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Extracting Ontological Knowledge from Textual Descriptions
Now, ambiguity due to different possible lexicalizations of sentences and semantic ambiguity present in sentences are challenges in this context.
Difficulty-level Modeling of Ontology-based Factual Questions
Semantics based knowledge representations such as ontologies are found to be very useful in automatically generating meaningful factual questions.
Ontology Verbalization using Semantic-Refinement
We propose a rule-based technique to generate redundancy-free NL descriptions of OWL entities. The existing approaches which address the problem of verbalizing OWL ontologies generate NL text segments which are close to their counterpart OWL statements. Some of these approaches also perform grouping and aggregating of these NL text segments to generate a more fluent and comprehensive form of the content. Restricting our attention to description of individuals and concepts, we find that the approach currently followed in the available tools is that of determining the set of all logical conditions that are satisfied by the given individual/concept name and translate these conditions verbatim into corresponding NL descriptions. Human-understandability of such descriptions is affected by the presence of repetitions and redundancies, as they have high fidelity to their OWL representation. In the literature, no efforts had been taken to remove redundancies and repetitions at the logical-level before generating the NL descriptions of entities and we find this to be the main reason for lack of readability of the generated text. Herein, we propose a technique called semantic-refinement(SR) to generate meaningful and easily-understandable descriptions of individuals and concepts of a given OWLontology. We identify the combinations of OWL/DL constructs that lead to repetitive/redundant descriptions and propose a series of refinement rules to rewrite the conditions that are satisfied by an individual/concept in a meaning-preserving manner. The reduced set of conditions are then employed for generating NL descriptions. Our experiments show that, SR leads to significantly improved descriptions of ontology entities. We also test the effectiveness and usefulness of the the generated descriptions for the purpose of validating the ontologies and find that the proposed technique is indeed helpful in the context.
Redundancy-free Verbalization of Individuals for Ontology Validation
We investigate the problem of verbalizing Web Ontology Language (OWL) axioms of domain ontologies in this paper.
Modeling of Item-Difficulty for Ontology-based MCQs
In this paper, we study various aspects and factors that are involved in determining the difficulty-score of an MCQ, and propose an ontology-based model for the prediction.
Enriching Linked Datasets with New Object Properties
In this paper, we present DART (Detecting Arbitrary Relations for enriching T-Boxes of Linked Data) - an unsupervised solution to enrich the LOD cloud with new object properties between two given classes.
