Abstract:
The present invention relates to a system and method for semantic level sentiment analysis. The system (100) comprises of a graph generator component (10), a semantic sentiment analyser component (20), a sentiment processor component (30), a sentiment dictionary (40), a sentiment taxonomy (50), a semantic sentiment patterns repository (60) and a propagation rules repository (70). The system (100) accepts text data as input and analyses sentiment in the text. The method enables semantically valid sentiment in terms of the entire text as well as the individual entities in the text.
Abstract:
A semantic web constructor system (100) supported by a plurality of web crawlers (112) in a World Wide Web upon declaring a plurality of trustworthy websites is provided, characterized in that, the system (100) includes at least one web crawler controller (110) engagable to manage the plurality of web crawlers (112), a semantic web database (116) connectable to the plurality of web crawlers (112) and a plurality of data building editors (122, 124, 126) connectable to the at least one web crawler controller (110) wherein a semantic browser (120) is further connectable to the semantic web database (116) to receive at least one natural language query from at least one user.
Abstract:
The present invention relates to a method for reasoning knowledge that is distributed across a plurality of linked knowledge bases. It handles queries in two ways. First, it handles a query by determining whether or not a fact is true. Secondly, it handles a query by returning query results that are not directly explicit within the triples of the queries knowledge bases which are the subjects, predicates and objects.
Abstract:
A system (100) for distributed querying of linked semantic webs (110) comprising at least one LOD ontologies index (120) comprising LOD ontologies and metadata relating to said LOD ontologies; at least one concept index (130) comprising concepts and corresponding URIs; at least one relation index (140) comprising relations and corresponding URIs; query interface (160) for entering SPARQL queries; and a distributed query engine (150) in communication with said LOD ontologies index (120), concept index (130) and relations index (140) and adapted to receive queries from said query interface (160); characterised in that said distributed query engine (150) is adapted to: parse and rewrite queries received from said query interface (160) and generate a plurality of sub-queries; identify dependencies within said sub-queries and chunk sub-queries based on ontology; execute sub-queries by sending to relevant source ontology; and merge results obtained from execution of said sub-queries.
Abstract:
A system for semantic images browsing and navigation for visualizing and learning of human anatomy comprises an information database (13) with records of medical knowledge bases; a first display interface (11) which allows user to manipulate an ontology for requested concepts on subject of interest, wherein the concepts and its knowledge bases in the information database (13) having tags which are harmonized and synchronized; and a base server (15) connected to the information database (13) and first display interface (11). The system (10) further comprises a graphical database (14) containing three-dimensional images of human anatomy; a second display interface (12) which allows user to manipulate images of subject of interest, wherein the images having tags which are harmonized and synchronized; and a graphic engine (16) connected to the graphical database (14) and second display interface (12), the graphic engine (16) is linked to the base server (15) for fetching the corresponding concept or image upon user requests.
Abstract:
A method (100) and an apparatus for user assisted deductive reasoning using a plurality of semantic network refinement iterations (102) comprises performing a user assisted query (104) to ascertain at least one valid node from a plurality of nodes of a semantic network, reducing a solution space of the semantic network (106) by disregarding at least one invalid node from the plurality of nodes of the semantic network and pruning the semantic network (108) based on the at least one valid node from the plurality of nodes of the semantic network. Pruning the semantic network (108) further comprises querying a knowledge base (200) to ascertain a plurality of hypothesis and a corresponding plurality of antecedents for the at least one valid node and constructing a refined semantic network (214) based on the plurality of hypothesis and the corresponding plurality of antecedents for the at least one valid node.