Automatic semantic annotation with KIM

Research in Computing Science, 2019

Due to the needs to improve the information search process, new strategies have been created to enhance searches. The semantic search performs the search by means of meaning instead of literals. The semantic search in unstructured documents requires to formalize knowledge through an annotation semantic process. Some annotation proposals use natural language processing tools, ontologies to link document terms; others use the similarity of entities through the weight of the edges, association between pair of concepts or the ontology structure. In this paper we present an alternative for semantic annotation in unstructured documents by semantic context extraction of entities. In the approach we detect the named entities through a data dictionary created from Wikipedia and link the instances in the ontology. The context extraction strategy is based on the concepts similarity; each term is associated with an instance of the ontology and the similarity between relationships explicit is measured by the combination of two types of measures: the association between each pair of concepts and the weight of the relationships. The approach was tested with two ontologies and two datasets in news and business, respectively.

The approach towards Semantic Web Information Extraction (IE) presented here is implemented in KIM -a platform for semantic indexing, annotation, and retrieval. It combines IE based on mature text engineering platform (GATE 1 ) with Semantic Web-compliant knowledge representation and management. The cornerstone is automatic generation of namedentity (NE) annotations with class and instance references to a semantic repository. Simplistic upper-level ontology, providing detailed coverage of the most popular entity types, is designed and used. A knowledge base (KB) with quasi-exhaustive coverage of real-world entities of general importance is maintained, used, and constantly enriched. Extensions of the ontology and KB take care of handling all the lexical resources used for IE, most notable, instead of gazetteer lists, aliases of specific entities are kept together with the latter in the KB.

2012

The aim of this paper is to present a system for semantic text annotation called Inforex. Inforex is a web-based system designed for managing and annotating text corpora on the semantic level including annotation of Named Entities (NE), anaphora, Word Sense Disambiguation (WSD) and relations between named entities. The system also supports manual text clean-up and automatic text pre-processing including text segmentation, morphosyntactic analysis and word selection for word sense annotation. Inforex can be accessed from any standard-compliant web browser supporting JavaScript. The user interface has a form of dynamic HTML pages using the AJAX technology. The server part of the system is written in PHP and the data is stored in MySQL database. The system make use of some external tools that are installed on the server or can be accessed via web services. The documents are stored in the database in the original format ― either plain text, XML or HTML. Tokenization and sentence segment...

ArXiv, 2016

People are producing more written material then anytime in the history. The increase is so high that professionals from the various fields are no more able to cope with this amount of publications. Text mining tools can offer tools to help them and one of the tools that can aid information retrieval and information extraction is semantic text annotation. In this report we present Marvin, a text annotator written in Java, which can be used as a command line tool and as a Java library. Marvin is able to annotate text using multiple sources, including WordNet, MetaMap, DBPedia and thesauri represented as SKOS.

Web Semantics: Science …, 2006

2007

IAEME PUBLICATION, 2016

Semantic annotation of web pages is the state of art technology for achieving the unified objective of attaining Semantic web Universe, which enables sharing, and reusing the document content beyond the boundaries and applications. Web is a treasury of knowledge and efficient tools should be designed to explore the structured and unstructured data. Annotating million of web pages manually is an impossible task. For high information retrieval rates, automatic annotation of documents is mandatory. Metadata is added to the web pages to make it intelligent for processing in content based intelligent applications. This paper analyses the problems with the current Semantic annotation systems and proposes a new Ontology based Automatic annotation system Framework. Ontology based semantic annotation is one of the best methods for extracting data from the Knowledge Base. The integration of Modified Manning’s Sentence boundary detection algorithm and Noun Phrase Collocation algorithm and classification using machine learning techiques in the Information Extraction module, and developing a new data model and ontology for Structured Ontology engineering model is contributed in this paper. Annotation module annotates the output of the information extraction module with the aid of ontologies and dictionaries and stores the resultant annotated data as RDF triples in the Annotation database. Reasoning is made on the Annotated data by the RDF repository interface. FIOBODA is abbreviated as the Financial Instruments ontology based open document annotation. Web pages extracted from the Financial securities domain are mapped with the Finance ontology to extract the subject, predicate and object. SVM classifier is used to classify the correct and incorrect annotations. The correct output annotation data is stored in Annotation data base and RDF repository for later use. The proposed framework to an extent solves the problem of knowledge bottleneck due to its reusability and interoperability features.

… Web and Web Services, 2003

The approach towards Semantic Web Information Extraction (IE) presented here is implemented in KIM-a platform for semantic indexing, annotation, and retrieval. It combines IE based on the mature text engineering platform (GATE 1) with Semantic Web-compliant knowledge representation and management. The cornerstone is automatic generation of named-entity (NE) annotations with class and instance references to a semantic repository. Simplistic upper-level ontology, providing detailed coverage of the most popular entity types (Person, Organization, Location, etc.; more than 250 classes) is designed and used. A knowledge base (KB) with de-facto exhaustive coverage of real-world entities of general importance is maintained, used, and constantly enriched. Extensions of the ontology and KB take care of handling all the lexical resources used for IE, most notable, instead of gazetteer lists, aliases of specific entities are kept together with them in the KB. A Semantic Gazetteer uses the KB to generate lookup annotations. Ontologyaware pattern-matching grammars allow precise class information to be handled via rules at the optimal level of generality. The grammars are used to recognize NE, with class and instance information referring to the KIM ontology and KB. Recognition of identity relations between the entities is used to unify their references to the KB. Based on the recognized NE, template relation construction is performed via grammar rules. As a result of the latter, the KB is being enriched with the recognized relations between entities. At the final phase of the IE process, previously unknown aliases and entities are being added to the KB with their specific types.

While much of a company's knowledge can be found in text repositories, current content management systems have limited capabilities for structuring and interpreting documents.

When working with large corpora of documents it is hard to comprehend and process all the information contained in them. Standard search engines usually rely on word matching and do not take the structure within the corpus into account. We try to overcome that by automatic extraction of topics covered within the documents, visualization of the corpus and semi-automatic construction of topic ontology.