Relationship Extraction: Fundamentals and Applications

By Fouad Sabry

What Is Relationship Extraction

The identification and categorization of semantic relationship mentions within a collection of artifacts, most commonly taken from text or XML documents, is necessary for the completion of a job known as relationship extraction. The process is quite similar to that of information extraction (IE), although IE also needs the elimination of repeated relations (disambiguation) and generally refers to the extraction of a wide variety of various relationships. The goal is extremely similar.

How You Will Benefit

(I) Insights, and validations about the following topics:

Chapter 1: Relationship Extraction

Chapter 2: Semantic Network

Chapter 3: Ontology (computer science)

Chapter 4: Text Mining

Chapter 5: Information Extraction

Chapter 6: Relational Data Mining

Chapter 7: Semantic Similarity

Chapter 8: Ontology Learning

Chapter 9: Knowledge Extraction

Chapter 10: Knowledge Graph

(II) Answering the public top questions about relationship extraction.

(III) Real world examples for the usage of relationship extraction in many fields.

(IV) 17 appendices to explain, briefly, 266 emerging technologies in each industry to have 360-degree full understanding of relationship extraction' technologies.

Who This Book Is For

Professionals, undergraduate and graduate students, enthusiasts, hobbyists, and those who want to go beyond basic knowledge or information for any kind of relationship extraction.

• Language: English
• Publisher: One Billion Knowledgeable
• Release date: Jul 5, 2023

Book preview

Chapter 1: Relationship extraction

The goal of a relationship extraction job is to identify and categorize references to semantic relationships within a collection of artifacts, such as text or XML files. Information extraction (IE) is a job with many similarities to this one, but IE also involves the elimination of redundant relations (disambiguation) and often refers to the extraction of a wide variety of connections.

In 1998, during the 7th Message Understanding Conference, the idea of connection extraction was presented for the first time. etc.

Researchers nowadays often approach relationship extraction as a classification issue when using machine learning technology. The researchers at Carnegie Mellon University created Never-Ending Language Learning, a semantic machine learning system that mines the internet for connections.

Relationship extraction may be accomplished in a number of ways, one of which is via the use of text. These techniques either leverage knowledge about the connection structure that has already been taught, or they learn the structure so that relationships may be revealed. utilizes simply the number of results from a search engine and Wikipedia pages to build basic ontologies.

Different formalisms/languages may be used to express the relationships. RDF is a language for representing data on the World Wide Web.

More recently, end-to-end systems have been presented that have great promise to achieve high performance by simultaneously learning to extract entity mentions and their semantic connections.

The majority of the systems that have been disclosed have proven their methodology on English-language datasets. Other languages' data and systems have been described, though; Russian and Vietnamese, for example.

To aid in the evaluation of connection extraction algorithms, researchers have created a number of datasets. The 2019 version of the document-level relationship extraction dataset DocRED is an example of such a dataset. It incorporates Wikidata connections with English Wikipedia articles. CodaLab has hosted a prediction competition using the dataset, which has been utilized by other academics.

{End Chapter 1}

Chapter 1: Semantic network

A semantic network, also known as a frame network, is a kind of knowledge base that uses a network to describe the semantic connections between ideas. This is a common method of representing information. It is a mapping or connection of semantic fields, represented as a directed or undirected graph with concepts as vertices and semantic interactions between concepts as edges. Instances of semantic networks include concept maps and graph databases. The most common representation for a semantic network is a set of triples.

Natural language processing technologies like semantic parsing rely on semantic networks.

Semantic networks, or directed acyclic graphs, have been used as a mnemonic device in logic for millennia. The Greek philosopher Porphyry's third-century AD commentary on Aristotle's categories is the oldest known usage of the term.

Richard H. Richens of the Cambridge Language Research Unit created the first computer implementation of the propositional calculus using Semantic Nets in 1956 as a interlingua for machine translation of natural languages.

Robert F. Simmons independently deployed a semantic network. Google's knowledge graph was dubbed Knowledge Graph in 2012.

As a means of semantic social networking, the semantic link network has been the subject of systematic research. Semantic nodes, linkages between nodes, and a space defining the semantics of nodes, links, and reasoning rules on semantic links make up its fundamental paradigm. Publication of the comprehensive theory and model occurred in 2004.

When your expertise is best represented as a web of interconnected ideas, a semantic network is the way to go.

The majority of semantic webs have a mental foundation. Additionally, they are made up of arcs and nodes that may be arranged in a hierarchical structure. Concepts of activation propagation, inheritance, and nodes as proto-objects were introduced by semantic networks.

Constructing semantic networks, sometimes called co-occurrence networks, requires the identification of keywords in the text, the calculation of co-occurrence frequencies, and the analysis of the networks to discover significant words and themes.

Here is some code demonstrating a simple semantic network implemented in Lisp by use of an association list.

(setq *database*

'((canary (is-a bird)

(color yellow)

(size small))

(penguin (is-a bird)

(movement swim))

(bird (is-a vertebrate)

(has-part wings)

(reproduction egg-laying))))

Using the assoc function with the key canary, one can get all data associated with the canary category.

WordNet, an English lexical database, is a good illustration of a semantic network. It does this by categorizing English words into synsets, defining them in broad terms, and keeping track of the many semantic relationships between synsets. These include meronymy (where A is a meronym of B if A is included in B), holonymy (where B is a holonym of A if B includes A), hyponymy (or troponymy) (where A is subordinate to B; A is a kind of B), hypernymy (where A is superordinate to B), synonymy (where A denotes the same as B), and antonymy (where A denotes the opposite of (A denotes the opposite of B).

Comparing WordNet to other semantic networks built using Roget's Thesaurus and word association tasks has been the focus of research