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10470results about "Semantic tool creation" patented technology

Conceptual factoring and unification of graphs representing semantic models

Techniques for factoring one or more source graphs into a composite graph containing nodes representing analogous elements of the source graphs and a variability graph containing nodes representing differences in the source graphs. The composite graph is made by taking analogous input trees from the source graphs and traversing the trees from top to bottom looking for nodes in each tree at each level that are analogous to the nodes at that level in the other input trees. The sets of analogous nodes are found by first automatically correlating the nodes in the level currently being examined. Correlation may, for example, be based on similar values of a property of the nodes being correlated. Representations of the sets of correlated nodes are then displayed to a user, who indicates which sets of correlated nodes are in fact analogous. The user may also indicate that the nodes in a set of correlated nodes are not analogous or that nodes that were found by the automatic correlation not to be autonomous are in fact. The analogous nodes are allocated to a corresponding node at a corresponding level in the composite graph; the other nodes are allocated to a set of anomalous nodes. One application for the techniques is managing graphs which are models of catalogs of items.
Owner:POPPET INT

Question and answer method based on knowledge map

The invention provides a question and answer method based on a knowledge map. The question and answer method based on a knowledge map provided in the invention is realized by subject entity matching,relationship matching and answer determination. The subject entity matching mainly comprises naming entity identification and entity linking. The naming entity identification is aimed at identifying naming entities such as names of people, names of places, and names of organizations in natural language questions q. The entity linking corresponds the identified naming entity to a certain entity inthe knowledge base, that is, finding out an entity s in triples; Relationship matching is to understand the semantics expressed by question q through natural language understanding technology, and match the relationship p in the triples (s, p, o) in the search space in order to determine the semantics of the question and its corresponding relationship with the knowledge base. The candidate subjectentity is obtained through entity identification and entity linking, and the relationship matching can obtain the candidate relationship, thereby obtaining several candidate triples; the answer determination is to rank the candidate triples according to entity recognition score, relationship match score, etc. to determine the final answer.
Owner:BEIHANG UNIV

Chinese natural language interrogative sentence semantization knowledge base automatic question-answering method

The invention discloses a Chinese natural language interrogative sentence semantization knowledge base automatic question-answering method. The method includes the following steps that Chinese natural language processing is performed on a fact type question input by a user, word segmentation, part-of-speech tagging and identification and expanding of a named entity are achieved, and a semantic dependency tree is generated; a generalization template and a semantic analysis technology are used for acquiring time, space, a fact entity, a fact object and the like in an interrogative sentence, then semantic processing is performed, composition element attributes relevant to all events in the interrogative sentence and values of the attributes are extracted, a plurality of 'attribute-value' pairs are generated, to-be-answered elements are substituted by interrogatives, and a complex fact triple set is formed; after a triple where a to-be-answered part is located is combined with other relevant fact triple sets to form knowledge base query with conditional constraints, and query matching based on similarity calculation is performed in a knowledge base, a result is extracted from the knowledge base, and a final answer is obtained. Fast and accurate query response to the knowledge base is achieved.
Owner:NANJING UNIV
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