:
3240:
3235:
3233:
3229:
3225:
3222:
3218:
3204:multilingual
3203:
3201:
3199:
3197:
3194:
3191:
3189:
3187:
3185:
3183:
3181:
3179:
3177:
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3172:
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3158:
3155:
3152:
3149:
3146:
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2260:multilingual
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1056:MappingMaster
1055:
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1043:MappingMaster
1041:
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1012:METAmorphoses
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353:foaf:homepage
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56:
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49:, documents,
48:
44:
40:
36:
32:
19:
5026:Microformats
4965:Common Logic
4772:
4670:Applications
4586:Semantic Web
4519:
4515:
4509:
4491:
4486:
4467:
4462:
4442:
4422:
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4405:
4393:
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4187:, p. 1 - 8,
4184:
4179:
4159:
4146:
4133:
4113:
4093:
4081:
4076:
4064:
4059:
4026:
4022:
4012:
4000:
3995:
3968:
3964:
3953:
3943:, retrieved
3937:
3931:
3920:. Retrieved
3911:
3884:
3874:
3847:
3837:
3827:, retrieved
3821:
3815:
3806:
3796:
3767:
3760:
3749:. Retrieved
3745:
3736:
3724:
3696:
3684:
3668:
3656:
3643:
3619:
3613:. Retrieved
3609:the original
3599:
3580:
3560:
3520:
3516:
3267:
3254:
3250:
3247:abstractions
3236:
3227:
3223:
3214:
3044:Text-To-Onto
2273:dump, SPARQL
2008:Output Model
1968:
1944:Output Model
1853:
1840:
1832:
1828:
1805:
1796:
1774:
1766:
1762:
1759:
1751:
1744:The task of
1743:
1713:Traditional
1712:
1696:
1678:
1635:
1627:
1604:
652:
627:
611:
591:
570:
376:
372:
360:
344:
340:
336:
332:
328:
324:
261:
142:
115:
83:
30:
29:
5062:Dublin Core
4788:Library 2.0
4656:Linked data
4492:AI Magazine
4468:AI Magazine
3568:, charter:
3354:Source code
3308:focuses on
3294:data mining
3239:data mining
3232:data mining
3174:ThingFinder
3090:Plain Text
2986:proprietary
2704:proprietary
2612:proprietary
2566:proprietary
2053:proprietary
1551:TriG Syntax
1087:proprietary
994:proprietary
902:proprietary
871:Visual Tool
796:Convert2RDF
769:TSV, CoNLL
734:CSV2RDF4LOD
676:Data Source
365:foaf:Person
157:Exposition
88:(RDF) from
5142:Categories
5072:Schema.org
4808:References
4758:Geotagging
4753:Folksonomy
4646:Dataspaces
4639:Sub-topics
4615:Ontologies
4593:Background
3945:2020-06-05
3922:2020-06-05
3829:2020-06-05
3751:2020-06-05
3615:2009-11-10
3553:References
3434:Metamodels
3424:Data model
3316:Input data
3100:automatic
3086:ThatNeedle
2716:OpenCalais
2674:plain text
2624:OntoSyphon
2362:plain text
2337:IE, OL, SA
2147:plain text
2107:plain text
2065:AlchemyAPI
1569:XML to RDF
1419:SPARQL/ETL
1388:LinkedData
1149:RDF (SKOS)
1143:LinkedData
1074:ODEMapster
889:DataMaster
827:D2R Server
710:SPARQL/ETL
399:status_id
333:first_name
321:ontologies
276:LinkedData
219:OpenCalais
79:ontologies
5103:hCalendar
5021:Microdata
4918:N-Triples
4911:Notation3
4837:Standards
4813:Topic map
4651:Hyperdata
4620:Semantics
4605:Hypertext
4600:Databases
4524:CiteSeerX
3987:0950-7051
3971:: 60â85.
3395:Sequences
3322:Databases
3302:data sets
3251:knowledge
3221:knowledge
3144:automatic
2998:Text2Onto
2968:automatic
2952:smart FIX
2923:automatic
2895:RDF, RDFa
2877:automatic
2824:Automatic
2778:automatic
2732:automatic
2640:automatic
2594:automatic
2548:automatic
2532:OntoLearn
2464:Automatic
2407:iDocument
2374:automatic
2328:automatic
2282:automatic
2236:automatic
2195:automatic
2078:automatic
2035:automatic
1588:automatic
1412:Ultrawrap
1310:automatic
1279:automatic
1167:RDBToOnto
1140:XML, Text
765:CoNLL-RDF
722:automatic
697:Uses GUI
463:marriedTo
448:marriedTo
393:marriedTo
377:status_id
373:status_id
341:marriedTo
337:last_name
313:Ultrawrap
227:Extractiv
98:knowledge
35:knowledge
5113:hProduct
4803:Metadata
4610:Internet
4546:17904603
4499:Archived
4475:Archived
4451:Archived
4431:Archived
4341:Archived
4283:Computer
4249:Archived
4192:Archived
4168:Archived
4122:Archived
4102:Archived
4043:27045825
3706:Archived
3589:Archived
3537:27045825
3488:See also
3439:Ontology
3429:Metadata
3349:Software
3332:Database
3310:metadata
3228:deriving
3122:English
2796:RDF, OWL
2664:English
2618:English
2572:English
2306:English
1999:Approach
1996:Uses GUI
1916:Approach
1908:Uses GUI
1781:concepts
1777:ontology
1381:Triplify
924:CSV, XML
858:DartGrid
809:RDF/DAML
618:ER model
493:homepage
396:homepage
345:homepage
317:Virtuoso
309:Triplify
267:Congress
204:Examples
112:Overview
106:Freebase
5123:hReview
5118:hRecipe
4890:JSON-LD
4883:RDF/XML
4876:triples
4818:Web 2.0
4051:2795344
3545:2795344
3080:German
2861:SCOOBIE
2494:OntoGen
2395:RDF/OWL
1835:DBpedia
1517:dynamic
1507:VisAVis
1486:dynamic
1455:dynamic
1422:dynamic
1391:dynamic
1198:RDF 123
1146:dynamic
981:MAPONTO
950:Krextor
868:dynamic
840:D2R Map
775:static
713:dynamic
655:XML2RDF
553:Teacher
535:Student
351:called
294:(using
286:(using
243:DBpedia
149:Source
102:DBpedia
5036:SAWSDL
4939:SPARQL
4897:Turtle
4544:
4526:
4049:
4041:
3985:
3899:
3862:
3784:
3543:
3535:
3467:(BPMN)
3383:Graphs
2907:SemTag
2808:Rosoka
2414:SPARQL
1978:Source
1860:Source
1594:false
1579:static
1563:false
1557:manual
1548:static
1526:manual
1501:false
1483:SPARQL
1452:SPARQL
1406:false
1400:manual
1360:static
1339:manual
1316:false
1301:static
1285:false
1270:static
1248:manual
1239:static
1217:manual
1208:static
1177:static
1161:false
1115:static
1093:manual
1084:static
1053:static
1031:manual
1022:static
1006:false
1000:manual
991:static
975:false
969:manual
960:static
930:static
908:manual
899:static
877:manual
852:false
846:manual
834:SPARQL
815:manual
806:static
790:false
787:false
759:false
753:manual
744:static
728:false
517:Person
315:, and
284:Person
65:) and
51:images
5108:hCard
5098:hAtom
5016:GRDDL
4995:SHACL
4768:iXBRL
4727:Solid
4542:S2CID
4047:S2CID
3541:S2CID
3478:(RDF)
3461:(KDM)
3224:about
3116:JSON
3093:dump
2670:ontoX
2143:ASIUM
2103:ANNIE
1850:Tools
1725:(NER)
1591:false
1585:false
1582:false
1560:false
1532:true
1498:false
1470:true
1467:false
1439:true
1436:false
1426:R2RML
1403:false
1375:true
1372:false
1366:false
1342:false
1313:false
1307:false
1304:false
1282:false
1276:false
1254:true
1229:RDOTE
1223:true
1220:false
1214:false
1211:false
1192:true
1189:false
1183:false
1130:true
1127:false
1099:true
1068:true
1065:false
1037:true
1034:false
944:true
941:false
914:true
883:true
880:false
849:false
821:true
818:false
781:true
778:none
756:false
725:false
719:false
544:Claus
526:Peter
505:Peter
484:Peter
442:Peter
420:Claus
404:Peter
325:users
100:(see
5082:SKOS
5077:SIOC
5067:FOAF
5057:DOAP
5031:RDFa
5011:eRDF
4990:ALPS
4975:RDFS
4934:RRID
4925:TriX
4904:TriG
4851:HTTP
4039:PMID
3983:ISSN
3897:ISBN
3860:ISBN
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