A structured model metametadata technique to enhance semantic searching in metadata repository WRAP: Warwick Research Archive Portal

Certain special codes are used to indicate special characters or accented

letters not available in the ASCII character set, as listed below. The one existing example of embedded semantics is the text

character markup introduced in the CIF Version 1.0 specification and

summarised in paragraphs below. The

specification is silent on which fields should be interpreted according to

these markup conventions, but the published examples suggest that they may

be used in any character field in a CIF data file except as prohibited by

a dictionary directive.

It remains to be discovered how these semantic maps change with different languages and cultures – one of the aims of the continuing research. With the development of semantic search engines, there needs to be a holistic effort that considers the site’s overall topical offering and semantic structure. If users are searching for your service using location-based terms, such as ‘phone repairs near me’, you can provide more contextually relevant information using location service pages.

A structured model metametadata technique to enhance semantic searching in metadata repository

There are a number of online tools, which will help to determine the sorts of words you should be targeting with your latent semantic optimisation efforts, based on your chosen keyword(s). For example, within the Google Ads platform, the Keyword Planner tool includes an option for “additional keywords”, which should provide some ideas. A free alternative to this is LSIGraph, which produces a list of words that are semantically linked to your main keyword. Polymorphic and metamorphic malware use code obfuscation techniques to construct new variants which preserve thesemantics of the original but change the code syntax, evading current compiled code based detection methods. Dynamic slicing is a technique that, given a variable of interest within a program, isolates a relevant subset of executed program code that influences that variable. Using dynamic slicing to condition semantic traces identifies ‘core’ behaviours that, as part of an overall semantics based approach, has the potential to play a significant rˆole in detecting difficult malware variants.

If you find that you have given insufficient importance to relevant target words, you can then adjust your content accordingly. While you should not rely solely on TF-IDF for your LSO efforts, it is an advantageous technique to turn to. Topically relevant content describes pages that extensively cover a topic and all related, relevant queries common with search engine users. Google will tend to prioritise pages that contain in-depth content about a topic and popular sub-topics. Semantic SEO is a technique that can allow your law firm’s website’s pages to be optimised for topics rather than specific keywords. This can be a great asset, as it allows for content to be written in a much more natural way without having to worry about presenting multiple iterations of your target keyword or related long-tail keywords.

What is Semantic Field

Semantic SEO is the practice of creating more meaningful and relevant web content around particular topics. With all these novelties, the search engine par excellence tries to reward quality texts in content marketing and penalize repetitions, even when synonyms are used. The codes indicated above are designed to

refer to special characters not expressible within the CIF character set, and

the initial specification did not permit markup for typographic style such as

italic or bold-face type. Since there are very few, if any, CIFs which contain text fields and comments

beginning this way, in most cases, it is reasonable to adopt the policy of doing

this processing unless it is disabled. Where the attributes of a data value are not available in a

dictionary listing, it may be assumed that a character string

interpretable as a number should be taken to represent an item of type

numb.

If you can cover a greater number of related topics on your page, you will entice users to stay on the page and avoid looking elsewhere for more specific information. But what’s great about Latent Semantic Indexing is that the search engines will also pick out phrases in your content that semantic techniques you may not have intended to rank for. If you have a broad range of topic-related content then you’ll be ranked for a broad range of phrases around your topic. Latent Semantic Indexing (LSI) is a tool of search engines to work out what words and phrases your content should be ranked for.

Semantic Techniques in Quantum Computation

It is done by utilizing keywords that are related to each other, either through similar meanings or through a more abstract relationship, for example, a topic. The character string [local] (including the literal

bracket characters) is reserved for local use. This allows

experimentation with data items in a strictly local context, i.e.

in cases where the CIF is not intended for interchange with any other

user.

This section covers a typical real-life semantic analysis example alongside a step-by-step guide on conducting semantic analysis of text using various techniques. AB – he performance of state-of-the-art Deep Learning models heavily depends on the availability of well-curated training and testing datasets that sufficiently capture the operational domain. Data augmentation is an effective technique in alleviating data scarcity, reducing the time-consuming and expensive data collection and labelling processes. Despite their potential, existing data augmentation techniques primarily focus on simple geometric and colour space transformations, like noise, flipping and resizing, producing datasets with limited diversity. When the augmented dataset is used for testing the Deep Learning models, the derived results are typically uninformative about the robustness of the models.

The Open University

We address this gap by introducing GENFUZZER, a novel coverage-guided data augmentation fuzzing technique for Deep Learning models underpinned by generative AI. We demonstrate our approach using widely-adopted datasets and models employed for image classification, illustrating its effectiveness in generating informative datasets leading up to a 26% increase in widely-used coverage criteria. N2 – he performance of state-of-the-art Deep Learning models heavily depends on the availability of well-curated training and testing datasets that sufficiently capture the semantic techniques operational domain. Semantic SEO is the process of building more meaning and relevance into web content, providing topical breadth and depth to help search engines understand your content more accurately. Semantic search is concerned with understanding the meaning of web-based information and search queries more accurately, with the ultimate aim of processing language and information in the same way a human could. A semantic field is a technique used when writing with the aim of creating or maintaining a certain more concrete image in the minds of users.

The upshot of Latent Semantic Indexing is that the search engines (but especially Google) can pick up the intent of your content from the words and language that you use. These models assign each word a numeric vector based on their co-occurrence patterns in a large corpus of text. The words with similar meanings are closer together in the vector space, making it possible to quantify word relationships and categorize them using mathematical operations.

Semantic HTML

The evaluation showed very promising results, and the viability of the system for real life. This book is a guide to the combination of the Internet of Things (IoT) and the Semantic Web, covering a variety of tools, technologies and applications that serve the myriad needs of the researchers in this field. It provides a multi dimensional view of the concepts, tools, techniques and issues that are involved in the development of semantics for the Web of Things. AB – The document text similarity measurement and analysis is a growing application of Natural Language Processing. This paper presents the results of using different techniques for semantic text similarity measurements in documents used for safety-critical systems. The research objective of this work is to measure the degree of semantic equivalence of multi-word sentences for rules and procedures contained in the documents on railway safety.

These pages should be internally linked to and from one another, and most importantly, to the main topic piece. Topic clusters cover a topic and wide range of subtopics across a series of interlinked pages. You’ll see Google’s Knowledge Graph in action almost any time you https://www.metadialog.com/ conduct a celebrity search, with a panel of information that details their birthdate, marital status, spouse, featured movies, fellow cast members and more. These considerations mirror the semantic cues we use in everyday conversation to understand what is being said.

NITELIGHT supports end users by providing a set of graphical notations that represent semantic query language constructs. These enable the user to combine ontology navigation capabilities with graphical query visualisation techniques. The attributes of data items defined in CIF dictionaries serve to

direct crystallographic applications in the retrieval, storage and

validation of relevant data. In principle a CIF might include as data

items suitably encoded fields representing data suitable for manipulation

by text processing, image, spreadsheet, database or other applications.

• When completing keyword research for semantic SEO, the most important thing to remember is that you are targeting topics, not keywords.
• It aims to understand the relationships between words and expressions, as well as draw inferences from textual data based on the available knowledge.
• Topic clusters cover a topic and wide range of subtopics across a series of interlinked pages.
• A free alternative to this is LSIGraph, which produces a list of words that are semantically linked to your main keyword.
• You will likely find that the search results are identical, even though both search terms are formed differently – this is because Google understands that, semantically, they share the same meaning under the same topic.

The increasing popularity of 3D technologies is having an impact on the amount of content that is being produced by users of these technologies. Witnessing the explosion of content such as images, music and videos available on the web, it is not difficult to predict that 3D will be the next type of content to undergo this effect. The research community has been taking action to ensure 3D content can be stored and managed in databases or repositories in order to be accessible to a wide variety of users. Idiomatic expressions are challenging because they require identifying idiomatic usages, interpreting non-literal meanings, and accounting for domain-specific idioms. Internal links are used to help users easily navigate across pages of your site, but are also used by search engine bots to crawl related pages.

When semantic elements aren’t used, the page has no structure or definition available to assistive technologies. A similar aim which can also massively impact your rankings is Voice Search Optimisation. Following Hummingbird and other similar algorithm updates, using variations of your target keywords has had a variable amount of efficiency. If a user is unsuccessful in finding the complete answer to their query on a page, they will move on to the next result. By allowing for more accurate translations that consider meaning and context beyond syntactic structure.