# Very old LLM / Knowledge-Graph Research Notes (2019)
## Core ontology / knowledge-base stack (entities, vocabularies, schemas)
#DBpedia, #Freebase, #OpenCyc, #Wikidata, and #YAGO; Google Knowledge Vault, #UMBEL, #FrameBASE, #WordNet, #Propbank, #Menya and #YAGO-SUMO, #Microsoft #Satori, #Siemens, along with many other #semantic #ontologies and #taxonomical #lexicons in the field of #NLP as applied to bootstrapping AI from language. I even worked with the top physics professor at Carnegie Mellon University, who developed the wordnet project.
[[DBpedia]], [[Freebase]], [[OpenCyc]], [[Wikidata]], and [[YAGO]]; [[Google Knowledge Vault]], [[UMBEL]], [[FrameBASE]], [[WordNet]], [[Propbank]], [[Menya]] and [[YAGO-SUMO]], [[Microsoft Satori]], [[Siemens]], along with many other [[semantic ontologies]] and [[taxonomical lexicons]] in the field of [[NLP]] as applied to bootstrapping AI from language. I even worked with the top physics professor at [[Carnegie Mellon University]], who developed the [[wordnet]] project.
## Graph visualization, exploration, and network tooling
[https://gephi.org/](https://gephi.org/)
[http://sigmajs.org/](http://sigmajs.org/)
[https://js.cytoscape.org/](https://js.cytoscape.org/)
[https://github.com/cytoscape/cytoscape.js-edgehandles](https://github.com/cytoscape/cytoscape.js-edgehandles)
[http://www.pearltrees.com/u/24265944-ucinet-software#l234](http://www.pearltrees.com/u/24265944-ucinet-software#l234)
[http://www.martingrandjean.ch/introduction-to-network-visualization-gephi/](http://www.martingrandjean.ch/introduction-to-network-visualization-gephi/)
[http://philogb.github.io/jit/](http://philogb.github.io/jit/)
### Ontology visualization / Semantic Web tools directories
[http://www.mkbergman.com/414/large-scale-rdf-graph-visualization-tools/](http://www.mkbergman.com/414/large-scale-rdf-graph-visualization-tools/)
[https://www.w3.org/wiki/SemanticWebTools](https://www.w3.org/wiki/SemanticWebTools)
[http://www.visualdataweb.de/webvowl/#sioc](http://www.visualdataweb.de/webvowl/#sioc)
[http://vowl.visualdataweb.org/webvowl.html](http://vowl.visualdataweb.org/webvowl.html)
[http://tapor.ca/tools?attribute_values=66](http://tapor.ca/tools?attribute_values=66)
## Linked Data / RDF infrastructure and “plumbing”
### Linked Open Vocabularies and LOD ecosystems
Linked Open Vocabularies: [https://lov.linkeddata.es/dataset/lov/](https://lov.linkeddata.es/dataset/lov/)
LLOD cloud: [http://linghub.org/llod-cloud](http://linghub.org/llod-cloud)
LinkedData.org: [http://linkeddata.org/](http://linkeddata.org/)
LOD Cloud dataset browser: [https://lod-cloud.net/dataset/umbel](https://lod-cloud.net/dataset/umbel)
LOD Laundromat: [http://lodlaundromat.org](http://lodlaundromat.org/)
Linked Data Fragments: [https://linkeddatafragments.org/](https://linkeddatafragments.org/)
The Chisel Group / Lodestone: [https://thechiselgroup.org/lodestone/](https://thechiselgroup.org/lodestone/)
Apache Jena: [http://jena.apache.org/](http://jena.apache.org/)
Magnus Tools (WMF Labs): [https://tools.wmflabs.org/magnustools/](https://tools.wmflabs.org/magnustools/)
OpenVocab: [http://vocab.org/open/](http://vocab.org/open/)
Semantic Web Challenge: [http://challenge.semanticweb.org/](http://challenge.semanticweb.org/)
PermID: [https://permid.org/](https://permid.org/)
Code Google Archive: [https://code.google.com/archive/](https://code.google.com/archive/)
### RDF/HDT
RDF/HDT downloads: [http://www.rdfhdt.org/downloads/](http://www.rdfhdt.org/downloads/)
## DBpedia / Wikidata / DBkWik (downloads, dev endpoints, dataset notes)
LIVE DBpedia dev (2019): [http://dev.dbpedia.org/](http://dev.dbpedia.org/)
DBpedia Databus: [https://databus.dbpedia.org/](https://databus.dbpedia.org/)
Databus DBpedia/Wikidata: [https://databus.dbpedia.org/dbpedia/wikidata/](https://databus.dbpedia.org/dbpedia/wikidata/)
DBpedia downloads index (historic): [http://downloads.dbpedia.org/](http://downloads.dbpedia.org/)
DBpedia 2015-10 core: [http://downloads.dbpedia.org/2015-10/core/](http://downloads.dbpedia.org/2015-10/core/)
DBpedia datasets wiki: [https://wiki.dbpedia.org/develop/datasets](https://wiki.dbpedia.org/develop/datasets)
DBpedia “downloads 2016-10” page: [https://wiki.dbpedia.org/develop/datasets/downloads-2016-10](https://wiki.dbpedia.org/develop/datasets/downloads-2016-10)
DBpedia “Download_DBpedia” (dev): [http://dev.dbpedia.org/Download_DBpedia](http://dev.dbpedia.org/Download_DBpedia)
Investigate DBkWik: [http://dbkwik.webdatacommons.org/](http://dbkwik.webdatacommons.org/)
Mailing-list thread: “which folders to index to get full English coverage?”
[https://sourceforge.net/p/dbpedia/mailman/message/35012273/](https://sourceforge.net/p/dbpedia/mailman/message/35012273/)
## YAGO (papers, downloads, tools)
Go to the YAGO web page to check individual downloads: simplified taxonomy, multilingual, links to DBpedia, GeoNames, WordNet.
YAGO overview:
[https://www.mpi-inf.mpg.de/departments/databases-and-information-systems/research/yago-naga/yago/](https://www.mpi-inf.mpg.de/departments/databases-and-information-systems/research/yago-naga/yago/)
YAGO downloads:
[https://www.mpi-inf.mpg.de/departments/databases-and-information-systems/research/yago-naga/yago/downloads/](https://www.mpi-inf.mpg.de/departments/databases-and-information-systems/research/yago-naga/yago/downloads/)
YAGO3 repo:
[https://github.com/yago-naga/yago3](https://github.com/yago-naga/yago3)
YAGO3 browser:
[https://gate.d5.mpi-inf.mpg.de/webyago3spotlxComp/SvgBrowser/](https://gate.d5.mpi-inf.mpg.de/webyago3spotlxComp/SvgBrowser/)
Related reading (Wikipedia + ontologies):
Semantic Content Filtering with Wikipedia and Ontologies
[http://wikipapers.referata.com/wiki/Semantic_Content_Filtering_with_Wikipedia_and_Ontologies](http://wikipapers.referata.com/wiki/Semantic_Content_Filtering_with_Wikipedia_and_Ontologies)
YAGO paper summary page:
[http://wikipapers.referata.com/wiki/YAGO:_A_Large_Ontology_from_Wikipedia_and_WordNet](http://wikipapers.referata.com/wiki/YAGO:_A_Large_Ontology_from_Wikipedia_and_WordNet)
## Freebase → Knowledge Graph → Knowledge Vault (note)
Freebase: The Knowledge Graph was powered in part by Freebase.[13] In August 2014, New Scientist reported that Google had launched Knowledge Vault. (Freebase → Knowledge “Vault”; from free to closed)
## UMBEL (definition note + link)
UMBEL: a lightweight reference structure of 20,000 subject concept classes and their relationships derived from OpenCyc, which can act as binding classes to external data; also has links to 1.5 million named entities from DBpedia and YAGO.
[https://en.wikipedia.org/wiki/UMBEL](https://en.wikipedia.org/wiki/UMBEL)
Freebase dump tool / mirror:
[http://freebase-easy.cs.uni-freiburg.de/dump/](http://freebase-easy.cs.uni-freiburg.de/dump/)
## FrameBase / FrameNet / PropBank / VerbNet / WordNet
FrameBase home: [http://www.framebase.org/](http://www.framebase.org/)
FrameNet: [https://framenet.icsi.berkeley.edu/fndrupal/](https://framenet.icsi.berkeley.edu/fndrupal/)
Cutting Edge: FrameBase
Currently framebase is supporting (getting ready to break off and hide) project at next leg: PIKES (see below)
[http://pikes.fbk.eu/eval-framebase.html](http://pikes.fbk.eu/eval-framebase.html)
FrameBase notes: FrameBase uses frame semantics (natural language semantics) to represent knowledge about the world in a consistent way. I also developed a new browsing interface for the FrameNet lexical resource, which FrameBase relies on. FrameBase integrates data from FrameNet, WordNet, YAGO, Freebase, DBpedia and Schema.org. Funding: EU FP7 under grant agreement No. FP7-SEC-2012-312651 (ePOOLICE). FrameBase integrates knowledge from large-scale LOD knowledge bases (e.g., YAGO2s, Freebase) and events from DBpedia and Schema.org under a single schema; it provides a flexible way to capture n-ary relationships by combining repositories of frames (FrameNet, WordNet), and it links to other KGs via integration rules beyond binary properties like owl:sameAs and rdfs:subClassOf. “If you can express it with language, you can express it with FrameBase.”
PIKES note: PIKES processed the whole SEW corpus in ∼507 core hours, average 1.2s/sentence and 16.7s/document; with 16 parallel instances finished in <32 hours; produced 357,853,792 triples.
PropBank / VerbNet / FrameNet / WordNet (definitions):
PropBank: a corpus of one million words of English text, annotated with argument role labels for verbs; and a lexicon defining those argument roles on a per-verb basis.
VerbNet: a lexicon that groups verbs based on their semantic/syntactic linking behavior.
FrameNet: a lexicon based on frame semantics.
WordNet: a lexicon that describes semantic relationships (synonymy, hyperonymy, etc.) between individual words.
## BabelNet / multilingual lexical resources
BABELNET downloads: [https://babelnet.org/download](https://babelnet.org/download)
MENTA (multilingual taxonomy induced from Wikipedia):
MENTA: inducing multilingual taxonomies from Wikipedia
Gerard de Melo, Gerhard Weikum. Published in CIKM 2010. DOI:10.1145/1871437.1871577
Summary note: integrates entities from all editions of Wikipedia + WordNet into a coherent taxonomic class hierarchy using linking heuristics, graph partitioning for equivalence classes, and Markov chain-based ranking; describes 5.4 million entities; “largest multilingual lexical knowledge base currently available.”
Additional note: “new MENTA extension adds a large-scale hierarchical taxonomy of named entities and their classes, drawing on over 200 different language editions of Wikipedia… over 15 million words and names in different languages.”
UWN: an automatically constructed multilingual lexical knowledge base based on WordNet.
Etymological Wordnet: a database of etymological and derivational relationships between words in different languages, mined from Wiktionary.
## NLP software (tooling list)
Stanford CoreNLP; OpenNLP; SENNA; MaltParser.
## Linguistic resources (corpora / lexicons list)
Wiktionary; VerbNet; PropBank; FrameNet; WordNet; Penn Tree Bank; Valex; Engtwol.
## “Best evolution” / state-of-the-art aggregations and reference hubs
KG SOTA mindmap: [https://coggle.it/diagram/W0PHnq_5PV00kuQd/t/knowledge-graph-kg-sota](https://coggle.it/diagram/W0PHnq_5PV00kuQd/t/knowledge-graph-kg-sota)
Gerard de Melo projects hub: [http://gerard.demelo.org/projects.html](http://gerard.demelo.org/projects.html)
Word2vec archive: [https://code.google.com/archive/p/word2vec/](https://code.google.com/archive/p/word2vec/)
## DataHub / dataset collections
DataHub collections: [https://datahub.io/collections/](https://datahub.io/collections/)
YAGO on DataHub: [https://datahub.io/collections/yago](https://datahub.io/collections/yago)
Reference data: [https://datahub.io/collections/reference-data](https://datahub.io/collections/reference-data)
Logistics data: [https://datahub.io/collections/logistics-data](https://datahub.io/collections/logistics-data)
Bibliographic data: [https://datahub.io/collections/bibliographic-data](https://datahub.io/collections/bibliographic-data)
OLD DataHub dataset index: [https://old.datahub.io/dataset](https://old.datahub.io/dataset)
BTC 2010 (AIFB/KIT): [http://km.aifb.kit.edu/projects/btc-2010/](http://km.aifb.kit.edu/projects/btc-2010/)
SocialLink note: Matches social media accounts on Twitter to the corresponding entities in multiple language chapters of DBpedia.
[https://old.datahub.io/dataset/sociallink](https://old.datahub.io/dataset/sociallink)
[http://sociallink.futuro.media/](http://sociallink.futuro.media/)
## DBpedia / YAGO / KG comparison and survey materials
Comparisons note: DBpedia is a community effort to extract structured information from Wikipedia. In this sense, both YAGO and DBpedia share the same goal of generating a structured ontology. The projects differ in their foci: in YAGO, the focus is on precision, the taxonomic structure, and the spatial and temporal dimension.
Good article links (first “amazing”, second useful):
[http://ifs.tuwien.ac.at/keystone.school/slides/Paulheim_KnowledgeGraph.pdf](http://ifs.tuwien.ac.at/keystone.school/slides/Paulheim_KnowledgeGraph.pdf)
[https://www.lri.fr/~sais/KGC/1-Introduction.pdf](https://www.lri.fr/~sais/KGC/1-Introduction.pdf)
“Which Knowledge Graph Is Best for Me?” / comparison references:
Linked Data Quality of DBpedia, Freebase, OpenCyc, Wikidata, and YAGO (arXiv PDF): [https://arxiv.org/pdf/1809.11099.pdf](https://arxiv.org/pdf/1809.11099.pdf)
Comparative Survey (Semantic Web Journal): [http://www.semantic-web-journal.net/system/files/swj1141.pdf](http://www.semantic-web-journal.net/system/files/swj1141.pdf)
Linked Data Quality (Uni Trier PDF): [https://www.uni-trier.de/fileadmin/fb2/LDV/Rettinger/publications/KG-Comparison-SWJ-Article.pdf](https://www.uni-trier.de/fileadmin/fb2/LDV/Rettinger/publications/KG-Comparison-SWJ-Article.pdf)
Machine Learning with and for Semantic Web Knowledge Graphs (slides): [https://www.slideshare.net/heikopaulheim/machine-learning-with-and-for-semantic-web-knowledge-graphs](https://www.slideshare.net/heikopaulheim/machine-learning-with-and-for-semantic-web-knowledge-graphs)
Survey / refinement:
Knowledge graph refinement survey (Paulheim et al. 2017): [http://www.semantic-web-journal.net/system/files/swj1167.pdf](http://www.semantic-web-journal.net/system/files/swj1167.pdf)
Additional SWJ link: [http://www.semantic-web-journal.net/system/files/swj2096.pdf](http://www.semantic-web-journal.net/system/files/swj2096.pdf)
OpenCyc/Wikidata/YAGO doc: [https://www.yumpu.com/en/document/read/52785352/opencyc-wikidata-and-yago](https://www.yumpu.com/en/document/read/52785352/opencyc-wikidata-and-yago)
## Knowledge Graph SOTA notes (tools, enterprise KGs, tutorials, domains)
Knowledge Graph (KG) SOTA
Tools / commercial solutions:
[Grapho](https://gra.fo/) (Thomson Reuters)
metaphactory: [http://www.semantic-web-journal.net/system/files/swj2051.pdf](http://www.semantic-web-journal.net/system/files/swj2051.pdf) (Haase et al. 2018)
Tool: [http://metaphacts.com/graphscope](http://metaphacts.com/graphscope)
Tutorial: [https://help.metaphacts.com/resource/Help:AWS-trial-getting-started](https://help.metaphacts.com/resource/Help:AWS-trial-getting-started)
Enterprise KG examples:
Schema.org; Google Knowledge Graph (2012); Google Knowledge Vault (2014)
Google Knowledge Graph 2012 video: [https://youtu.be/mmQl6VGvX-c](https://youtu.be/mmQl6VGvX-c)
Paper: Schema.org: Evolution of structured data on the Web (Guha et al. 2016)
Paper: Knowledge Vault: A Web-Scale Approach to Probabilistic Knowledge Fusion (Dong et al. SIGKDD 2014): [https://www.cs.ubc.ca/~murphyk/papers/kv-kdd14.pdf](https://www.cs.ubc.ca/~murphyk/papers/kv-kdd14.pdf)
IBM Watson
Microsoft Satori (KDD 2018 tutorial): [https://kdd2018tutorialt39.azurewebsites.net/KDD%20Tutorial%20T39.pdf](https://kdd2018tutorialt39.azurewebsites.net/KDD%20Tutorial%20T39.pdf)
Bing blog: [https://blogs.bing.com/search/2013/03/21/understand-your-world-with-bing/](https://blogs.bing.com/search/2013/03/21/understand-your-world-with-bing/)
Facebook Open Graph Protocol / Graph API: [https://developers.facebook.com/docs/sharing/opengraph](https://developers.facebook.com/docs/sharing/opengraph)
Springer KG; LinkedIn KG; Airbnb KG: [https://medium.com/airbnb-engineering/contextualizing-airbnb-by-building-knowledge-graph-b7077e268d5a](https://medium.com/airbnb-engineering/contextualizing-airbnb-by-building-knowledge-graph-b7077e268d5a)
Yahoo Spark KG / Yahoo entity search
Amazon Product Graph / True Knowledge; Apple Siri; Siemens; Elsevier KG; Baidu; eBay Beam KG: [https://github.com/eBay/beam](https://github.com/eBay/beam)
Siemens note: Use cases of the industrial knowledge graph at Siemens (Hubauer et al. ISWC 2018)
“What is a Knowledge Graph?” (McCusker et al. 2018)
[http://graphs.whyis.io/ns](http://graphs.whyis.io/ns) (dead link September 2018)
[http://graphs.whyis.io/](http://graphs.whyis.io/) (dead link September 2018)
Survey / tutorials:
Constructing and Mining Web-scale Knowledge Graphs (Bordes & Gabrilovich, KDD 2014 tutorial slides): [http://www.cs.technion.ac.il/~gabr/publications/papers/KDD14-T2-Bordes-Gabrilovich.pdf](http://www.cs.technion.ac.il/~gabr/publications/papers/KDD14-T2-Bordes-Gabrilovich.pdf)
Tutorial summary: [https://dl.acm.org/citation.cfm?id=2914807](https://dl.acm.org/citation.cfm?id=2914807)
Domain-specific KG (Health):
Personalized Healthcare KG (PHKG): [http://wiki.knoesis.org/index.php/KnoesisKnowledgeGraph](http://wiki.knoesis.org/index.php/KnoesisKnowledgeGraph)
DepressionKG: Constructing Knowledge Graphs of Depression (Huang et al. HIS 2017): [https://link.springer.com/chapter/10.1007/978-3-319-69182-4_16](https://link.springer.com/chapter/10.1007/978-3-319-69182-4_16)
Semantic Health Knowledge Graph (Shi et al. 2016): [https://pdfs.semanticscholar.org/439f/4ef90197d7b74147d3ae754a0053ecd9da0b.pdf](https://pdfs.semanticscholar.org/439f/4ef90197d7b74147d3ae754a0053ecd9da0b.pdf)
TCM KG framework (Weng et al. 2017): [https://jbiomedsem.biomedcentral.com/articles/10.1186/s13326-017-0145-x](https://jbiomedsem.biomedcentral.com/articles/10.1186/s13326-017-0145-x)
KnowLife (Ernst et al. 2015): [https://www.ncbi.nlm.nih.gov/pubmed/25971816](https://www.ncbi.nlm.nih.gov/pubmed/25971816)
KnowLife demo: [http://knowlife.mpi-inf.mpg.de/](http://knowlife.mpi-inf.mpg.de/)
OpenKG: [http://OpenKG.CN](http://openkg.cn/)
Research KGs / academic:
AceKG dataset: [https://www.acemap.info/app/AceKG/](https://www.acemap.info/app/AceKG/)
AceKG paper (Wang et al. 2018): [https://arxiv.org/pdf/1807.08484.pdf](https://arxiv.org/pdf/1807.08484.pdf)
Research Graph: [http://researchgraph.org/](http://researchgraph.org/)
Academic KG resources:
Freebase (developers): [https://developers.google.com/freebase/](https://developers.google.com/freebase/)
Freebase paper (Bollacker et al. SIGMOD 2008): [http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.538.7139&rep=rep1&type=pdf](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.538.7139&rep=rep1&type=pdf)
DBpedia paper (Lehmann et al. 2015): [http://svn.aksw.org/papers/2013/SWJ_DBpedia/public.pdf](http://svn.aksw.org/papers/2013/SWJ_DBpedia/public.pdf)
YAGO: [http://www.mpi-inf.mpg.de/yago-naga/yago](http://www.mpi-inf.mpg.de/yago-naga/yago)
KBpedia: [http://kbpedia.com/](http://kbpedia.com/)
PROSPERA; CyC and OpenCyc; Wikidata; Bio2RDF; Unigraph: [https://unigraph.io/](https://unigraph.io/)
DataCommons; NELL; Open Knowledge Network; WordNet; BabelNet; ConceptNet; DeepDive; OpenKE: [http://openke.thunlp.org](http://openke.thunlp.org/)
Other KGs: SWEET NASA
Additional tutorials:
Ren et al. WWW 2018
[https://kgtutorial.github.io/](https://kgtutorial.github.io/) (Pujara et al.)
WSDM 2018 and AAAI 2017
Facebook Google KDD 2014
Domain-specific (IoT):
Graph of Things (Le-Phuoc)
Semantic Sensor Web / Linked Sensor Data
Knowledge Graph for IoT (IBM Research Ireland) – Feb. 2018: [https://www.youtube.com/watch?v=ebBTdH62yLg](https://www.youtube.com/watch?v=ebBTdH62yLg)
Oxford Semantic: [https://www.oxfordsemantic.tech/](https://www.oxfordsemantic.tech/)
## RDFox / ontology-driven data integration (concept notes + images)
## Data integration
Data can be structured in many different ways that are not compatibly queryable. RDFox allows the user to create an ontology—a rich conceptual model that can integrate multiple views of the data. Combine different data sources coherently to gain insight from a broader viewpoint.
## AI Q&A
Databases are designed to answer only a particular type of question, and only in a particular way; this makes it hard to answer unanticipated user questions (e.g., chatbots). Since RDFox represents data as a graph, information can be stored coherently; thanks to reasoning, RDFox can process queries formulated by non-expert users. Store all your data in a single database in an easily accessible way.
## Risk & compliance
It is difficult to design an effective data model for regulatory compliance, as regulations can be complex to model. RDFox makes it easier to model complex business concepts, and automatically infers facts based on existing data and rules. Avoid reputational and financial damage.
Oxford CS Foundations / Structures / Quantum theme:
[http://www.cs.ox.ac.uk/research/fls/index.html](http://www.cs.ox.ac.uk/research/fls/index.html)
The Foundations, Structures and Quantum theme encompasses interdisciplinary research into conceptual and structural underpinnings across computer science, physics, pure mathematics, natural language, cognition, and AI; includes Quantum Computation and Foundations work employing category theory, diagrammatic reasoning, convex structures and logic.
## 2019 Product / design notes (social graph UI, incentives, “The River”)
I think we should attempt to find the best scraps that they left behind when they made the break to private copyrighted data sets, and try to build our own very basic and creative solution that at least puts us in the game. Or else no one will take the project seriously, and our data will be too chaotic and have so much noise in it that it will have very little value in the competitive space of data-driven success.
But we can’t spend months trying to figure out how to do this. We need to examine the data sets we can get our hands on and pick the most concise, manageable, and small-but-accurate data set that can serve as our initial structural framework so that as people start using the system we can do the same thing the big players did by refining the data; we can use the data to steer the ship. We have to look deep, think simple, innovate, and find the magic balance that serves as our foundation.
Yeah I think one of the first steps is just to get any one of these databases imported into the graph with at least an 80% accuracy rate of nodes and edges—but not one of the giant databases that has too many leaves. As long as we can keep it as simple as a tweet on mobile, it sounds great, but we do need some chaos (as you know from graphics) that creates the waves of patterns; so I like the incentive toward “good” part.
5:41 PM: We can turn the river as a config page of the feed on mobile.
5:41 PM: but it needs to be an info and STALKERS paradise where people can see interactions and investigate.
5:41 PM: lol 5:41 PM: i see~
this will bring them in—who looked at what. then the incentive part is like graphic structure that brings in the symmetry from the chaos. right now graph actions are hidden, but with our model of activity: x looked at your post; x clicked on your main menu; x visited one of your friends profile; etc. people can see (the real way) people are using system—curiosity, intrigue, people watching. people like these things. then merit system suggests order like laws of math to the static. without disruption no graphic beauty emerges. so we find perfect blend for social fabric, plus it is more truthful to the ways people interact.
I agree and that's why in our previous system we had two streams. One was just for the user to see what people are interacting with their content. Since it's their content they have a right to know if anonymous users or other users are looking at their content. The public activity stream that anyone can look at would, as you say, be more private and we would be more selective about the type of material we display.
### THE RIVER
But for example if you look at LinkedIn you can see who is looking at your profile and what companies they belong to. People regularly check their LinkedIn just to see who's looking at their profile. Even though it's not their own content, LinkedIn also shows anonymous views of your content: sometimes it just says a person from a region or a company; sometimes it shows their name and company. Anonymous user information is also no different than a person having a website and looking at their logs to see what content people are looking at and engaging with. I don't know if we should allow private profiles.
And voting is only allowed by login members and we will assign a different weight of the vote up and down based on if they are verified by presenting a copy of their ID, or if they are verified as a public figure.
5:54 PM: ok, back
5:55 PM: That would incentivize more people to use their true identity, and the more people who use their true identity the more tame the space will be.
We could have several relation graphs: one based on WordNet; or a basic 3-level taxonomy of the main fields and locals; or we can let them use deep dive with Freebase. How much organization can these new tools you discovered and NLP provide?
6:19 PM: hmm let me list the features that the NLP can do
6:19 PM: without taxonomy we have no real suggestion model other than interaction
Wong, 6:20 PM:
1. sentiment
2. topics
3. related words using freebase model
4. extract adjectives
## Misc research notes and excerpts (kept verbatim)
Google, IBM, and Intel have all displayed quantum processors with around 50 qubits, devices that are the building blocks of quantum computers, around the size experts expected would be needed to demonstrate quantum supremacy.
## Constellation / LOD sky reference
[http://www.mkbergman.com/457/a-new-constellation-in-the-linking-open-data-lod-sky/](http://www.mkbergman.com/457/a-new-constellation-in-the-linking-open-data-lod-sky/)
---
# Unknown placement / unclear items (retained, not dropped)
1. “Ontology RDFs” (header fragment; no additional content provided)
2. “Linked Open Vocabularies” appears both as a section label and a link; link retained above
3. “DataHub.io” appears as a label; collections links retained above
4. “Ontology RDFs / ### DBpedia, Freebase, OpenCyc, Wikidata, and YAGO” (duplicate concept already covered; leaving as an unresolved header stub here)
5. “Menya” / “MENTA” / “UWN” relationship ambiguity: note contains both “Menya” and “MENTA”; “MENTA” is defined in-text; “Menya” is referenced in the opening ontology list but not defined elsewhere in the note (kept as-is)