Search Results for author: Saeedeh Shekarpour

Found 17 papers, 9 papers with code

HopfE: Knowledge Graph Representation Learning using Inverse Hopf Fibrations

1 code implementation12 Aug 2021 Anson Bastos, Kuldeep Singh, Abhishek Nadgeri, Saeedeh Shekarpour, Isaiah Onando Mulang, Johannes Hoffart

A few KGE techniques address interpretability, i. e., mapping the connectivity patterns of the relations (i. e., symmetric/asymmetric, inverse, and composition) to a geometric interpretation such as rotations.

Knowledge Graph Embedding Link Prediction

Capturing Knowledge of Emerging Entities From Extended Search Snippets

1 code implementation17 Mar 2021 Sunday C. Ngwobia, Saeedeh Shekarpour, Faisal Alshargi

The current encyclopedias are limited to highly popular entities, which are far fewer compared with the emerging entities.

Entity Embeddings

Towards Optimisation of Collaborative Question Answering over Knowledge Graphs

no code implementations14 Aug 2019 Kuldeep Singh, Mohamad Yaser Jaradeh, Saeedeh Shekarpour, Akash Kulkarni, Arun Sethupat Radhakrishna, Ioanna Lytra, Maria-Esther Vidal, Jens Lehmann

Collaborative Question Answering (CQA) frameworks for knowledge graphs aim at integrating existing question answering (QA) components for implementing sequences of QA tasks (i. e. QA pipelines).

Knowledge Graphs Question Answering

A Road-map Towards Explainable Question Answering A Solution for Information Pollution

no code implementations4 Jul 2019 Saeedeh Shekarpour, Faisal Al-Shargi

The increasing rate of information pollution on the Web requires novel solutions to tackle that.

Question Answering

Old is Gold: Linguistic Driven Approach for Entity and Relation Linking of Short Text

1 code implementation NAACL 2019 Ahmad Sakor, on, Isaiah o Mulang{'}, Kuldeep Singh, Saeedeh Shekarpour, Maria Esther Vidal, Jens Lehmann, S{\"o}ren Auer

Short texts challenge NLP tasks such as named entity recognition, disambiguation, linking and relation inference because they do not provide sufficient context or are partially malformed (e. g. wrt.

Entity Linking Implicit Relations +3

HCqa: Hybrid and Complex Question Answering on Textual Corpus and Knowledge Graph

1 code implementation24 Nov 2018 Somayeh Asadifar, Mohsen Kahani, Saeedeh Shekarpour

Exploiting the answer to complex questions is further challenged if it requires integrating knowledge from unstructured data sources, i. e., textual corpus, as well as structured data sources, i. e., knowledge graphs.

Knowledge Graphs named-entity-recognition +3

Analyzing and learning the language for different types of harassment

no code implementations1 Nov 2018 Mohammadreza Rezvan, Saeedeh Shekarpour, Faisal Alshargi, Krishnaprasad Thirunarayan, Valerie L. Shalin, Amit Sheth

In this paper, we introduce the notion of contextual type to harassment involving five categories: (i) sexual, (ii) racial, (iii) appearance-related, (iv) intellectual and (v) political.

No One is Perfect: Analysing the Performance of Question Answering Components over the DBpedia Knowledge Graph

3 code implementations26 Sep 2018 Kuldeep Singh, Ioanna Lytra, Arun Sethupat Radhakrishna, Saeedeh Shekarpour, Maria-Esther Vidal, Jens Lehmann

Question answering (QA) over knowledge graphs has gained significant momentum over the past five years due to the increasing availability of large knowledge graphs and the rising importance of question answering for user interaction.

Knowledge Graphs Question Answering

Concept2vec: Metrics for Evaluating Quality of Embeddings for Ontological Concepts

1 code implementation12 Mar 2018 Faisal Alshargi, Saeedeh Shekarpour, Tommaso Soru, Amit Sheth

This deficiency is further sensed with respect to embeddings generated for structured data because there are no concrete evaluation metrics measuring the quality of the encoded structure as well as semantic patterns in the embedding space.

A Quality Type-aware Annotated Corpus and Lexicon for Harassment Research

no code implementations26 Feb 2018 Mohammadreza Rezvan, Saeedeh Shekarpour, Lakshika Balasuriya, Krishnaprasad Thirunarayan, Valerie Shalin, Amit Sheth

In this paper, we publish first, a quality annotated corpus and second, an offensive words lexicon capturing different types type of harassment as (i) sexual harassment, (ii) racial harassment, (iii) appearance-related harassment, (iv) intellectual harassment, and (v) political harassment. We crawled data from Twitter using our offensive lexicon.

CEVO: Comprehensive EVent Ontology Enhancing Cognitive Annotation

no code implementations19 Jan 2017 Saeedeh Shekarpour, Faisal Al-Shargi, Valerie Shalin, Krishnaprasad Thirunarayan, Amit P. Sheth

These use-cases demonstrate the benefits of using CEVO for annotation: (i) annotating English verbs from an abstract conceptualization, (ii) playing the role of an upper ontology for organizing ontological properties, and (iii) facilitating the annotation of text relations using any underlying vocabulary.

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