Scope’s team of subject matter experts (SMEs) and certified Taxonomists provide highly customized and effective knowledge modeling services – taxonomies, thesauri and ontology – to help improve content search & discoverability, online navigation, and effective storage & retrieval of content assets.
Our Experience – Scope has nearly 15 years of experience in knowledge modeling business serving world’s largest publishers, standards developing organization (SDOs), aggregators. scholarly societies, corporate libraries and information providers.
Unique Assisted Automation Model – Scope uses a unique blend of subject matter experts and knowledge modeling platform tools to deliver knowledge models that precisely ensure adequate coverage of the content, incorporate synonyms for better recall during search and make sure there is no ambiguities in concept representation due to linguistic issues like homographs. We also leverage our vast ecosystem of experts from the industry and academia to ensure currency and relevancy of the subject terms we use in our knowledge models.
Subject Domains (indicative list) – Scope has delivered taxonomy, thesaurus and ontologies for multiple domains including Engineering, Material Sciences, Oil & Gas, Physical Science, Medical, Automotive, Nanotechnology and Life Science domains. Scope delivers the controlled vocabularies in customizable XML, SKOS, RDF and OWL formats to enable easy integration with clients’ content environment.
Taxonomies — Taxonomies provide the hierarchical categorization and classification of content into subject categories and also help to categorize and classify products and brands represented in online directories and product catalogues. Read more about our Taxonomy services
Thesauri — Thesaurus provide the expanded terms of the basic taxonomic classification (Broader, Narrower) to incorporate user variants and Related concepts ( Equivalence , associative) to improve the recall efficiency of the search and retrieval process. Read more about our Thesaurus services
Ontology — Compared to thesauri, ontologies can define concepts with their semantic meaning and relations to other concepts for better cross linking between multiple vocabularies and help search applications to interpret, infer and derive complex relationships from a variety of data silos and thus facilitate conceptual search and handle natural language queries on a very large corpus of data. Read more about our Ontology services
How to choose the right knowledge model?
Each knowledge Model brings its own set of benefits. See a brief description below.
|Provides ‘Basic’ discoverability||Helps in ‘Targeted’ discoverability||Provides ‘Extended’ discoverability||Enables ‘Networked’ discoverability|
|Enables broad level browsing for subjects and named entities||More contextual precision through guided navigation using a hierarchical subject categorization and progressive filtering||Improving both precision and recall through synonyms||Knowledge discovery by defining and linking concepts to identify and visualize related documents|
|Facilitates search relevancy & usefulness||Facilitates search expansion and completeness||Facilitates semantic search and Related content|
Semantic metadata enables content intelligence by extracting domain-specific entities and concepts from content, and relating them in a meaningful way to identify related content and facilitate intelligent answers to user queries.
Semantic enrichment services from Scope aim to enhance content/data by adding contextual information by tagging, categorizing and/or classifying data in relationship to each other. Our semantic enrichment services enable users to find more relevant information, receive deep insight and provide decision-making support.
With nearly 15 years of association with several leading publishers, online digital libraries and information providers, Scope has recognized the need for semantic enrichment of content. Scope uses a combination of ML and NLP based algorithms, ontologies and subject matter experts (SMEs) to deliver semantic enrichment services. We extract concepts from content using advanced AI based algorithms and ontologies to identify relationships across concepts and express them in the form of Triples (Subject-Predicate-Object). Further curation by SMEs help improve the contextual accuracy of such relationships. This iterative process also helps enhance ML and improve the level of accuracy in our automatic text mining solutions. Scope’s proprietary semantic workflow platform employs sentence extraction, text classification, parts of speech (POS) tagging, controlled vocabulary (CV) based semantic tagging for entity and concept extraction and generates relationships among concepts using ontologies.
Scope’s Semantic Enrichment architecture consists of the following components:
Scope focuses on using domain-specific controlled vocabularies (CVs) such as taxonomies, thesauri and ontologies for semantic enrichment. This is further complemented by the domain knowledge of SMEs from the industry and academia. Based on client’s requirements, Scope can either process existing CVs or build new CVs by extracting keywords from source documents and classifying them into hierarchical structures. CVs can also be built by adopting an open source CV and further updating it with the keywords extracted from the source documents.
Semantic Annotation involves the extracting and tagging of Named Entities and Concepts from source documents using ML, NLP and statistical algorithms and also based on the CVs. The relationships between concepts are built using Ontologies and NLP techniques. Triplets (Subject/Predicate/Object) extracted from each document are stored into a RDF store, which is referred during the annotation process to extract similar terms and relationships when further documents are passed through Semantic Annotation platform.
Scope team can supply back the output in industry-acceptable standards such as RDF XML, RDF store (N3 format), SKOS and OWL formats. This also facilitates seamless integration with the content management systems of the clients.
Entity Extraction: Named entities as well as domain specific concepts are automatically extracted using Scope’s proprietary AI driven indexing algorithm and controlled vocabularies. SMEs develop the knowledge framework, which is integrated into our semantic enrichment workflow platform for entity and concept extraction.
Semantic Tagging: Tagging XML documents with conceptual tags using domain specific mark up languages, such as CML (Chemical Markup Language), MatML (Material Markup Language), GML (Geographic Markup Language), etc.
Semantic Annotation: Annotating the texts in the documents based on concepts in the ontologies, allowing relevant content to be linked.
Semantic Indexing: Indexing documents with domain-specific concepts using controlled vocabularies such as taxonomy and thesauri to provide the domain context to the terms.
Semantic Linking: Identifying the semantic relationships across concepts and using such relationships to link content within the legacy content of the publishers and also from external open access databases. The relationships among concepts are extracted using NLP techniques or Ontologies, and delivered as Triple Stores.
Semantic Authoring: Authoring abstracts that could extract and explain concepts described in a document, and delivering such abstracts in a structured format with pre-defined concept labels.
Scope’s Semantic Processing – Value Chain
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