Semantic matching is a technique used in computer science to identify information which is semantically related.
Given any two graph-like structures, e. For example, applied to file systems it can identify that a folder labeled "car" is semantically equivalent to another folder "automobile" because they are synonyms in English.
This information can be taken from a linguistic resource like WordNet. In the recent years many of them have been offered. These sentences are translated into a formal logical formula according to an artificial unambiguous language codifying the meaning of the node taking into account its position in the graph. For example, in case the folder "car" is under another folder "red" we can say that the meaning of the folder "car" is "red car" in this case.
This is translated into the logical formula "red AND car". In our example the algorithm will return a mapping between "car" and "automobile" attached with an equivalence relation. Information semantically matched can also be used as a measure of relevance through a mapping of near-term relationships. Such use of S-Match technology is prevalent in the career space where it is used to gauge depth of skills through relational mapping of information found in applicant resumes.
Semantic matching represents a fundamental technique in many applications in areas such as resource discovery, data integration, data migrationquery translation, peer to peer networks, agent communication, schema and ontology merging. Its use is also being investigated in other areas such as event processing. Interoperability among people of different cultures and languages, having different viewpoints and using different terminology has always been a huge problem.
Especially with the advent of the Web and the consequential information explosion, the problem seems to be emphasized. People face the concrete problem to retrieve, disambiguate and integrate information coming from a wide variety of sources.
From Wikipedia, the free encyclopedia. Semantics Linguistic Logical. Lexical lexis lexicology. Statistical Structural. Prototype theory Force dynamics.Large-scale relational learning becomes crucial for handling the huge amounts of structured data generated daily in many application domains ranging from computational biology or information retrieval, to natural language processing.
In this paper, we present a new neural network architecture designed to embed multi-relational graphs into a flexible continuous vector space in which the original data is kept and enhanced. The network is trained to encode the semantics of these graphs in order to assign high probabilities to plausible components. We empirically show that it reaches competitive performance in link prediction on standard datasets from the literature as well as on data from a real-world knowledge base WordNet.
In addition, we present how our method can be applied to perform word-sense disambiguation in a context of open-text semantic parsing, where the goal is to learn to assign a structured meaning representation to almost any sentence of free text, demonstrating that it can scale up to tens of thousands of nodes and thousands of types of relation. Multi-relational data, which refers to graphs whose nodes represent entities and edges correspond to relations that link these entities, plays a pivotal role in many areas such as recommender systems, the Semantic Web, or computational biology.
Relations are modeled as triplets of the form subject, relation, objectwhere a relation either models the relationship between two entities or between an entity and an attribute value; relations are thus of several types. Such data sources are equivalently termed multi-relational graphs. They can also be represented by 3-dimensional tensors, for which each slice represents an adjacency matrix for one relation.
Multi-relational graphs are popular tools for encoding data via knowledge bases, semantic networks or any kind of database following the Resource Description Framework RDF format. Footnote 2 or natural language processing WordNetFootnote 3 to name a few. Social networks can also be represented using RDF.
In spite of their appealing ability for representing complex data, multi-relational graphs remain complicated to manipulate for several reasons. First, interactions are of multiple types and heterogeneous various frequencies, concerning different subsets of entities, etc.
In addition, most databases have been built either collaboratively or partly automatically. As a consequence, data is noisy and incomplete: relations can be missing or be invalid, there can be redundancy among entities because several nodes actually refer to the same concept, etc.
Finally, most multi-relational graphs are of very large dimensions in terms of numbers of entities and of relation types: Freebase contains more than 20 millions entities, DBpedia is composed of 1 billion triplets linking around 4 millions entities, GeneOntology contains more than k verified biological entities, etc.
In this paper, we propose a new model to learn multi-relational semantics, that is, to encode multi-relational graphs into representations that capture the inherent complexity in the data, while seamlessly defining similarities among entities and relations and providing predictive power.
Our work is based on an original energy function, which is trained to assign low energies i. This energy function, termed semantic matching energyrelies on a compact distributed representation: all elements entity and relation type are represented into the same relatively low e. The embeddings are learnt by a neural network whose particular architecture and training process force them to encompass the original data structure.
Unlike in previous work, in this model, relation types are modeled similarly as entities. In this way, entities can also play the role of relation type, as in natural language for instance, and this requires less parameters when the number of relation types grows. We show empirically that this model achieves competitive results on benchmark tasks of link prediction, i. We also demonstrate the flexibility and scalability of the semantic matching energy by applying it for word-sense disambiguation WSD.
The model can successfully be trained on various heterogeneous data sources knowledge bases, free text, etc. On two different evaluation test sets, the proposed approach outperforms both previous work for learning with multi-relational data and standard methods for unsupervised WSD. Note that this paper extends a shorter version Bordes et al.
However, the previous paper was only focused on the application to word-sense disambiguation, whereas the present paper has a wider scope and considers more problems involving multi-relational data. New elements are provided: a fresh and cleaner form of the bilinear formulation, new experiments comparing to the state-of-the-art in link prediction, entity ranking and WSD, a more comprehensive literature review, and more details on the model formulation and the training procedure.
The paper is organized as follows. Extensive experimental results are given in Sect. Finally, the application to WSD is described in Sect.
Several methods have been explored to represent and encode multi-relational data, such as clustering approaches. Hence, Kemp et al.How do you feel when candidates leave your website?
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Get the best talent by extracting talent through skill mapping. Enhance your search capabilities.Phono-semantic matching PSM is the incorporation of a word into one language from another, often creating a neologismwhere the word's non-native quality is hidden by replacing it with phonetically and semantically similar words or roots from the adopting language.Quartet competitions
Thus, the approximate sound and meaning of the original expression in the source language are preserved, though the new expression the PSM in the target language may sound native. Phono-semantic matching is distinct from calquingwhich includes semantic translation but does not include phonetic matching i.
A semantic matching energy function for learning with multi-relational data
At the same time, phono-semantic matching is also distinct from homophonic translationwhich retains the sound of a word but not the meaning. The term "phono-semantic matching" was introduced by linguist and revivalist Ghil'ad Zuckermann.Vscode scroll
Examples of such mechanisms are phonetic matching, semanticized phonetic matching and phono-semantic matching. Zuckermann concludes that language plannersfor example members of the Academy of the Hebrew Languageemploy the very same techniques used in folk etymology by laymenas well as by religious leaders. Zuckermann analyses the evolution of the word artichoke. Arabic has made use of phono-semantic matching to replace blatantly imported new terminology with a word derived from an existing triliteral root.
Examples are:. A number of PSMs exist in Dutch as well. One notable example is hangmat "hammock"which is a modification of Spanish hamacaalso the source of the English word.
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Natively, the word is transparently analysed as a "hang-mat", which aptly describes the object. A few PSMs exist in English. The French word chartreuse Carthusian monastery was translated to the English charterhouse. The French word choupiqueitself an adaptation of the Choctaw name for the bowfinhas likewise been Anglicized as "shoepike",  although it is unrelated to the pikes.
The French name for the Osage orangebois d'arc lit. The Prayer Book 's runagates in Psalm 68 derive from phono-semantic matching between Latin renegatus and English run agate.
The Finnish compound word for "jealous" mustasukkainen literally means "black-socked" musta "black" and sukka "sock". However, the word is a case of a misunderstood loan translation from Swedish svartsjuk "black-sick". The Finnish word sukka fit with a close phonological equivalent to the Swedish sjuk. Mailhammer "applies the concepts of multisourced neologisation and, more generally, camouflaged borrowing, as established by Zuckermann a to Modern German, pursuing a twofold aim, namely to underline the significance of multisourced neologisation for language contact theory and secondly to demonstrate that together with other forms of camouflaged borrowing it remains an important borrowing mechanism in contemporary German.
In modern Japanese, loanwords are generally represented phonetically via katakana.
However, in earlier times loanwords were often represented by kanji Chinese charactersa process called ateji when used for phonetic matching, or jukujikun when used for semantic matching.
Some of these continue to be used; the characters chosen may correspond to the sound, the meaning, or both. In most cases the characters used were chosen only for their matching sound or only for their matching meaning. In some cases, however, the kanji were chosen for both their semantic and phonetic values, a form of phono-semantic matching.Lang101x: Phono-Semantic Matching
The characters can mean "wings coming together", as the pointed capa resembles a bird with wings folded together. PSM is frequently used in Mandarin borrowings. Often in phono-semantic matching, the source-language determines both the root word and the noun-pattern. This makes it difficult to determine the source language's influence on the target-language morphology. The last part corresponds to the Latin donum "gift". According to Zuckermann, PSM has various advantages from the point of view of a puristic language planner : .
An expressive loan is a loanword incorporated into the expressive system of the borrowing language, making it resemble native words or onomatopoeia. Expressive loanwords are hard to identify, and by definition, they follow the common phonetic sound change patterns poorly. The difference to a folk etymology is that a folk etymology or eggcorn is based on misunderstanding, whereas an expressive loan is changed on purpose, the speaker taking the loanword knowing full well that the descriptive quality is different from the original sound and meaning.
Stack Overflow for Teams is a private, secure spot for you and your coworkers to find and share information. I have this problem of matching two strings for 'more general', 'less general', 'same meaning', 'opposite meaning' etc. At a glance it appears to me that S-match will do the job. If I use word2vec or dl4jI guess it can give me the similarity scores. But does it also support telling a string is more general or less general than the other? The current usage of machine learning methods such as word2vec and dl4j for modelling words are based on distributional hypothesis.
They train models of words and phrases based on their context. There is no ontological aspects in these word models. At its best trained case a model based on these tools can say if two words can appear in similar contexts. That is how their similarity measure works. The Mikolov papers ab and c which suggests that these models can learn "Linguistic Regularity" doesn't have any ontological test analysis, it only suggests that these models are capable of predicting "similarity between members of the word pairs".
This kind of prediction doesn't help your task. These models are even incapable of recognising similarity in contrast with relatedness e. I would say that you need an ontological database to solve your problem. More specifically about your examples, it seems for String 1 and String 2 in your examples:. You are trying to check entailment relations in sentences:. In your two first examples, you can probably use semantic knowledge bases to solve the problem.
But your third example will probably need a syntactical parsing before understanding the difference between two phrases.
For example, these phrases:. It needs a logical understanding to solve your problem. However, you can analyse that based on economy of languageadding more words to a phrase usually makes it less general. Longer phrases are less general comparing to shorter phrases.
It doesn't give you a precise tool to solve the problem, but it can help to analyse some phrases without special words such as allgeneral or every.S-Match is a semantic matching framework, which provides several semantic matching algorithms and facilities for developing new ones. S-Match was heavily used inside KnowDive group for several years to conduct experiments in semantic matching. The goal of this project is to make S-Match available for the community by releasing it under a permissive open source license.
Currently S-Match contains implementations of several semantic matching algorithms. It contains implementation of the original S-Match semantic matching algorithm, as well as minimal semantic matching algorithm and structure preserving semantic matching algorithm. Original S-Match semantic matching algorithm is a general purpose matching algorithm, very customizable and suitable for many applications. Minimal semantic matching algorithm exploits additional knowledge encoded in the structure of the input and capable of producing minimized mapping and maximized mapping.
Maximized mapping contains all possible links and is well suited for consumption by applications which are not aware of semantics of lightweight ontologies. Structure preserving semantic matching algorithm is an algorithm well suited for matching API and database schemas.
It matches the inputs distinguishing between structural elements such as functions and variables. S-Match project is hosted on SourceForge and we welcome everybody to join and contribute. About S-Match Join the Project. About S-Match Framework S-Match is a semantic matching framework, which provides several semantic matching algorithms and facilities for developing new ones.
Algorithms Currently S-Match contains implementations of several semantic matching algorithms. Where Does It Come From? Join the Project S-Match project is hosted on SourceForge and we welcome everybody to join and contribute.Skip to search form Skip to main content You are currently offline.
Some features of the site may not work correctly. Matching two texts is a fundamental problem in many natural language processing tasks. Then a convolutional neural network is utilized to capture rich matching patterns in a layer-by-layer way. We show that by resembling the compositional hierarchies of patterns in image recognition, our model can successfully identify salient signals such as n-gram and n-term matchings… Expand Abstract.
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