NELL (Never-Ending Language Learning)

Notes:

NELL (Never-Ending Language Learning) is a computer system that uses machine learning algorithms to continuously improve its ability to understand and generate natural language. NELL was developed by researchers at Carnegie Mellon University, and it is designed to learn from a variety of sources, including the internet, books, and other written texts. As it learns, it builds a knowledge base of information about the world, and it uses this knowledge to generate responses to questions and to understand the meaning of natural language text.

NELL is an example of a large-scale language learning system, and it is designed to be able to learn and improve over time without the need for human intervention. This makes it an interesting and powerful tool for studying the potential capabilities of machine learning algorithms for natural language processing. However, because NELL is constantly learning from new data, the accuracy and reliability of its responses can vary, and it is not always able to provide accurate or complete answers to questions.

Resources:

Wikipedia:

References:

See also:

Open Information Extraction | OpenCalais 2018

Never-ending learning
T Mitchell, W Cohen, E Hruschka, P Talukdar… – Communications of the …, 2018 – dl.acm.org
… comments, and thank Lucas Navarro, Bill McDowell, Oscar Romero and Amos Azaria for help in the empirical evaluation of NELL … 8. Carlson, A., Betteridge, J., Kisiel, B., Settles, B., Hruschka Jr, ER, Mitchell, TM Toward an architecture for never-ending language learning …

Never-ending learning for open-domain question answering over knowledge bases
A Abujabal, R Saha Roy, M Yahya… – Proceedings of the 2018 …, 2018 – dl.acm.org
Page 1. Never-Ending Learning for Open-Domain Question Answering over Knowledge Bases Abdalghani Abujabal Max Planck Institute for Informatics Saarland Informatics Campus, Germany abujabal@mpi-inf.mpg.de Rishiraj …

Training neural tensor networks with the never ending language learner
FAO Santos, FB do Nascimento, MS Santos… – Information Technology …, 2018 – Springer
… Our model has achieved significant accuracy given the limited number of tuples per relationship in NELL’s KB. Keywords. Neural Tensor Network Never Ending Language Learning Knowledge base. Download conference paper PDF. 4.1 Introduction …

Computer-assisted Ontology Construction System: Focus on Bootstrapping Capabilities
O Qawasmeh, M Lefranois, A Zimmermann… – European Semantic Web …, 2018 – Springer
… an original approach for ontology bootstrapping based on the use of three external knowledge bases: DBpedia, WikiData, an NELL … 5. Carlson, A., Betteridge, J., Kisiel, B., Settles, B., Hruschka Jr., RH, Mitchell, TM: Toward an architecture for never-ending language learning …

NELL2RDF: Reading the Web, Tracking the Provenance, and Publishing it as Linked Data
JM Giménez-Garc?a, M Duarte, A Zimmermann… – 2018 – ceur-ws.org
… Never-Ending Language Learning (NELL) [2] is an autonomous computational system with the aim of learning continually and incrementally. It generates a knowledge base where beliefs are learned from the Web using an ontology previously created to guide the learning …

From One-off Machine Learning to Perpetual Learning: A STEP Perspective
D Zhang – 2018 IEEE International Conference on Systems …, 2018 – ieeexplore.ieee.org
… semantic relations. NELL has grown to be an ambitious “Read the Web” project [42]. NEIL … CA, 2004. [9] Carlson, et al., Toward an architecture for never-ending language learning. Proc. of AAAI, Atlanta, Georgia, July, 2010. [10 …

A Knowledge Base Completion Model Based on Path Feature Learning.
X Lin, Y Liang, L Wang, X Wang… – International Journal …, 2018 – search.ebscohost.com
… Large-scale knowledge bases (KBs), such as Never-Ending Language Learning (NELL) [6], Freebase [3], Yago [35], and DBpedia [11], construct their own ontologies derived from facts that … We use the Never-Ending Language Learning (NELL) dataset to evaluate our model …

RICH-CPL: Fact Extraction from Wikipedia-sized Corpora for Morphologically Rich Languages
S Budkov, K Buraya, A Filchenkov… – Proceedings of the 23rd …, 2018 – dl.acm.org
… The first prototype implementation of the NEL approach was done for English and was called NELL (Never-ending language learning) [5]. Since the system showed good results an attempt was made to extend the never-ending learning approach to Portuguese [6]. Experiments …

Improved categorization of computer-assisted ontology construction systems: focus on bootstrapping capabilities
O Qawasmeh, M Lefrançois… – Extended semantic …, 2018 – hal-emse.ccsd.cnrs.fr
… Our functionality takes advantage of three large public knowledge bases: a) DBpedia [9], b) Wikidata [10] and c) NELL ( Never Ending Language Learner) [11]. We report on the evaluation of our functionality compared with other approaches, using the ontology for wine …

Human and Machine Cultures of Reading: A Cognitive-Assemblage Approach
NK Hayles – PMLA, 2018 – MLA
… devices with increasingly sophisticated cognitive capabilities, culminating in computational media that can read texts, draw inferences, interpret ambiguous information, make connections between texts, and reach conclusions (“NELL: Never- Ending Language Learning”) …

MDL-based Development of Ensembles with Active Learning over Evolving Data Streams
S Khoshrou, M Pechenizkiy – 2018 – milets18.github.io
… a cumulative nature”. The Never-Ending Language Learning (NELL) [5] research project has been the inspiration of numerous researches to address the never-ending learning problem [2, 8, 16, 18]. Obviously, the techniques …

Eliminating Redundant and Irrelevant Association Rules in Large Knowledge Bases.
RGL Miani, ERH Junior – ICEIS (1), 2018 – scitepress.org
… time it finds nba, contributing to increasing the KB and decrease the amount of missing values. NELL (Never-Ending Language Learning) is a computer system that runs 24 hours per day, 7 days per week, extracting information from web text to populate and extend its own KB …

NELL2RDF: Reading the Web, and Publishing it as Linked Data
JM Giménez-García, M Duarte, A Zimmermann… – arXiv preprint arXiv …, 2018 – arxiv.org
… 1 Introduction Never-Ending Language Learning (NELL) [3, 16] is an autonomous computa- tional system that aims at continually and incrementally learning. NELL has been running for about 7 years in Carnegie Mellon University (US) …

Zero-shot recognition via semantic embeddings and knowledge graphs
X Wang, Y Ye, A Gupta – Proceedings of the IEEE …, 2018 – openaccess.thecvf.com
… The dataset consists of relationships and graph from Never-Ending Language Learning (NELL) [3] and images from Never-Ending Image Learning (NEIL) [8]. This is an ideal dataset for: (a) demonstrating that our ap- proach is robust even with automatically learned (and noisy …

Machine Common Sense Concept Paper
D Gunning – arXiv preprint arXiv:1810.07528, 2018 – arxiv.org
… The most notable and comprehensive example that combines machine leaning with crowdsourcing is Tom Mitchell’s Never Ending Language Learning (NELL) system [23][24]. NELL has been learning to read the Web 24 hours a day since January 2010 …

A Multi-Hop Link Prediction Approach Based on Reinforcement Learning in Knowledge Graphs
H Chen, G Li, Y Sun, W Jiang… – 2018 11th International …, 2018 – ieeexplore.ieee.org
… believe the potential reason is that the FB15K-237 dataset has more long paths than NELL-995 … [5] Carlson, A., Betteridge, J., Kisiel, B., Settles, B., Hruschka Jr, ER, & Mitchell, TM (2010, July). Toward an architecture for never-ending language learning. In AAAI (Vol. 5, p. 3). 168 …

Principles for Developing a Knowledge Graph of Interlinked Events from News Headlines on Twitter
S Shekarpour, A Saxena, K Thirunarayan… – arXiv preprint arXiv …, 2018 – arxiv.org
… For exam- ple, for the relation plays holds between an athlete and his/her favorite sport, and NELL2 extracts the triple seve ballesteros plays golf for two entities seve ballesteros and golf, and (ii) others that (eg, [13, 12]) utilize the pattern (e1, ?p, e2) to leverage the entities …

One-shot relational learning for knowledge graphs
W Xiong, M Yu, S Chang, X Guo, WY Wang – arXiv preprint arXiv …, 2018 – arxiv.org
… Entitycount Entity degree distribution on NELL and Wikidata 0 50 100 150 200 Number of neighbors 0 5000 10000 15000 20000 25000 30000 35000 … Note that all triples in Tmeta?train, Tmeta?validation or Tmeta?test are removed from G . Upper: NELL; Lower: Wikidata …

Contronymy and Semantic Primes
P Dziedziul – Crossroads. A Journal of English Studies, 2018 – ceeol.com
… mantics, as a coherent and non-contronymous system that is not based on antilogy. To demonstrate the significance such entities like contronyms one may point to the project NELL (Never-Ending Language Learning system). This semantic machine learning system, de …

Extending neural generative conversational model using external knowledge sources
P Parthasarathi, J Pineau – arXiv preprint arXiv:1809.05524, 2018 – arxiv.org
… Model PPL BLEU-4 Vanilla Seq2Seq 38.09 0.437 Ext-ED – L3 (Wiki) 38.37 0.435 Ext-ED – L3 Ablation 37.06 0.425 Ext-ED (Wiki) 30.26 0.53 Ext-ED (NELL KB) 29.07 0.525 Ext-ED Ablation 601.8 0.274 … 2010. Toward an architecture for never-ending language learning. In AAAI …

A Collaborative Framework for Ontology and Instance Data Co-evolution and Extraction.
O Qawasmeh – EKAW (Doctoral Consortium), 2018 – researchgate.net
… ontology bootstrapping based on the use of three external knowledge bases: DB- pedia, WikiData, an NELL … Informasi 10(2), 59–66 (2017) 7. Carlson, A., Betteridge, J., Kisiel, B., Settles, B., Jr., ERH, Mitchell, TM: To- ward an architecture for never-ending language learning …

Ontology Summit 2017 communiqué–AI, learning, reasoning and ontologies
K Baclawski, M Bennett, G Berg-Cross… – Applied …, 2018 – content.iospress.com
… training examples. Proper seeding of these, however, may be needed, as was the case with the Never-Ending Language Learning (NELL), along with cumulative use of knowledge to support learning to learn (Hruschka, 2017) …

Symbolic Artificial Intelligence
ND Rodríguez – 2018 – perso.telecom-paristech.fr
… reasoning that typify real-world problems6. Later: NELL (Never Ending Language Learning, 2010) [12],… 6MYCIN’s aim: give advice regarding antimicrobial selection, making it acceptable to physicians. 3 goals: ability to 1) give …

Joint Posterior Revision of NLP Annotations via Ontological Knowledge.
M Rospocher, F Corcoglioniti – IJCAI, 2018 – dkm-static.fbk.eu
… triples. A notable example is NELL (Never-Ending Language Learning) [Mitchell et al., 2015], where the strict constraints defined in NELL’s ontology (eg, a person cannot be a city) are used to filter the extracted triples. Other …

Towards Knowledge Graph Construction from Entity Co-occurrence.
N Heist – EKAW (Doctoral Consortium), 2018 – people.kmi.open.ac.uk
… The never-ending language learner NELL [3] cyclically crawls a corpus of one billion web pages to continuously learn new facts … 2004) 3. Carlson, A., Betteridge, J., Kisiel, B., Settles, B., Hruschka Jr, ER, Mitchell, TM: Toward an architecture for never-ending language learning …

Dual graph convolutional networks for graph-based semi-supervised classification
C Zhuang, Q Ma – Proceedings of the 2018 World Wide Web Conference, 2018 – dl.acm.org
… consistency. Finally, we introduce a regularizer for the ensemble. 1If the edges have attributes, we can add additional edge nodes for encoding. For detailed pre-processing, please refer to the NELL dataset in our experiments. 3.2 …

ReinforceWalk: Learning to walk in graph with Monte Carlo tree search
Y Shen, J Chen, PS Huang, Y Guo, J Gao – 2018 – openreview.net
… Table 1: NELL-995 Link Prediction Performance Comparison using MAP scores. Tasks RW PG A2C MINERVAa DeepPath PRA TransE TransR athletePlaysForTeam 0.831 0.769 0.700 0.630 0.750 0.547 0.627 0.673 … Toward an architecture for never-ending language learning …

Tractable reasoning in probabilistic OWL profiles
MW Chekol, H Stuckenschmidt – Proceedings of the 33rd Annual ACM …, 2018 – dl.acm.org
… This situation is common for present-day KBs that are often obtained by crawling and ex- tracting open text, for instance, Never Ending Language Learning (NELL [21]), Google’s Knowledge Vault [9]. The advance of open information extraction has lead to the creation of several …

Construction of a Multi-dimensional Vectorized Affective Lexicon
Y Wang, C Feng, Q Liu – … on Natural Language Processing and Chinese …, 2018 – Springer
… (i) The ones only containing affective words, such as the Never-Ending Language Learner (NELL) [5]. They … 10 (2010)Google Scholar. 5. Carlson, A., Betteridge, J., Kisiel, B., Settles, B., Hruschka Jr, ER, Mitchell, TM: Toward an architecture for never-ending language learning …

Information extraction and knowledge graph construction from geoscience literature
C Wang, X Ma, J Chen, J Chen – Computers & geosciences, 2018 – Elsevier
… Furthermore, some researchers try to extract the structured information from the unstructured web to build the knowledge base, such as DBPedia, OpenIE (Open information extraction) and NELL (Never-ending language learning), and transfer the text information into a network …

Towards probabilistic bitemporal knowledge graphs
MW Chekol, H Stuckenschmidt – … of the The Web Conference 2018, 2018 – dl.acm.org
… ABSTRACT The emergence of open information extraction as a tool for con- structing and expanding knowledge graphs has aided the growth of temporal data, for instance, YAGO, NELL and Wikidata. While YAGO and Wikidata …

Towards Building a Knowledge Graph with Open Data–A Roadmap
A Ojo – e-Infrastructure and e-Services for Developing …, 2018 – books.google.com
… source for example, some of the knowledge graph systems surf the internet to extract information from unstructured data sources, example of such systems include KV, NELL and PROSPERA … ER, Mitchell, TM: Toward an architecture for never-ending language learning …

Approximate Counting for Fast Inference and Learning in Probabilistic Programming
M Das, DS Dhami, G Kunapuli, K Kersting, S Natarajan – utdallas.edu
… NELL-Sports MACH 0.78 0.65 253.92 FACT 0.76 0.64 238.07 MLN-Boost 0.78 0.66 396.24 Table 1. Results: Performance (AUC) vs. Efficiency (Learning time in seconds) … 2010. Toward an architecture for never-ending language learning. In AAAI …

I Know What You Don’t Know: Proactive Learning through Targeted Human Interaction
A Bourai, J Carbonell – Proceedings of the 17th International Conference …, 2018 – dl.acm.org
… Learning agents such as the Never Ending Language Learn- ing (NELL) system would be able to utilize human interaction for knowledge acquisition as well as textual data on the internet [6]. We propose the … 2010. Toward an Architecture for Never-Ending Language Learning …

SmartCalendar: improving scheduling through overcoming temporal inconsistencies
T JiaHua, D Zhang – 2018 IEEE 17th International Conference …, 2018 – ieeexplore.ieee.org
Page 1. SmartCalendar: Improving Scheduling through Overcoming Temporal Inconsistencies Tang JiaHua Faculty of Information Technology Macau University of Science and Technology Macau, China henson.j.tang@gmail.com …

The interplay between lexical resources and Natural Language Processing
J Camacho-Collados, L Espinosa-Anke… – arXiv preprint arXiv …, 2018 – arxiv.org
… 4. Information extraction. Recent ap- proaches for extracting semantic relations from text: NELL (Carlsonetal., 2010), ReVerb (Fader et al., 2011), PATTY (Nakashole et al., 2012), KB-Unify (Delli Bovi et al., 2015) … Toward an architecture for never- ending language learning …

Knowledge Extraction and Inference from Text: Shallow, Deep, and Everything in Between1
P Talukdar – malllabiisc.github.io
… 13:00-13:15 Overview and motivation 13:15-13:45 Case study: NELL 13:45-14:00 Bootstrapped Entity Extraction 14:00-15:00 Open Relation Extraction & Canonicalization 15:00-15:30 Coffee Break 15:30-16:15 Distantly-supervised Neural Relation Extraction 16:15-16:45 …

Knowledge Based Machine Reading Comprehension
Y Sun, D Guo, D Tang, N Duan, Z Yan, X Feng… – arXiv preprint arXiv …, 2018 – arxiv.org
… We implement a framework consisting of both a question answering model and a question generation model, both of which take the knowledge extracted from the document as well as relevant facts from an external knowl- edge base such as Freebase/ProBase/Reverb/NELL …

Estimating rule quality for knowledge base completion with the relationship between coverage assumption
K Zupanc, J Davis – Proceedings of the 2018 World Wide Web …, 2018 – dl.acm.org
… 1 INTRODUCTION Knowledge bases (KBs) store structured relational data such as “Aaron Rodgers plays for the Green Bay Packers” and “the Green Bay Packers are a football team.” Some of the most prominent KBs are YAGO [22], Wikidata,1 DBpedia [1], NELL [5], Freebase …

Open information extraction with meta-pattern discovery in biomedical literature
X Wang, Y Zhang, Q Li, Y Chen, J Han – Proceedings of the 2018 ACM …, 2018 – dl.acm.org
… extraction [18]. Mitchell et al. [27] introduce Never-Ending Language Learning (NELL) based on free-text predi- cate patterns. Patty [29] aims to extract a set of typed lexical patterns along the shortest dependency path. MetaPAD [20 …

Eliminating Temporal Conflicts in Uncertain Temporal Knowledge Graphs
L Lu, J Fang, P Zhao, J Xu, H Yin, L Zhao – International Conference on …, 2018 – Springer
… KGs as DBpedia [3], Wikidata, YAGO [20], Google’s Knowledge Graph, NELL (Never Ending Language Learning) [13] are automatically constructed by Open Information Extraction (OIE) which will extract a significant amount of incorrect, incomplete or even inconsistent factual …

A Design of IoT Based Contextual Adaptation Management System
H Kang, SH Kwon, J Yu, WS You – … International Conference on …, 2018 – ieeexplore.ieee.org
… To solve these problems, various open knowledge bases such as DBpedia [16], Freebase [17], NELL [18], and YAGO [19] are appeared … AcM, 2008. [18] Carlson, Andrew, et al, “Toward an Architecture for Never-Ending Language Learning”, AAAI. Vol. 5. 2010 …

Learning Contextual Knowledge Structures from the Web for Facilitating Semantic Interpretation of Tweets
N Javed, BL Muralidhara – … on Recent Advancement on Computer and …, 2018 – Springer
… There are state-of-the-art KBs like DeepDive [17], NELL [18], and Knowledge Vault [19] … Carlson, A., Betteridge, J., Kisiel, B., Settles, B., Hruschka, Jr., E., Mitchell, T.: Toward an architecture for never-ending language learning. In: AAAI (2010)Google Scholar. 19 …

Semi-supervised learning with declaratively specified entropy constraints
H Sun, WW Cohen, L Bing – Advances in Neural Information …, 2018 – papers.nips.cc
… relation extraction are also related to well-studied SSL methods; for instance, the rules encouraging agreement between predictions based on the type and relationship are inspired by constraints used in NELL [7, 19 … Toward an architecture for never-ending language learning …

A Survey of Truth Discovery in Information Extraction
X Wang, Y Zhang, Y Chen – pdfs.semanticscholar.org
… Carlson et al. [5] and Mitchell et al. [28] introduced Never-Ending Language Learning (NELL) based on free text predicate patterns. ReVerb [10] identified relational phrase via part-of-speech- based regular expression. Besides …

News category network based approach for news source recommendations
S Gupta, S Sodhani, D Patel… – … on Advances in …, 2018 – ieeexplore.ieee.org
… Motivated from the recent success of Never Ending System such as NELL [8], NEIL [9] and continuously updating system [7], we also create the 978-1-5386-5314-2/18/$31.00 ©2018 IEEE 133 Page 2 … Toward an architecture for never-ending language learning …

A survey on data collection for machine learning: a big data-ai integration perspective
Y Roh, G Heo, SE Whang – arXiv preprint arXiv:1811.03402, 2018 – arxiv.org
Page 1. 1 A Survey on Data Collection for Machine Learning: a Big Data – AI Integration Perspective Yuji Roh, Geon Heo, Steven Euijong Whang, Member, IEEE Abstract—Data collection is a major bottleneck in machine learning …

Translating Representations of Knowledge Graphs with Neighbors
CC Wang, PJ Cheng – The 41st International ACM SIGIR Conference on …, 2018 – dl.acm.org
… Last, NELL is a CMU project which accumulating many beliefs by reading the web. Table 1 shows the statistics of each dataset. Table 1: Data statistics … 2010. Toward an Architecture for Never-ending Language Learning. In Proceedings of AAAI. 1306–1313 …

Towards partition-aware lifted inference
MW Chekol, H Stuckenschmidt – Proceedings of the 27th ACM …, 2018 – dl.acm.org
… The results are shown in Fig. 1(b), as it can be seen, computing the probabilities on the 4-hop graph is much faster (more than 400x) than on the full graph (obtained by grounding the NELL dataset entirely) … Toward an Architecture for Never-Ending Language Learning. In AAAI …

Taxogen: Unsupervised topic taxonomy construction by adaptive term embedding and clustering
C Zhang, F Tao, X Chen, J Shen, M Jiang… – Proceedings of the 24th …, 2018 – dl.acm.org
… proposed a learning architecture for Never-Ending Language Learning (NELL) in 2010 [5]. PATTY leveraged parsing structures to derive relational patterns with se- mantic types and organizes the patterns into a taxonomy [22] …

Relational Restricted Boltzmann Machines: A Probabilistic Logic Learning Approach
W Cohen, S Natarajan – … ILP 2017, Orléans, France, September 4 …, 2018 – books.google.com
… We consider NELL data from the sports domain consisting of information about players and teams … 23–28 (2004) 5. Carlson, A., Betteridge, J., Kisiel, B., Settles, B., Hruschka Jr., ER, Mitchell, TM: Toward an architecture for never-ending language learning. In: AAAI, pp …

TaxoGen: Constructing Topical Concept Taxonomy by Adaptive Term Embedding and Clustering
C Zhang, F Tao, X Chen, J Shen, M Jiang, B Sadler… – Proc. KDDI, 2018 – meng-jiang.com
… proposed a learning architecture for Never-Ending Language Learning (NELL) in 2010 [6]. PATTY leveraged parsing structures to derive relational patterns with se- mantic types and organizes the patterns into a taxonomy [23] …

A study on big knowledge and its engineering issues
R Lu, X Jin, S Zhang, M Qiu… – IEEE Transactions on …, 2018 – ieeexplore.ieee.org
… or in form of data partition where the learning system, NELL, has over 50M candidate beliefs, among which 3.56M are of high confidence [32] while the remaining 46.44M candidate beliefs are clean data resources; or in form of data sharing where DBpedia has only 4.58M …

Bootstrapping Polar-Opposite Emotion Dimensions from Online Reviews
L Huangfu, M Surdeanu – … of the Eleventh International Conference on …, 2018 – aclweb.org
… This idea was generalized by the NELL system (Carl- son et al., 2010). McIntosh and Curran (2010) extended counter training with negative categories that are discov- ered on the fly … Toward an architec- ture for never-ending language learning. In AAAI …

Graph partition neural networks for semi-supervised classification
R Liao, M Brockschmidt, D Tarlow, AL Gaunt… – arXiv preprint arXiv …, 2018 – arxiv.org
… Next, we consider experimental results of entity clas- sification task on the NELL dataset extracted from the knowledge graph first presented in [8]. A knowledge graph consists of a set of entities and a set of directed edges which have labels (ie, different types of relation) …

CSTF: Large-Scale Sparse Tensor Factorizations on Distributed Platforms
Z Blanco, B Liu, MM Dehnavi – … of the 47th International Conference on …, 2018 – dl.acm.org
Page 1. CSTF: Large-Scale Sparse Tensor Factorizations on Distributed Platforms Zachary Blanco ? Rutgers University Piscataway, NJ, USA zac.blanco@rutgers.edu Bangtian Liu ? Rutgers University Piscataway, NJ, USA bangtian.liu@rutgers.edu …

Parallel sparse tensor decomposition in chapel
TB Rolinger, TA Simon… – 2018 IEEE International …, 2018 – ieeexplore.ieee.org
… Therefore, for the sake of brevity, in this paper we only present the results from the YELP and NELL-2 data sets, as described in Table I. The Yelp Phoenix Academic Data set, from the Yelp Dataset Challenge3, contains reviews of businesses …

An Ontology-Driven Probabilistic Soft Logic Approach to Improve NLP Entity Annotations
M Rospocher – International Semantic Web Conference, 2018 – Springer
… Some of these works exploit ontological knowledge to constrain the selection of the extracted candidate triples. In NELL (Never-Ending Language Learning) [28], ontological constraints (eg, a person cannot be a city) are used to filter the extracted triples …

Improving Word Representations Using Paraphrase Dataset
FAO Santos, HT Macedo – Information Technology-New Generations, 2018 – Springer
… are knowledge bases that present semantic information about words, such as Freebase [3], WordNet [18], Dbpedia [2], NELL [7]. Often … A. Carlson, J. Betteridge, B. Kisiel, B. Settles, ER Hruschka Jr., TM Mitchell, Toward an architecture for never-ending language learning, in AAAI …

Category-Embodied Knowledge Embedding
M Zhang, Q Wang, Z Xu, J Zhu, S Sun… – … Conference on Neural …, 2018 – Springer
… As for the first three datasets 1 , LOCATION, SPORT, and NELL186 are the subsets of NELL, and are created by the … 891–900 (2015)Google Scholar. 6. Carlson, A., Betteridge, J., Kisiel, B., Settles, B., Mitchell, TM: Toward an architecture for never-ending language learning …

A unified scheme of text localization and structured data extraction for joint OCR and data mining
Y Ye, S Zhu, J Wang, Q Du, Y Yang… – … Conference on Big …, 2018 – ieeexplore.ieee.org
… In addition, Never-Ending Language Learning (NELL) has the ability to learn knowledge and entity relationships [1], IBM Watson extracts text information and metadata [2], and Microsoft Xiaoice acts as a good assistant to recognize text in images and videos [3]. Due to its …

Machine learning with and for semantic web knowledge graphs
H Paulheim – Reasoning Web International Summer School, 2018 – Springer
… One of the earliest approaches working at web-scale was the Never Ending Language Learning (NELL) project [5]. The project works on a large-scale corpus of web sites and exploits a coupled process which learns text patterns corresponding to type and relation assertions, as …

Enriching Frame Representations with Distributionally Induced Senses
S Faralli, A Panchenko, C Biemann… – arXiv preprint arXiv …, 2018 – arxiv.org
… Web-scale information extraction sys- tems like NELL (Carlson et al., 2010) or Knowledge Vault (Dong et al., 2014) can acquire massive amounts of machine-readable knowledge from the Web, whereas projects like … Toward an architec- ture for never-ending language learning …

Revisiting Distant Supervision for Relation Extraction
T Jiang, J Liu, CY Lin, Z Sui – Proceedings of the Eleventh International …, 2018 – aclweb.org
… In recent years, knowledge bases (KBs) like Freebase (Bol- lacker et al., 2008), DBpedia (Lehmann et al., 2015) and NELL (Carlson et al., 2010) have become extremely use- ful resources for many natural language … Toward an architecture for never-ending language learning …

Word Vector Embeddings and Domain Specific Semantic based Semi-Supervised Ontology Instance Population
KM Sugathadasa, V Jayawardana, D Lakmal… – ICTer, 2018 – journal.icter.org
… July 2018 Carlson et al. [38] have expanded coupled semi- supervised learning [37] to never-ending language learning (NELL); an agent that runs forever to extract information from the web and populate them continuously into a knowledge base …

Alexandria: Unsupervised High-Precision Knowledge Base Construction using a Probabilistic Program
J Winn, J Guiver, S Webster, Y Zaykov, M Kukla… – 2018 – openreview.net
… For example, KnowledgeVault [Dong et al., 2014], NELL [Carlson et al., 2010, Mitchell et al., 2015], YAGO2 [Hoffart et al., 2013], DIG [P. Szekely et al., 2015], and many other systems aim either to construct a KB automatically or make an existing KB more complete …

A fast mapper as a foundation for forthcoming conceptual blending experiments
J Gonçalves, P Martins, A Cardoso – International Conference on Case …, 2018 – Springer
… system such as the ones outlined in [6]. However, as far as we know we have not found an algorithm fast enough to extract in real-time mappings from giant non trivial semantic graphs available in the web, such as the Never-Ending Language Learning (NELL) project [17] and …

Harvesting Knowledge from Cultural Heritage Artifacts in Museums of India
A Sancheti, P Maheshwari, R Chaturvedi… – Pacific-Asia Conference …, 2018 – Springer
… Popular knowledge bases like DBpedia [2], NELL [7], YAGO [24] contain “facts” of the form “subject-predicate-object” and are … org/. 7. Carlson, A., Betteridge, J., Kisiel, B., Settles, B., Hruschka Jr., ER, Mitchell, TM: Toward an architecture for never-ending language learning …

Verb Based Conceptual Common Sense Extraction
J Youlang, Y Yang, Z Hongying, Z Jun… – Proceedings of the …, 2018 – dl.acm.org
… work of building a knowledge base which based on information extraction includes: TextRunner/ReVerb[3], NELL[4]?PATTY[5 … Toward an architecture for never-ending language learning[C]. Twenty- Fourth AAAI Conference on Artificial Intelligence, Atlanta, Georgia, 2010 …

Interactive Area Topics Extraction with Policy Gradient
J Han, W Rong, F Zhang, Y Zhang, J Tang… – … Conference on Artificial …, 2018 – Springer
… Carlson et al. [3] design a knowledge base called NELL, which can … J. Mach. Learn. Res. 3, 993–1022 (2003)zbMATHGoogle Scholar. 3. Carlson, A., Betteridge, J., Kisiel, B., Settles, B., Hruschka Jr., ER, Mitchell, TM: Toward an architecture for never-ending language learning …

Big Data and Deep Learning: A Short Survey
N de Silva – pdfs.semanticscholar.org
… The [29] data set contained data from Citeseer, Cora, and Pubmed. The [28] data set contained data from NELL [30] … [30] A. Carlson, J. Betteridge, B. Kisiel, B. Settles, ER Hruschka Jr, and TM Mitchell, “Toward an architecture for never-ending language learning.” in AAAI, vol …

Compositional learning for human object interaction
K Kato, Y Li, A Gupta – Proceedings of the European …, 2018 – openaccess.thecvf.com
… We consider two nodes are connected if (1) they are the immediate hypernym or hyponym to each other (denoted as 1 HOP); (2) their LCH similarity score [32] is larger than 2.0. Furthermore, we extracted SVO from NELL [5] and further verified them using COCO dataset [34] …

Exploring Encoder-Decoder Model for Distant Supervised Relation Extraction.
S Su, N Jia, X Cheng, S Zhu, R Li – IJCAI, 2018 – ijcai.org
… 1 Introduction Knowledge bases (KBs) such as Freebase [Bollacker et al., 2008], DBpedia [Auer et al., 2007], and NELL [Carlson et al., 2010] are extremely useful resources for many NLP tasks including information … Toward an architecture for never-ending language learning …

Construction of MeSH-Like Obstetric Knowledge Graph
K Zhang, K Li, H Ma, D Yue… – … Conference on Cyber …, 2018 – ieeexplore.ieee.org
… There are many famous Knowledge Graph projects such as Google Knowledge Graph, NELL[3], Microsoft’s Satori[4], DBpedia[5], Wikidata[6], CN-DBpedia[7], YAGO2[8], etc. In the medical field, SNOMED-CT[9] and IBM Watson Health are the famous foreign ontology library …

Learning Entity and Relation Embeddings with Entity Description for Knowledge Graph Completion
S Dai, Y Liang, S Liu, Y Wang, W Shao… – 2018 2nd …, 2018 – atlantis-press.com
… I. INTRODUCTION Knowledge graphs (KGs) on a large scale such as NELL [1], Freebase [2], and WordNet [3] are important for … Carlson, J. Betteridge, B. Kisiel, B. Settles, ER Hruschka Jr, and TM Mitchell, “Toward an Architecture for Never-Ending Language Learning,” in AAAI …

Extracting semantic relations for scholarly knowledge base construction
RA Al-Zaidy, CL Giles – 2018 IEEE 12th international …, 2018 – ieeexplore.ieee.org
… for extracting concept attributes [18], [19] Semantic learners based on iterative algorithms such as TextRunner [20], NELL [6], and … A. Carlson, J. Betteridge, B. Kisiel, B. Settles, ER Hruschka Jr, and TM Mitchell, “Toward an architecture for never-ending language learning.” in AAAI …

Scalable distributed semantic network for knowledge management in cyber physical system
S Song, Y Lin, B Guo, Q Di, R Lv – Journal of Parallel and Distributed …, 2018 – Elsevier
… important and difficult task. In recent years, several large-scale knowledge bases have been constructed, such as YAGO [31], NELL [7], DBpedia [3], IBM’s Watson [13] and Microsoft’s EntityCube [39]. However, the rapid growth …

Rule based temporal inference
MW Chekol, H Stuckenschmidt – Technical Communications of …, 2018 – drops.dagstuhl.de
… This is often done by crawl- ing the web and extracting facts and relations using machine learning techniques, for instance NELL [4]. Some of the kgs contain high quality, human curated facts for in- stance YAGO [20], Wikidata [30], DBpedia [1] and some contain probabilistic …

Linknbed: Multi-graph representation learning with entity linkage
R Trivedi, B Sisman, J Ma, C Faloutsos, H Zha… – arXiv preprint arXiv …, 2018 – arxiv.org
… This has led to the increased ef- forts in constructing numerous large-scale Knowl- edge Bases (eg Freebase (Bollacker et al., 2008), DBpedia (Auer et al., 2007), Google’s Knowledge graph (Dong et al., 2014), Yago (Suchanek et al., 2007) and NELL (Carlson et al., 2010)), that …

Research Progress of Knowledge Graph Based on Knowledge Base Embedding
T Caifang, R Yuan, Y Hualei, C Jiamin – International Conference of …, 2018 – Springer
… tasks, and there are also a large number of influential knowledge graph resources, including ConceptNet, WordNet, NELL, YAGO, Google … 121–124 (2013)Google Scholar. 7. Carlson, A., Betteridge, J., Kisiel, B., et al.: Toward an architecture for never-ending language learning …

Multiple order semantic relation extraction
S Song, Y Sun, Q Di – Neural Computing and Applications, 2018 – Springer
… significant and difficult task. In recent years, several large-scale knowledge bases have been constructed, such as YAGO [29], NELL [30], DBpedia [31], IBM’s Watson [32] and Microsoft’s EntityCube [27]. However, most of these …

Improving medium-grain partitioning for scalable sparse tensor decomposition
S Acer, T Torun, C Aykanat – IEEE Transactions on Parallel and …, 2018 – ieeexplore.ieee.org
Page 1. Improving Medium-Grain Partitioning for Scalable Sparse Tensor Decomposition Seher Acer , Tugba Torun, and Cevdet Aykanat Abstract—Tensor decomposition is widely used in the analysis of multi-dimensional data …

Re-evaluating Embedding-Based Knowledge Graph Completion Methods
F Akrami, L Guo, W Hu, C Li – Proceedings of the 27th ACM International …, 2018 – dl.acm.org
… 1 INTRODUCTION Large-scale knowledge graphs (KG) such as Freebase [2], DBpe- dia [1] and NELL [4] store real-world facts in the form of triples (head entity, relation, tail entity), denoted (h, r, t). They are an im- portant resource for many AI-related applications such as …

Question answering over freebase via attentive RNN with similarity matrix based CNN
Y Qu, J Liu, L Kang, Q Shi, D Ye – arXiv preprint arXiv:1804.03317, 2018 – arxiv.org
… 1 Introduction In recent years, several large-scale general-purpose knowledge bases have emerged, including YAGO [8], Freebase [9], NELL [10] and DBpedia [11], and people are seeking effective ways to access the rich knowledge in them …

Deriving validity time in knowledge graph
J Leblay, MW Chekol – Companion Proceedings of the The Web …, 2018 – dl.acm.org
… Some well- known examples of KGs include Google’s Knowledge Vault [5], NELL [4], YAGO [6], and DBpedia [1]. Whether the data is gen- erated and maintained by users or computer programs, mistakes and … 2010. Toward an Architecture for Never-Ending Language Learning …

Natural Language Processing for Information Extraction
S Singh – arXiv preprint arXiv:1807.02383, 2018 – arxiv.org
… There has been growing trend of constructing large Knowledge Bases(KBs) such as Freebase(Bollacker et al., 2008), DBpedia (Auer et al., 2007), YAGO (Suchanek et al., 2007), YAGO2 (Hoffart et al., 2011), Google Knowledge Graph (Dong et al., 2014), NELL (Mitchell et …

Scalable semantic querying of text
X Wang, A Feng, B Golshan, A Halevy… – Proceedings of the …, 2018 – dl.acm.org
Page 1. Scalable Semantic Querying of Text Xiaolan Wang† Aaron Feng‡ Behzad Golshan‡ Alon Halevy‡ George Mihaila‡ Hidekazu Oiwa‡? Wang-Chiew Tan‡ †University of Massachusetts ‡Megagon Labs xlwang@umass …

A Novel Asymmetric Embedding Model for Knowledge Graph Completion
Z Geng, Z Li, Y Han – 2018 24th International Conference on …, 2018 – ieeexplore.ieee.org
… entity. In recent years, with the development of the big data, various large-scale knowledge graphs such as WordNet[5], Freebase[6], Yago[7] and NELL[8] have been built either collaboratively or (partly) automatically. However …

Knowledge graph embedding with iterative guidance from soft rules
S Guo, Q Wang, L Wang, B Wang, L Guo – Thirty-Second AAAI Conference …, 2018 – aaai.org
… Introduction Knowledge graphs (KGs) such as WordNet (Miller 1995), Freebase (Bollacker et al. 2008), YAGO (Suchanek, Kas- neci, and Weikum 2007), and NELL (Carlson et al. 2010) are extremely useful resources for many AI related applica- tions …

Knowledge graph embedding with hierarchical relation structure
Z Zhang, F Zhuang, M Qu, F Lin, Q He – Proceedings of the 2018 …, 2018 – aclweb.org
… Large scale, collaboratively created KGs , such as Free- base (Bollacker et al., 2008), WordNet (Miller, 1994), Yago (Suchanek et al., 2007), Gene On- tology (Sherlock, 2009), NELL (Carlson et al., 2010) and Google’s KG1, have recently become available …

Representation learning of knowledge graphs with entity attributes and multimedia descriptions
Y Zuo, Q Fang, S Qian, X Zhang… – 2018 IEEE Fourth …, 2018 – ieeexplore.ieee.org
… Typical knowledge graph such as Freebase [3], YAGO [4], and NELL [5] consists of entities (nodes) and relations (edges). Each edge of the graph repre- sents a triple (head entity, relation, tail entity) … Toward an architecture for never-ending language learning …

SemaTyP: a knowledge graph based literature mining method for drug discovery
S Sang, Z Yang, L Wang, X Liu… – BMC …, 2018 – bmcbioinformatics.biomedcentral …
… Knowledge graphs (KGs) are collections of relational facts, which have proven to be sources of valuable information that have become important for various applications [14]. The famous knowledge graphs include Freebase [15], DBpedia [16], Nell [17] and YAGO [18], etc …

Integrating User Feedback under Identity Uncertainty in Knowledge Base Construction
A Kobren, N Monath, A McCallum – 2018 – openreview.net
Page 1. Automated Knowledge Base Construction (2019) Conference paper Integrating User Feedback under Identity Uncertainty in Knowledge Base Construction Ari Kobren akobren@cs.umass.edu Nicholas Monath nmonath@cs.umass.edu Andrew McCallum …

Jointly Modeling Structural and Textual Representation for Knowledge Graph Completion in Zero-Shot Scenario
J Ding, S Ma, W Jia, M Guo – Asia-Pacific Web (APWeb) and Web-Age …, 2018 – Springer
… Knowledge graphs (KGs) including Freebase [1], NELL [5] and WordNet [15] provide effective structured information and have been … 5. Carlson, A., Betteridge, J., Kisiel, B., Settles, B., Hruschka Jr., ER, Mitchell, TM: Toward an architecture for never-ending language learning …

The Opportunity
MK Bergman – A Knowledge Representation Practionary, 2018 – Springer
… NELL, for example, contains a relatively flat listing of assertions extracted from the Web for various entities … Some of these efforts, like NELL, or its academic cousins such as KnowItAll or Open IE (UWash), involve extractions from the open Web …

Lifelong Learning of Everyday Human Behaviors using Deep Neural Networks: Dual Memory Architecture and Incremental Moment Matching
??? – 2018 – s-space.snu.ac.kr
… (Carlson et al., 2010; Mitchell et al., 2015) proposed the Never-Ending Language Learner (NELL), which extracts a vari- ety of information from the web and constructs a structured knowledge base. Chen et al. (Chen et al., 2013) extended the NELL to develop the Never-Ending …

Bridging weighted rules and graph random walks for statistical relational models
SM Kazemi, D Poole – Frontiers in Robotics and AI, 2018 – frontiersin.org
The aim of statistical relational learning is to learn statistical models from relational or graph-structured data. Three main statistical relational learning paradigms include weighted rule learning, random walks on graphs, and tensor factorization. These paradigms have been …

Data Analysis Project: Using Knowledge Graphs for Image Classification
K Marino – 2018 – ml.cmu.edu
… 3.2 Graph Search Neural Network The biggest problem in adapting GGNN for image tasks is computational scalability. NEIL [4] for example has over 2000 concepts, and NELL [3] has over 2M confi- dent beliefs. Even after pruning to our task, these graphs would still be huge …

Deterministic and Flexible Parallel Latent Feature Models Learning Framework for Probabilistic Knowledge Graph
X Guan – 2018 – diva-portal.org
… For exam- ple, Oren Etzioni leads an open information extraction (OpenIE), nd Tom Mitchell leads the Never-Ending language learning, NELL). As of now, TextRunner has extracted 500 million entity from 100 million web pages The noise in such data is high [5] …

Bootstrapped Multi-level Distant Supervision for Relation Extraction
Y He, Z Li, G Liu, F Cao, Z Chen, K Wang… – … Conference on Web …, 2018 – Springer
… mechanism, several well-known open information systems are developed such as Snowball [12], KnowItAll [13], NELL [14], and Probase … Carlson, A., Betteridge, J., Kisiel, B., Settles, B., Hruschka Jr., ER, Mitchell, TM: Toward an architecture for never-ending language learning …

OC-2-KB: integrating crowdsourcing into an obesity and cancer knowledge base curation system
JA Lossio-Ventura, W Hogan… – BMC medical …, 2018 – bmcmedinformdecismak …
… Creating KBs has been an active research area with academic projects such as YAGO [17] and NELL [18], community-driven efforts such as Freebase [19] and Wikidata [20], and commercial projects such as those by Google [21] and Facebook [22] …

Relation path embedding in knowledge graphs
X Lin, Y Liang, F Giunchiglia, X Feng… – Neural Computing and …, 2018 – Springer
… Large-scale knowledge graphs, such as Freebase[2], WordNet [22], Yago [28], and NELL [6], are critical to natural language processing applications, eg, question answering [8], relation extraction [26], and language modeling [1]. These knowledge graphs generally contain …

Incorporating structural information in scientific document retrieval
F Azimzadeh, F Norouzi – 2018 4th International Conference on …, 2018 – ieeexplore.ieee.org
… Recent work in the field of constructing graphs of knowledge, such as NOUS [18], Knowledge Vault [19], or NELL [20], focuses on the formation of graphs based on the … Mitchell, “Toward an architecture for never-ending language learning.,” in Proceedings of the 24th AAAI, 2010 …

TTMF: A Triple Trustworthiness Measurement Frame for Knowledge Graphs
S Jia, Y Xiang, X Chen – arXiv preprint arXiv:1809.09414, 2018 – arxiv.org
… meet the speed of updating and growth of the KG [3]. Therefore, an increasing number of researchers are committed to automatically ex- tracting structured information directly from unstructured Internet web pages, such as Open information extraction [4] [5] [6], NELL [7], and …

Deep Generative Modeling with Applications in Semi-Supervised Learning
Z Yang – 2018 – pdfs.semanticscholar.org
Page 1. August 19, 2018 DRAFT Deep Generative Modeling with Applications in Semi-Supervised Learning Zhilin Yang August 10 School of Computer Science Carnegie Mellon University Pittsburgh, PA 15213 Thesis Committee …

Representation Learning for Knowledge Graph with Dynamic Margin
Y Luo, L Chang, G Rao, W Chen… – 2018 11th International …, 2018 – ieeexplore.ieee.org
… knowledge expression, knowledge graph has received great attention from scholars in recent years, such as YAGO[1], NELL[2], DBpedia[3 … [2] A. Carlson, J. Betteridge, B. Kisiel, B Settles, and TM Mitchell, “Toward an Architecture for Never-Ending Language Learning,” In 24th …

Knowledge Graph Representation via Similarity-Based Embedding
Z Tan, X Zhao, Y Fang, B Ge, W Xiao – Scientific Programming, 2018 – hindawi.com
… PresidentOf (DonaldTrump, American)). Nowadays, a great number of knowledge graphs, such as WordNet [11], Freebase [12], DBpedia [13], YAGO [14], and NELL [15] usually do not appear simultaneously. Instead, they were …

Scientific Papers Retrieval with an Emphasis on Graph-based Structural Information
F Norouzi, F Azimzadeh – International Journal of Web Research, 2018 – ijwr.usc.ac.ir
… Recent works in the field of constructing graphs of knowledge, such as NOUS [16], Knowledge Vault [17], or NELL [18],[19],[20],[21],[22], focus on the formation of graphs based on the inference of existing data relationships. Knowledge Vault proposed by Dong et al …

Conditional Relationship Extraction for Diseases and Symptoms by a Web Search-Based Approach
YH Lee, JL Koh – … IEEE/WIC/ACM International Conference on …, 2018 – ieeexplore.ieee.org
… automatically. For example, the online encyclopedia is con- sidered to be a good resource to construct a knowledge base, such as the DBpedia1 constructed from Wikipedia2 [10] and the NELL [11]–[13] learned knowledge from the web …

Parallel CANDECOMP/PARAFAC decomposition of sparse tensors using dimension trees
O Kaya, B Uçar – SIAM Journal on Scientific Computing, 2018 – SIAM
Page 1. Copyright © by SIAM. Unauthorized reproduction of this article is prohibited. SIAM J. SCI. COMPUT. c 2018 Society for Industrial and Applied Mathematics Vol. 40, No. 1, pp. C99–C130 PARALLEL CANDECOMP/PARAFAC …

Automated Knowledge Base Completion Using Collaborative Filtering and Deep Reinforcement Learning
A Tortay, JH Lee, CH Lee… – 2018 IEEE International …, 2018 – ieeexplore.ieee.org
… KBs, such as FreeBase [1], NELL [2], WordNet [3], and DBPedia [4] which aim to provide structured knowledge to users, have … [2] A. Carlson, J. Betteridge, B. Kisiel, B. Settles, ER Hruschka Jr, and TM Mitchell, “Toward an architecture for never-ending language learning.” in AAAI …

Construction and applications of teknowbase: a knowledge base of computer science concepts
P Upadhyay, A Bindal, M Kumar… – … Proceedings of the The …, 2018 – researchgate.net
… There are already many such general-purpose knowledge-bases such as Yago [22] and DBPedia [11]. Moreover, projects such as OpenIE [2] and NELL [3] aim to extract information from unstructured textual sources on a large scale …

Rule-based Indonesian Open Information Extraction
A Romadhony, A Purwarianti… – 2018 5th International …, 2018 – ieeexplore.ieee.org
… Open IE systems that use the learning-based method are: TextRunner [1], ReVerb [4], OLLIE [13], WOE [3] and NELL [15] … [15] A. Carlson, J. Betteridge, B. Kisiel, B. Settles, ER Hruschka Jr, and TM Mitchell, “Toward an architecture for never-ending language learning.” in AAAI …

TrueWeb: A Proposal for Scalable Semantically-Guided Data Management and Truth Finding in Heterogeneous Web Sources
A Madkour, WG Aref, S Prabhakar, M Ali… – Proceedings of the …, 2018 – dl.acm.org
… Other architectures are realized through learning such as NELL [8]. The problem of storage and indexing of distributed datastores was studied from multiple dimensions including Triplestores [15, 22], vertically … 2010. Toward an Architecture for Never-Ending Language Learning …

A Distance Approach for Open Information Extraction Based on Word Vector.
L Peiqian, W Xiaojie – KSII Transactions on Internet & …, 2018 – search.ebscohost.com
… Similar systems are OLLIE [6], NELL [7], Wanderlust [8], SOFIE [9], Prospera [10], PATTY [11], Sonex [12] and Exemplar [13]. There are two significant problems in all prior Open IE systems: incoherent extractions and uninformative extractions …

A systematic mapping study on open information extraction
R Glauber, DB Claro – Expert Systems with Applications, 2018 – Elsevier
Skip to main content …

Bootstrapped graph diffusions: Exposing the power of nonlinearity
B Eliav, E Cohen – Proceedings of the ACM on Measurement and …, 2018 – dl.acm.org
… The data sets include three citation networks: Cora, Pubmed, and Citeseer from [36], one Knowledge graph (entity classification) dataset (NELL) preprocessed by [40] from [7], and the YouTube group membership data set from [28] …

Multi-Cultural Interlinking of Web Taxonomies with ACROSS
N Boldyrev, M Spaniol… – The Journal of Web …, 2018 – nowpublishers.com
… Those knowl- edge collections range from commercial endeavors such as Google Knowledge Graph (Singhal, 2012), centered around Freebase (Bollacker et al., 2008), to academic projects like DBpedia (Auer et al., 2007), Yago (Suchanek et al., 2007), NELL (Carl- son et al …

A Dense Vector Representation for Relation Tuple Similarity
A Romadhony, A Purwarianti… – … Concept Theory and …, 2018 – ieeexplore.ieee.org
… There are several Open IE systems: TextRunner [1], ReVerb [2], Ollie [3], NELL [4], Exemplar [5], ClauseIE [6], Stanford Open IE [7 … A. Carlson, J. Betteridge, B. Kisiel, B. Settles, ER Hruschka Jr, and TM Mitchell, “Toward an architecture for never-ending language learning.” in AAAI …

Sambaten: Sampling-based batch incremental tensor decomposition
E Gujral, R Pasricha, EE Papalexakis – Proceedings of the 2018 SIAM …, 2018 – SIAM
… factor File Size NIPS [8] (Paper,Author,Word) 2,482 x 2862 x 14036 3,101,609 500 10 57MB NELL [2] (Entity,Relation,Entity) 12092 x 9184 x 28818 76,879,419 500 10 1.4GB Facebook-wall [20] (Wall owner, Poster, day) 62,8

Pattern Discovery and Anomaly Detection via Knowledge Graph
B Jia, C Dong, Z Chen, KC Chang… – 2018 21st …, 2018 – ieeexplore.ieee.org
… In recent years, many large Nnowledge graph databases were developed, such as Freebase [1], YAGO [2], NELL [3], DBpedia [4], WordNet [4] and Google Knowledge Graph [6]. Due to the increasing data volume, the implementation of the scalable algorithm is highly desirable …

Blocking Optimization Techniques for Sparse Tensor Computation
J Choi, X Liu, S Smith, T Simon – 2018 IEEE International …, 2018 – ieeexplore.ieee.org
Page 1. Blocking Optimization Techniques for Sparse Tensor Computation Jee W. Choi IBM TJ Watson Research Center Yorktown Heights, NY, USA jwchoi@us.ibm. com Xing Liu ? Parallel Computing Laboratory Intel Corporation …

Deep Learning for Noise-tolerant RDFS Reasoning
B Makni, J Hendler – 2018 – semantic-web-journal.net
… Probability distri- butions are then used to decide if a statement with low relative predicate frequency should be considered erro- neous. Both algorithms are validated on DBpedia and Never-Ending Language Learning (NELL) [5] knowl- edge bases …

Knowledge representation learning: A quantitative review
Y Lin, X Han, R Xie, Z Liu, M Sun – arXiv preprint arXiv:1812.10901, 2018 – arxiv.org
… 1. Introduction In recent years, people have built a large amount of knowledge graphs (KGs) such as Freebase [1], DBpedia [2], YAGO [3], NELL [4] and Wikidata [5]. KGs provide us a novel aspect to describe the real world, which stores structured relational facts of …

Bridging Weighted Rules and Graph Random Walks for Statistical Relational Models. Front. Robot. AI 5: 8. doi: 10.3389/frobt. 2018.00008 Bridging Weighted …
SM Kazemi – Computer Science Department, University of British …, 2018 – persagen.com
Page 1 …

Towards practical open knowledge base canonicalization
TH Wu, Z Wu, B Kao, P Yin – Proceedings of the 27th ACM International …, 2018 – dl.acm.org
… ConceptResolver processes names extracted by the NELL sys- tem [3]. NELL is an OIE system that extracts knowledge by reading the Web. Each name extracted by NELL is assigned a category, such as it being a “city” or a “company” …

Effective heuristics for matchings in hypergraphs
I Panagiotas, B Uçar, F Dufossé, K Kaya – 2018 – hal.archives-ouvertes.fr
Page 1. HAL Id: hal-01924180 https://hal.archives-ouvertes.fr/hal-01924180v2 Submitted on 21 Nov 2018 HAL is a multi-disciplinary open access archive for the deposit and dissemination of sci- entific research documents, whether they are pub- lished or not …

From knowledge graph embedding to ontology embedding? An analysis of the compatibility between vector space representations and rules
V Gutiérrez-Basulto, S Schockaert – Sixteenth International Conference on …, 2018 – aaai.org
… Bollacker et al. 2008), ConceptNet (Speer, Chin, and Havasi 2017) and WikiData (Vrandecic and Krötzsch 2014), and resources that have been extracted from natural lan- guage such as NELL (Carlson et al. 2010). However, de …

N-ary relation extraction for simultaneous T-Box and A-Box knowledge base augmentation
M Fossati, E Dorigatti, C Giuliano – Semantic Web, 2018 – content.iospress.com
The Web has evolved into a huge mine of knowledge carved in different forms, the predominant one still being the free-text document. This motivates the need for intelligent Web-reading agents : hypothetically, they would skim through disparate Web so.

CrumbTrail: An efficient methodology to reduce multiple inheritance in knowledge graphs
S Faralli, I Finocchi, SP Ponzetto, P Velardi – Knowledge-Based Systems, 2018 – Elsevier
… years ago. Web-scale open information extraction systems like NELL [1] or ReVerb [2] have been successful in acquiring massive amounts of machine-readable knowledge by effectively tapping large amounts of text. Moreover …

Construction and recommendation of a water affair knowledge graph
J Yan, T Lv, Y Yu – Sustainability, 2018 – mdpi.com
… KnowItAll [15] and Nell [16] used iterative methods to learn high-quality triples from web page data to construct a knowledge graph. However, these studies are not fully applicable to the construction and application of a water knowledge graph …

Automatic Detection of Relation Assertion Errors and Induction of Relation Constraints
A Melo, H Paulheim – semantic-web-journal.net
… We perform an extensive evaluation on a variety of datasets comparing our error detection approach with state-of-the-art error detection and knowledge completion methods, backed by a manual evaluation on DBpedia and NELL …

Fvqa: Fact-based visual question answering
P Wang, Q Wu, C Shen, A Dick… – IEEE transactions on …, 2018 – ieeexplore.ieee.org
… Large-scale structured KBs are constructed either by man- ual annotation (eg, DBpedia [22], Freebase [24] and Wiki- data [28]), or by automatic extraction from unstructured/ semi-structured data (eg, YAGO [27], [32], OpenIE [23], [33], [34], NELL [25], NEIL [26], WebChild [35 …

Fake news: A survey of research, detection methods, and opportunities
X Zhou, R Zafarani – arXiv preprint arXiv:1812.00315, 2018 – arxiv.org
… 2013; Suchanek et al. 2007], Freebase18 [Bollacker et al. 2008], NELL [Carlson et al. 2010], PATTY [Nakashole et al. 2012], DBpedia [Auer et al. 2007], Elementary/DeepDive [Niu et al. 2012], and Knowledge Vault [Dong et al. 2014] …

Scalable integration of uncertainty reasoning and semantic web technologies
J Schönfisch – 2018 – madoc.bib.uni-mannheim.de
… Data obtained through open information extraction (OIE), like the knowledge bases created by NELL5 (Carlson et al., 2010), YAGO6 (Hoffart et al., 2013), Mi- crosoft’s Concept Graph7 (Wu et al., 2012), or Google’s Knowledge Vault8 (Dong …

Semantic Assets: Latent Structures for Knowledge Management
S Melzer – 2018 – d-nb.info
Page 1. From the Institute of Information Systems of the University of Lübeck Director: Prof. Dr. rer. nat. habil. Ralf Möller Semantic Assets: Latent Structures for Knowledge Management Dissertation for Fulfillment of Requirements for the Doctoral Degree …

The BigGrams: the semi-supervised information extraction system from HTML: an improvement in the wrapper induction
MM Miro?czuk – Knowledge and Information Systems, 2018 – Springer
… [21]. IESs, such as Never-Ending Language Learner (NELL), Know It All, TextRunner, or Snowball represent this approach [1, 3, 6, 9, 10, 22, 23, 56, 59, 68, 78]. The systems mentioned above represent the trend called open IE …

Tracking in information space
H Greenhough – 2018 – spiral.imperial.ac.uk
… NELL Another notable example of automatic KG construction is the Never Ending Lan- guage Learner (NELL) developed at Carnegie Mellon University (CMU). The input and ap- proach is similar to that of DeepDive but NELL, in addition to the distantly supervised ap …

Active instance matching with pairwise constraints and its application to Chinese knowledge base construction
W Lu, H Dai, Z Zhang, C Wu, Y Zhuang – Knowledge and Information …, 2018 – Springer
… Knowledge bases, such as DBpedia [1], YAGO [2], Freebase [3], NELL [4], play an increasingly important role in text understanding. Among them, DBpedia has become the central hub and reference point in the Linked Open Data (LOD) …

Extending the YAGO knowledge base
MB AMANN – 2018 – thomasrebele.org
… Information extraction techniques, such as regular expressions, could quickly generate large num- bers of facts. Some of the more prominent approaches along these lines are YAGO, DBpedia, Wiki- data, NELL, and Google’s Knowledge Vault …

Explainable Fact Checking by Combining Automated Rule Discovery withProbabilistic Answer Set Programming
A Pradhan – 2018 – search.proquest.com
… Auer et al. (2007), Wikidata Vrandecic and Krötzsch (2014), Yago Suchanek et al. (2007), and NELL Carlson et al. (2010). Several KGs consist of millions of entities … (2007), Wikidata Vrandecic and Krötzsch (2014), Yago Suchanek et al. (2007), and NELL Carlson et al. (2010) …

Multi-dimensional mining of unstructured data with limited supervision
C Zhang – 2018 – ideals.illinois.edu
Page 1. c 2018 Chao Zhang Page 2. MULTI-DIMENSIONAL MINING OF UNSTRUCTURED DATA WITH LIMITED SUPERVISION BY CHAO ZHANG DISSERTATION Submitted in partial fulfillment of the requirements for the degree …

A virtual memory manager optimized for node-level cooperative multi-tasking in memory constrained systems
J Iverson, G Karypis – The International Journal of High …, 2018 – journals.sagepub.com
There is a growing need to perform large computations on small systems, as access to large systems is not widely available and cannot keep up with the size of t…