Semanlink - Favoris ; Semantic Web AND Machine learning

> Top2Vec is an algorithm for topic modeling and semantic search. It automatically detects topics present in text and generates jointly embedded topic, document and word vectors.
>
> "Update: Pre-trained Universal Sentence Encoders and BERT Sentence Transformer now available for embedding."

> **The assumption the algorithm makes is that many semantically similar documents are indicative of an underlying topic**. The first step is to create a joint embedding of document and word vectors. Once documents and words are embedded in a vector space the goal of the algorithm is to find dense clusters of documents, then identify which words attracted those documents together. Each dense area is a topic and the words that attracted the documents to the dense area are the topic words.

> Once you train the Top2Vec model you can:
> - ...
> - Get **hierarchical topics**.
> - Search topics by keywords.
> - Search documents by topic, by keywords.
> - Find similar words, similar documents.

Refered by [BERTopic](doc:2022/03/maartengr_bertopic_leveraging_)

2022-03-10 About

How to Build a Semantic Search Engine With Transformers and Faiss | by Kostas Stathoulopoulos | Towards Data Science

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2022-01-29 About

Semantic Search — Sentence-Transformers documentation

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2022-01-29 About

Semantic search through a vectorized Wikipedia (SentenceBERT) with the Weaviate vector search engine

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2021-12-05 About

Building a sentence embedding index with fastText and BM25 | by David Mezzetti | Towards Data Science

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2021-09-30 About

neuml/txtai: Build AI-powered semantic search applications

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txtai

2021-09-30 About

[1410.5859] Towards a Model Theory for Distributed Representations

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2021-06-10 About

Semantic Search with S-BERT is all you need

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2021-06-05 About

[2007.12603] IR-BERT: Leveraging BERT for Semantic Search in Background Linking for News Articles

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2021-04-12 About

raphaelsty/ckb: Contextual knowledge bases

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2020-11-09 About

[2009.00318] More is not Always Better: The Negative Impact of A-box Materialization on RDF2vec Knowledge Graph Embeddings

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2020-09-02 About

Disambiguating KBpedia Knowledge Graph Concepts

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2020-05-03 About

Martynas Jusevicius sur Twitter : "Is there a solution for entity recognition that would use a local #KnowledgeGraph to look for matches? Ideally any SPARQL datasource..."

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2020-03-13 About

Named Entity Recognition with Pytorch Transformers – Pierre-Yves Vandenbussche

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2019-12-11 About - Local Copy

Document analysis with machine learning

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2019-10-28 About

[1905.10070] Label-aware Document Representation via Hybrid Attention for Extreme Multi-Label Text Classification

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2019-06-22 About

Dandelion API - Semantic Text Analytics as a service

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2019-04-24 About

A Neural QA Model for DBpedia (GSoC 2019)

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2019-03-26 About

Using Machine Learning to Support Continuous Ontology Development (2010)

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2018-08-07 About

ML-knn: A lazy learning approach to multi-label learning (2007)

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a lazy learning approach derived from the traditional [k-nearest neighbors algorithm](tag:k_nearest_neighbors_algorithm)

> for each unseen instance, its K nearest neighbors in the training set are firstly identified. After that, based on statistical information gained from the label sets of these neighboring instances, i.e. the number of neighboring instances belonging to each possible class, [Maximum a posteriori (MAP)](tag:maximum_a_posteriori_estimation) principle is utilized to determine the label set for the unseen instance.

Implemented in [scikit-multilearn](http://scikit.ml/api/skmultilearn.adapt.mlknn.html), in [java](https://github.com/lefman/mulan-extended/blob/master/mulan/src/mulan/classifier/lazy/MLkNN.java)

> the first lazy approach proposed specifically for multi-label classification. This is also a binary relevance approach which considers each label independently as a binary classification problem. Instead of a standard k-NN method, however, MLkNN uses the maximum a-posteriori (MAP) (Kelleher et al., 2015) approach combined with k-NN. [src](https://pdfs.semanticscholar.org/af9b/33da37d290c063cd826ab5923d96892a9767.pdf)

2018-03-18 About

word2vec-api

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2017-06-09 About

Create a Domain Text Classifier Using Cognonto | Frederick Giasson

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2016-10-25 About

Using Cognonto to Generate Domain Specific word2vec Models | Frederick Giasson

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2016-09-29 About

Sphere Engineering - Machine Learning Solutions - QuickAnswers.io: a new algorithm

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2014-07-04 About

Extraction de la semantique

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2014-06-18 About

nltk - hierarchical classification + topic model training data for internet articles and social media - Stack Overflow

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2014-04-23 About

NERD: Named Entity Recognition and Disambiguation

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2013-07-10 About

LINDEN: Linking Named Entities with Knowledge Base via Semantic Knowledge

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2012-04-19 About

A Spectrometry of Linked Data

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Entity mining is still a troublesome open problem. In past years many approaches allowed to automate the generation of equivalence links between references using schema matching or various heuristics based on the recognition of similar property values. In contrast, few of them considered the analysis of the network of equivalence links (“equivalence network”) as an indication of the likelihood and strength of the equivalence.

Could a URI reference (URIRef) be thought as exactly “attached” to its referent? Could it make sense to talk about entity “identifiers” or would it be better to talk about more ambiguous “references”, i.e., placeholders for any model that satisfies the formal semantics of the Semantic Web (Hayes)? Booth observes that the aforementioned question, which in the past has been often regarded as fundamental in the debate about identity on the Web, is relatively unimportant. As long as an entity, identified by whatsoever URIRef, is associated to at least one description containing machine understandable information, this information can be automatically processed and used by applications.

2012-04-16 About