@prefix derw: <https://derwen.ai/ns/v1#> .

@prefix bibo: <http://purl.org/ontology/bibo/> .

@prefix cito: <http://purl.org/spar/cito/> .

@prefix dct: <http://purl.org/dc/terms/> .

@prefix foaf: <http://xmlns.com/foaf/0.1/> .

@prefix lcsh: <http://id.loc.gov/authorities/subjects/> .

@prefix madsrdf: <http://www.loc.gov/mads/rdf/v1#> .

@prefix owl: <http://www.w3.org/2002/07/owl#> .

@prefix rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#> .

@prefix rdfs: <http://www.w3.org/2000/01/rdf-schema#> .

@prefix skos: <http://www.w3.org/2004/02/skos/core#> .

@prefix wd: <http://www.wikidata.org/entity/> .

@prefix xsd: <http://www.w3.org/2001/XMLSchema#> .

derw:citeKey rdfs:domain bibo:Document ;

rdfs:range xsd:string ;

skos:definition "bibliographic citekey for team use in related publications"@en ;

.

derw:openAccess rdfs:domain bibo:Document ;

rdfs:range dct:identifier ;

skos:definition "open access URL for a cited document"@en ;

.

a derw:Author ;

foaf:name "David Gleich"@en ;

dct:identifier <https://orcid.org/0000-0002-8107-6474>

.

a derw:Author ;

foaf:name "Mike Williams"@en ;

dct:identifier <https://scholar.google.com/citations?user=MZ648iUAAAAJ>

.

a derw:Author ;

foaf:name "Cornelia Caragea"@en ;

dct:identifier <https://dblp.org/pid/69/6680.html>

.

a derw:Author ;

foaf:name "Corina Florescu"@en ;

dct:identifier <https://dblp.org/pid/195/8141.html>

.

a derw:Author ;

foaf:name "Ashkan Kazemi"@en ;

dct:identifier <https://dblp.org/pid/277/9400.html>

.

a derw:Author ;

foaf:name "Verónica Pérez-Rosas"@en ;

dct:identifier <https://dblp.org/pid/53/9684.html>

.

a derw:Author ;

foaf:name "Rada Mihalcea"@en ;

dct:identifier <https://dblp.org/pid/m/RadaMihalcea.html>

.

a derw:Author ;

foaf:name "Paul Tarau"@en ;

dct:identifier <https://dblp.org/pid/t/PaulTarau.html>

.

a derw:Author ;

foaf:name "Lawrence Page"@en ;

dct:identifier <https://dblp.org/pid/p/LawrencePage.html>

.

a derw:Author ;

foaf:name "Sergey Brin"@en ;

dct:identifier <https://dblp.org/pid/b/SergeyBrin.html>

.

a derw:Author ;

foaf:name "Rajeev Motwani"@en ;

dct:identifier <https://dblp.org/pid/m/RajeevMotwani.html>

.

a derw:Author ;

foaf:name "Terry Winograd"@en ;

dct:identifier <https://dblp.org/pid/w/TerryWinograd.html>

.

a bibo:Journal ;

bibo:shortTitle "Comput Linguist Assoc Comput Linguis"@en ;

dct:title "Computational Linguistics"@en ;

dct:identifier <https://www.mitpressjournals.org/loi/coli>

.

a bibo:Journal ;

bibo:shortTitle "SIAM Review"@en ;

dct:title "Society for Industrial and Applied Mathematics"@en ;

dct:identifier <https://www.siam.org/publications/journals/siam-review-sirev>

.

a bibo:Proceedings ;

bibo:shortTitle "EMNLP"@en ;

dct:title "Proceedings of the 2004 Conference on Empirical Methods in Natural Language Processing"@en ;

dct:Date "2004" ;

dct:identifier <https://www.aclweb.org/anthology/venues/emnlp/> ;

dct:publisher <https://www.aclweb.org/portal/>

.

a bibo:Journal ;

bibo:shortTitle "COLING"@en ;

dct:title "Proceedings - International Conference on Computational Linguistics"@en ;

dct:identifier <https://www.aclweb.org/anthology/venues/coling/>

.

a bibo:Collection ;

bibo:shortTitle "Stanford InfoLab"@en ;

dct:title "Stanford InfoLab"@en ;

dct:identifier <http://infolab.stanford.edu/>

.

a bibo:Article ;

derw:citeKey "florescuc17"@en ;

derw:openAccess <https://www.aclweb.org/anthology/P17-1102.pdf> ;

dct:title "PositionRank: An Unsupervised Approach to Keyphrase Extraction from Scholarly Documents"@en ;

dct:isPartOf <urn:issn:0891-2017> ;

dct:language "en" ;

dct:Date "2017-07-30" ;

bibo:pageStart "1105" ;

bibo:pageEnd "1115" ;

bibo:authorList (

<https://derwen.ai/s/y3w6mvj2r9wv>

<https://derwen.ai/s/v3rq24nf6426>

) ;

bibo:doi "10.18653/v1/P17-1102" ;

bibo:abstract "The large and growing amounts of online scholarly data present both challenges and opportunities to enhance knowledge discovery. One such challenge is to automatically extract a small set of keyphrases from a document that can accurately describe the document’s content and can facilitate fast information processing. In this paper, we propose PositionRank, an unsupervised model for keyphrase extraction from scholarly documents that incorporates information from all positions of a word’s occurrences into a biased PageRank. Our model obtains remarkable improvements in performance over PageRank models that do not take into account word positions as well as over strong baselines for this task. Specifically, on several datasets of research papers, PositionRank achieves improvements as high as 29.09%."@en

.

a bibo:Article ;

derw:citeKey "gleich15"@en ;

derw:openAccess <https://www.cs.purdue.edu/homes/dgleich/publications/Gleich%202015%20-%20prbeyond.pdf> ;

dct:title "PageRank Beyond the Web"@en ;

dct:language "en" ;

dct:Date "2015-08-06" ;

dct:isPartOf <urn:issn:0036-1445> ;

bibo:volume "57" ;

bibo:issue "3" ;

bibo:pageStart "321" ;

bibo:pageEnd "363" ;

bibo:authorList (

<https://derwen.ai/s/7zk738z8fn9t>

) ;

bibo:doi "10.1137/140976649" ;

bibo:abstract "Google's PageRank method was developed to evaluate the importance of web-pages via their link structure. The mathematics of PageRank, however, are entirely general and apply to any graph or network in any domain. Thus, PageRank is now regularly used in bibliometrics, social and information network analysis, and for link prediction and recommendation. It's even used for systems analysis of road networks, as well as biology, chemistry, neuroscience, and physics. We'll see the mathematics and ideas that unite these diverse applications."@en

.

a bibo:Article ;

derw:citeKey "kazemi-etal-2020-biased"@en ;

derw:openAccess <https://www.aclweb.org/anthology/2020.coling-main.144.pdf> ;

dct:title "Biased TextRank: Unsupervised Graph-Based Content Extraction"@en ;

dct:language "en" ;

dct:Date "2020-12-08" ;

dct:isPartOf <urn:issn:1525-2477> ;

bibo:volume "28" ;

bibo:pageStart "1642" ;

bibo:pageEnd "1652" ;

bibo:authorList (

<https://derwen.ai/s/rjsnrs5jhswk>

<https://derwen.ai/s/svmndvvnndkv>

<https://derwen.ai/s/wwrw59tbtzzp>

) ;

bibo:doi "10.18653/v1/2020.coling-main.144" ;

bibo:abstract "We introduce Biased TextRank, a graph-based content extraction method inspired by the popular TextRank algorithm that ranks text spans according to their importance for language processing tasks and according to their relevance to an input 'focus'. Biased TextRank enables focused content extraction for text by modifying the random restarts in the execution of TextRank. The random restart probabilities are assigned based on the relevance of the graph nodes to the focus of the task. We present two applications of Biased TextRank: focused summarization and explanation extraction, and show that our algorithm leads to improved performance on two different datasets by significant ROUGE-N score margins. Much like its predecessor, Biased TextRank is unsupervised, easy to implement and orders of magnitude faster and lighter than current state-of-the-art Natural Language Processing methods for similar tasks."@en

.

a bibo:Article ;

derw:citeKey "page1998"@en ;

derw:openAccess <http://ilpubs.stanford.edu:8090/422/1/1999-66.pdf> ;

dct:title "The PageRank Citation Ranking: Bringing Order to the Web"@en ;

dct:language "en" ;

dct:Date "1999-11-11" ;

dct:isPartOf <http://ilpubs.stanford.edu:8090/> ;

bibo:authorList (

<https://derwen.ai/s/mk6xj6cfrrxg>

<https://derwen.ai/s/j636dghdyws5>

<https://derwen.ai/s/9hhpmgjs7kwt>

<https://derwen.ai/s/jdxk7fz84nzq>

) ;

bibo:abstract "The importance of a Web page is an inherently subjective matter, which depends on the readers interests, knowledge and attitudes. But there is still much that can be said objectively about the relative importance of Web pages. This paper describes PageRank, a method for rating Web pages objectively and mechanically, effectively measuring the human interest and attention devoted to them. We compare PageRank to an idealized random Web surfer. We show how to efficiently compute PageRank for large numbers of pages. And, we show how to apply PageRank to search and to user navigation."@en

.

a bibo:Article ;

derw:citeKey "mihalcea04textrank"@en ;

derw:openAccess <https://web.eecs.umich.edu/~mihalcea/papers/mihalcea.emnlp04.pdf> ;

dct:title "TextRank: Bringing Order into Text"@en ;

dct:language "en" ;

dct:Date "2004-07-25" ;

dct:isPartOf <https://www.aclweb.org/anthology/venues/emnlp/> ;

bibo:pageStart "404" ;

bibo:pageEnd "411" ;

bibo:authorList (

<https://derwen.ai/s/wwrw59tbtzzp>

<https://derwen.ai/s/vnfvsgvc9gfy>

) ;

bibo:abstract "In this paper, the authors introduce TextRank, a graph-based ranking model for text processing, and show how this model can be successfully used in natural language applications."@en

.

a bibo:Slideshow ;

derw:citeKey "williams2016"@en ;

dct:title "Summarizing documents"@en ;

dct:language "en" ;

dct:Date "2016-09-25" ;

bibo:presentedAt <https://2016.pygotham.org/> ;

bibo:authorList (

<https://derwen.ai/s/2t2mbms2x4p3>

) ;

bibo:abstract "I've recently given a couple of talks (PyGotham video, PyGotham slides, Strata NYC slides) about text summarization. I cover three ways of automatically summarizing text. One is an extremely simple algorithm from the 1950s, one uses Latent Dirichlet Allocation, and one uses skipthoughts and recurrent neural networks. The talk is conceptual, and avoids code and mathematics. So here is a list of resources if you're interested in text summarization and want to dive deeper. This list useful is hopefully also useful if you're interested in topic modelling or neural networks for other reasons."@en

.

derw:Topic a skos:Concept ,

madsrdf:Topic ,

madsrdf:Authority ;

skos:prefLabel "Topic"@en ;

dct:identifier wd:Q1969448 ;

skos:definition "Subject heading used for classifying content and navigating discovery within it."@en ;

.

derw:topic_Abstractive_Summarization

a derw:Topic ;

skos:prefLabel "abstractive summarization"@en ;

skos:closeMatch <https://paperswithcode.com/task/abstractive-text-summarization> ;

skos:broader derw:topic_Summarization ;

skos:definition "Generating a short, concise summary which captures salient ideas of the source text, potentially using new phrases and sentences that may not appear in the source."@en

.

derw:topic_Cloud_Computing a derw:Topic ;

skos:prefLabel "cloud computing"@en ;

skos:altLabel "cloud"@en ;

skos:closeMatch wd:Q483639 ,

lcsh:sh2008004883 ,

<https://csrc.nist.gov/publications/detail/sp/800-145/final> ,

<https://rise.cs.berkeley.edu/blog/a-berkeley-view-on-serverless-computing/> ;

skos:broader <https://derwen.ai/d/computer_science> ;

skos:definition "The on-demand availability of computing resources over the Internet without direct active management by the user, often paid for on a short-term basis, giving the illusion of infinite computing resources available and thereby eliminating the need to plan far ahead for provisioning."@en

.

derw:topic_Computable_Content a derw:Topic ;

skos:prefLabel "computable content"@en ;

skos:closeMatch <https://www.slideshare.net/pacoid/computable-content> ,

<https://youtu.be/EPftGvsXonc> ,

<https://www.wolfram.com/cdf/> ;

skos:broader <https://derwen.ai/d/computer_science> ;

skos:definition "Interactive learning materials which leverage remote computation, e.g., Jupyter notebooks."@en

.

derw:topic_Coreference_Resolution

a derw:Topic ;

skos:prefLabel "coreference resolution"@en ;

skos:closeMatch wd:Q63087 ,

<https://paperswithcode.com/task/coreference-resolution> ,

<http://nlpprogress.com/english/coreference_resolution.html> ;

skos:broader derw:topic_Natural_Language ;

skos:definition "Clustering mentions within a text that refer to the same underlying entities."@en

.

derw:topic_Data_Science a derw:Topic ;

skos:prefLabel "data science"@en ;

skos:closeMatch wd:Q2374463 ,

<https://projecteuclid.org/euclid.aoms/1177704711> ,

<https://ischoolonline.berkeley.edu/data-science/what-is-data-science/> ,

<https://hbr.org/2018/11/curiosity-driven-data-science> ,

<https://community.ibm.com/community/user/datascience/blogs/paco-nathan/2019/03/04/what-is-data-science> ;

skos:broader <https://derwen.ai/d/artificial_intelligence> ;

skos:definition "An interdisciplinary field which emerged from industry not academia, focused on deriving insights from data, emphasizing how to leverage curiosity and domain expertise, and applying increasingly advanced mathematics for novel business cases in response to surges in data rates and compute resources."@en

.

derw:topic_Data_Strategy a derw:Topic ;

skos:prefLabel "data strategy"@en ;

skos:broader <https://derwen.ai/d/computer_science> ;

skos:definition "The tools, processes, and practices that define how to manage and leverage data to make informed decisions."@en

.

derw:topic_Deep_Learning

a derw:Topic ;

skos:prefLabel "deep learning"@en ;

skos:altLabel "DL"@en ;

skos:closeMatch wd:Q197536 ,

<https://en.wikipedia.org/wiki/Deep_learning> ;

skos:broader <https://derwen.ai/d/machine_learning> ;

skos:definition "A family of machine learning methods based on artificial neural networks which use representation learning."@en

.

derw:topic_Eigenvector_Centrality

a derw:Topic ;

skos:prefLabel "eigenvector centrality"@en ;

skos:closeMatch wd:Q28401090 ,

<https://demonstrations.wolfram.com/NetworkCentralityUsingEigenvectors/> ;

skos:broader derw:topic_Graph_Algorithms ;

skos:definition "Measuring the influence of a node within a network."@en

.

derw:topic_Entity_Linking

a derw:Topic ;

skos:prefLabel "entity linking"@en ;

skos:broader derw:topic_Named_Entity_Recognition ,

derw:topic_Knowledge_Graph ;

skos:closeMatch wd:Q17012245 ,

<http://nlpprogress.com/english/entity_linking.html> ,

<https://paperswithcode.com/task/entity-linking> ;

skos:definition "Recognizing named entities within a text, then disambiguating them by linking to specific contexts in a knowledge graph."@en

.

derw:topic_Extractive_Summarization

a derw:Topic ;

skos:prefLabel "extractive summarization"@en ;

skos:closeMatch <https://paperswithcode.com/task/extractive-summarization> ;

skos:broader derw:topic_Summarization ;

skos:definition "Summarizing the source text by identifying a subset of the sentences as the most important excerpts, then generating a sequence of them verbatim."@en

.

derw:topic_Graph_Algorithms

a derw:Topic ;

skos:prefLabel "graph algorithms"@en ;

skos:closeMatch lcsh:sh2002004605 ,

<https://networkx.org/documentation/stable/reference/algorithms/index.html> ;

skos:broader <https://derwen.ai/d/computer_science> ;

skos:definition "A family of algorithms that operation on graphs for network analysis, measurement, ranking, partitioning, and other methods that leverage graph theory."@en

.

derw:topic_Knowledge_Graph

a derw:Topic ;

skos:prefLabel "knowledge graph"@en ;

skos:altLabel "KG"@en ;

skos:closeMatch wd:Q33002955 ,

<https://www.poolparty.biz/what-is-a-knowledge-graph/> ;

skos:broader <https://derwen.ai/d/artificial_intelligence> ;

skos:definition "A knowledge base that uses a graph-structured data model, representing and annotating interlinked descriptions of entities, with an overlay of semantic metadata."@en

.

derw:topic_Knowledge_Graph_Conference a derw:Topic ;

skos:prefLabel "knowledge graph conference"@en ;

skos:altLabel "KGC"@en ;

skos:closeMatch <https://www.knowledgegraph.tech/> ;

skos:broader derw:topic_Knowledge_Graph ;

skos:definition "Founded in 2019 at Columbia University, The Knowledge Graphs Conference is emerging as the premiere source of learning around knowledge graph technologies. We believe knowledge graphs are an underutilized yet essential force for solving complex societal challenges like climate change, democratizing access to knowledge and opportunity, and capturing business value made possible by the AI revolution."@en

.

derw:topic_Language_Model

a derw:Topic ;

skos:prefLabel "language model"@en ;

skos:broader derw:topic_Natural_Language ,

<https://derwen.ai/d/machine_learning> ;

skos:closeMatch wd:Q3621696 ,

<http://nlpprogress.com/english/language_modeling.html> ,

<https://paperswithcode.com/task/language-modelling> ;

skos:definition "A statistical model used for predicting the next word or character within a document."@en

.

derw:topic_Lemma_Graph

a derw:Topic ;

skos:prefLabel "lemma graph"@en ;

skos:broader derw:topic_Textgraphs ;

cito:usesMethodIn "mihalcea04textrank"@en ;

skos:definition "A graph data structure used to represent links among phrase extracted from a source text, during the operation of the TextRank algorithm."@en

.

derw:topic_Natural_Language

a derw:Topic ;

skos:prefLabel "natural language"@en ;

skos:altLabel "NLP"@en ;

skos:closeMatch wd:Q30642 ,

lcsh:sh88002425 ,

<https://plato.stanford.edu/entries/computational-linguistics/> ;

skos:broader <https://derwen.ai/d/artificial_intelligence> ;

skos:definition "Intersection of computer science and linguistics, used to leverage data in the form of text, speech, and images to identify structure and meaning. Also used for enabling people and computer-based agents to interact using natural language."@en

.

derw:topic_Named_Entity_Recognition

a derw:Topic ;

skos:prefLabel "named entity recognition"@en ;

skos:altLabel "NER"@en ;

skos:broader derw:topic_Phrase_Extraction ;

skos:closeMatch wd:Q403574 ,

<https://paperswithcode.com/task/named-entity-recognition-ner> ,

<http://nlpprogress.com/english/named_entity_recognition.html> ;

skos:definition "Extracting mentions of *named entities* from unstructured text, then annotating them with pre-defined categories."@en

.

derw:topic_Personalized_PageRank

a derw:Topic ;

skos:prefLabel "personalized pagerank"@en ;

skos:broader derw:topic_Eigenvector_Centrality ;

cito:usesMethodIn "page1998"@en , "gleich15"@en ;

skos:definition "Using the *personalized teleportation behaviors* originally described for the PageRank algorithm to focus ranked results within a neighborhood of the graph, given a set of nodes as input."@en

.

derw:topic_Phrase_Extraction

a derw:Topic ;

skos:prefLabel "phrase extraction"@en ;

skos:broader derw:topic_Natural_Language ;

skos:closeMatch wd:Q66709886 ;

skos:definition "Selecting representative phrases from a document as its characteristic entities; in contrast to *keyword* analysis."@en

.

derw:topic_Reinforcement_Learning a derw:Topic ;

skos:prefLabel "reinforcement learning"@en ;

skos:altLabel "RL"@en ;

skos:closeMatch wd:Q830687 ,

lcsh:sh92000704 ,

<https://docs.ray.io/en/master/rllib-models.html> ;

skos:broader <https://derwen.ai/d/machine_learning> ;

skos:definition "Optimal control theory mixed with deep learning where software agents learn to take actions within an environment and make sequences of decisions to maximize a cumulative reward -- typically stated in terms of markov decision process -- finding a balance between exploration (uncharted territory) and exploitation (current knowledge). Generally a reverse engineering of various psychological learning processes."@en

.

derw:topic_Semantic_Relations

a derw:Topic ;

skos:prefLabel "semantic relations"@en ;

skos:closeMatch wd:Q2268906 ;

skos:broader derw:topic_Knowledge_Graph ;

skos:definition "Associations that exist between the meanings of phrases."@en

.

derw:topic_Stop_Words

a derw:Topic ;

skos:prefLabel "stop words"@en ;

skos:broader derw:topic_Natural_Language ;

skos:closeMatch wd:Q80735 ,

lcsh:sh85046249 ;

skos:definition "Words to be filtered out during natural language processing."@en

.

derw:topic_Summarization

a derw:Topic ;

skos:prefLabel "summarization"@en ;

skos:altLabel "text summarization"@en ;

skos:closeMatch wd:Q1394144 ,

<http://nlpprogress.com/english/summarization.html> ;

skos:broader derw:topic_Natural_Language ;

skos:definition "Producing a shorter version of one or more documents, while preserving most of the input's meaning."@en

.

derw:topic_Textgraphs

a derw:Topic ;

skos:prefLabel "textgraphs"@en ;

skos:closeMatch wd:Q18388823 ,

<http://www.gabormelli.com/RKB/Text_Graph> ,

<http://www.textgraphs.org/> ;

skos:broader derw:topic_Natural_Language ,

derw:topic_Graph_Algorithms ;

skos:definition "Use of graph algorithms for NLP, based on a graph representation of a source text."@en

.

derw:topic_Transformers

a derw:Topic ;

skos:prefLabel "transformers"@en ;

skos:broader derw:topic_Language_Model ,

derw:topic_Deep_Learning ;

skos:closeMatch wd:Q85810444 ,

<https://paperswithcode.com/methods/category/transformers> ;

skos:definition "A family of deep learning models, mostly used in NLP, which adopts the mechanism of *attention* to weigh the influence of different parts of the input data."@en

.