The shortest dictionary definition of semantics is: the study of meaning. The more complex explanation of this term would lead to a relationship that maps words, terms and written expressions into common sense and understanding of objects and phenomena in the real world. It is worthy to mention that objects, phenomena and relationships between them are language independent. It means that the same semantic network of concepts can map to multiple languages which is useful in automatic translations or cross-lingual searches.
In the proposed approach semantics will be modeled as a defined ontology making it possible for the web to “understand” and satisfy the requests and intents of people and machines to use the web content. The ontology is a model that encapsulates knowledge from specific domain and consists of hierarchical structure of classes (taxonomy) that represents concepts of things, phenomena, activities etc. Each concept has a set of attributes that represent the mapping of that particular concept to words and phrases that represents that concepts in written language (as shown at the top of the figure below). Moreover, the proposed ontology model will have horizontal relationships between concepts, e.g. the linguistic relationships (synonymy, homonymy etc.) or domain specific relationships (medicine, law, military, biological, chemical etc.). Such a defined ontology model will be called a Semantic Map and will be used in the proposed search engine. An exemplar part of an enriched ontology of beverages is shown in the figure below. The ontology is enriched, so that the concepts can be easily identified in text using attributes such as the representation of the concept in the written text.
The Semantic Map is an ontology that is used for bidirectional mapping of textual representation of concepts into a space of their meaning and associations. In this manner, it becomes possible to transform user queries into concepts, ideas and intent that can be matched with indexed set of similar concepts (and their relationships) derived from documents that are returned in a form of result set. Moreover, users will be able to precise and describe their intents using visualized facets of concept taxonomy, concept attributes and horizontal (domain) relationships. The search module will also be able to discover users’ intents based on the history of queries and other relevant factors, e.g. ontological axioms and restrictions. A potentially interesting approach will retrieve additional information regarding the specific user profile from publicly available information available in social portals like Facebook, blog sites etc., as well as in user’s own bookmarks and similar private resources, enabling deeper intent discovery.
Semantic Search Engine
The search engine will be composed of the following components:
- Connector – This module will be responsible for acquisition of data from external repositories and pass it to the search engine. The purpose of the connector is also to extract text and relevant metadata from files and external systems and pass it to further processing components.
- Parser – This module will be responsible for text processing including activities like: tokenization (breaking text into lexems – words or phrases), lemmatization (normalization of grammar forms), exclusion of stop-words, paragraph and sentence boundary detector. The result of parsing stage is structured text with additional annotations that is passed to semantic Tagger.
- Tagger – This module is responsible for adding semantic information for each lexem extracted from the processed text. Technically it refers to addition of identifiers to relevant concepts stored in the Semantic Map for each lexem. Moreover phrases consisting of several words are identified and disambiguation is performed basing on derived contexts. Consider the example illustrated in the figure.
- Indexer – This module is responsible for taking all the processed information, transformation and storage into the search index. This module will be enriched with methods of semantic indexing using ontology (semantic map) and language tools.
- Search index – The central storage of processed documents (document repository) structured properly to manage full text of the documents, their metadata and all relevant semantic information (document index). The structure is optimized for search performance and accuracy.
- Search – This module is responsible for running queries against the search index and retrieval of relevant results. The search algorithms will be enriched to use user intents (complying data privacy) and the prepared Semantic Map to match semantic information stored in the search index.
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