Search Results for author: Filip Ilievski

Found 43 papers, 15 papers with code

The Predicate Matrix and the Event and Implied Situation Ontology: Making More of Events

no code implementations GWC 2016 Roxane Segers, Egoitz Laparra, Marco Rospocher, Piek Vossen, German Rigau, Filip Ilievski

This paper presents the Event and Implied Situation Ontology (ESO), a resource which formalizes the pre and post situations of events and the roles of the entities affected by an event.

ReferenceNet: a semantic-pragmatic network for capturing reference relations.

no code implementations GWC 2018 Piek Vossen, Filip Ilievski, Marten Postrma

In this paper, we present ReferenceNet: a semantic-pragmatic network of reference relations between synsets.

Word Embeddings

Numeracy enhances the Literacy of Language Models

no code implementations EMNLP 2021 Avijit Thawani, Jay Pujara, Filip Ilievski

This paper studies the effect of using six different number encoders on the task of masked word prediction (MWP), as a proxy for evaluating literacy.

Case-Based Reasoning with Language Models for Classification of Logical Fallacies

no code implementations27 Jan 2023 Zhivar Sourati, Filip Ilievski, Hông-Ân Sandlin, Alain Mermoud

The ease and the speed of spreading misinformation and propaganda on the Web motivate the need to develop trustworthy technology for detecting fallacies in natural language arguments.

Language Modelling Logical Fallacies +2

Robust and Explainable Identification of Logical Fallacies in Natural Language Arguments

1 code implementation12 Dec 2022 Zhivar Sourati, Vishnu Priya Prasanna Venkatesh, Darshan Deshpande, Himanshu Rawlani, Filip Ilievski, Hông-Ân Sandlin, Alain Mermoud

Our three-stage framework natively consolidates prior datasets and methods from existing tasks, like propaganda detection, serving as an overarching evaluation testbed.

Data Augmentation Logical Fallacies +2

A Study of Slang Representation Methods

1 code implementation11 Dec 2022 Aravinda Kolla, Filip Ilievski, Hông-Ân Sandlin, Alain Mermoud

Considering the large amount of content created online by the minute, slang-aware automatic tools are critically needed to promote social good, and assist policymakers and moderators in restricting the spread of offensive language, abuse, and hate speech.

Word Embeddings

Utilizing Background Knowledge for Robust Reasoning over Traffic Situations

no code implementations4 Dec 2022 Jiarui Zhang, Filip Ilievski, Aravinda Kollaa, Jonathan Francis, Kaixin Ma, Alessandro Oltramari

Understanding novel situations in the traffic domain requires an intricate combination of domain-specific and causal commonsense knowledge.

Knowledge Graphs Multiple-choice +2

PINTO: Faithful Language Reasoning Using Prompt-Generated Rationales

1 code implementation3 Nov 2022 Peifeng Wang, Aaron Chan, Filip Ilievski, Muhao Chen, Xiang Ren

Neural language models (LMs) have achieved impressive results on various language-based reasoning tasks by utilizing latent knowledge encoded in their own pretrained parameters.

Decision Making

Does Wikidata Support Analogical Reasoning?

no code implementations2 Oct 2022 Filip Ilievski, Jay Pujara, Kartik Shenoy

Analogical reasoning methods have been built over various resources, including commonsense knowledge bases, lexical resources, language models, or their combination.

Coalescing Global and Local Information for Procedural Text Understanding

1 code implementation COLING 2022 Kaixin Ma, Filip Ilievski, Jonathan Francis, Eric Nyberg, Alessandro Oltramari

In this paper, we propose Coalescing Global and Local Information (CGLI), a new model that builds entity- and timestep-aware input representations (local input) considering the whole context (global input), and we jointly model the entity states with a structured prediction objective (global output).

Procedural Text Understanding Structured Prediction

Enriching Wikidata with Linked Open Data

no code implementations1 Jul 2022 Bohui Zhang, Filip Ilievski, Pedro Szekely

We present a novel workflow that includes gap detection, source selection, schema alignment, and semantic validation.

Entity Alignment Knowledge Graphs

Understanding Narratives through Dimensions of Analogy

1 code implementation14 Jun 2022 Thiloshon Nagarajah, Filip Ilievski, Jay Pujara

Experiments with language models and neuro-symbolic AI reasoners on these tasks reveal that state-of-the-art methods can be applied to reason by analogy with a limited success, motivating the need for further research towards comprehensive and scalable analogical reasoning by AI.

An Empirical Investigation of Commonsense Self-Supervision with Knowledge Graphs

no code implementations21 May 2022 Jiarui Zhang, Filip Ilievski, Kaixin Ma, Jonathan Francis, Alessandro Oltramari

In this paper, we study the effect of knowledge sampling strategies and sizes that can be used to generate synthetic data for adapting language models.

Knowledge Graphs

Augmenting Knowledge Graphs for Better Link Prediction

1 code implementation26 Mar 2022 Jiang Wang, Filip Ilievski, Pedro Szekely, Ke-Thia Yao

Experiments on legacy benchmarks and a new large benchmark, DWD, show that augmenting the knowledge graph with quantities and years is beneficial for predicting both entities and numbers, as KGA outperforms the vanilla models and other relevant baselines.

Knowledge Graph Embedding Knowledge Graphs +1

Generalizable Neuro-symbolic Systems for Commonsense Question Answering

no code implementations17 Jan 2022 Alessandro Oltramari, Jonathan Francis, Filip Ilievski, Kaixin Ma, Roshanak Mirzaee

This chapter illustrates how suitable neuro-symbolic models for language understanding can enable domain generalizability and robustness in downstream tasks.

Knowledge Graphs Question Answering

Contextualized Scene Imagination for Generative Commonsense Reasoning

1 code implementation ICLR 2022 Peifeng Wang, Jonathan Zamora, Junfeng Liu, Filip Ilievski, Muhao Chen, Xiang Ren

In this paper, we propose an Imagine-and-Verbalize (I&V) method, which learns to imagine a relational scene knowledge graph (SKG) with relations between the input concepts, and leverage the SKG as a constraint when generating a plausible scene description.

Common Sense Reasoning Story Generation

Story Generation with Commonsense Knowledge Graphs and Axioms

no code implementations AKBC Workshop CSKB 2021 Filip Ilievski, Jay Pujara, Hanzhi Zhang

Our method aligns story types with commonsense axioms, and queries to a commonsense knowledge graph, enabling the generation of hundreds of thousands of stories.

Common Sense Reasoning Knowledge Graphs +1

Analyzing Race and Country of Citizenship Bias in Wikidata

no code implementations11 Aug 2021 Zaina Shaik, Filip Ilievski, Fred Morstatter

Through this analysis, we discovered that there is an overrepresentation of white individuals and those with citizenship in Europe and North America; the rest of the groups are generally underrepresented.

User-friendly Comparison of Similarity Algorithms on Wikidata

1 code implementation11 Aug 2021 Filip Ilievski, Pedro Szekely, Gleb Satyukov, Amandeep Singh

While the similarity between two concept words has been evaluated and studied for decades, much less attention has been devoted to algorithms that can compute the similarity of nodes in very large knowledge graphs, like Wikidata.

Entity Linking Knowledge Graphs

Creating and Querying Personalized Versions of Wikidata on a Laptop

no code implementations6 Aug 2021 Hans Chalupsky, Pedro Szekely, Filip Ilievski, Daniel Garijo, Kartik Shenoy

Application developers today have three choices for exploiting the knowledge present in Wikidata: they can download the Wikidata dumps in JSON or RDF format, they can use the Wikidata API to get data about individual entities, or they can use the Wikidata SPARQL endpoint.


A Study of the Quality of Wikidata

1 code implementation1 Jul 2021 Kartik Shenoy, Filip Ilievski, Daniel Garijo, Daniel Schwabe, Pedro Szekely

Wikidata has been increasingly adopted by many communities for a wide variety of applications, which demand high-quality knowledge to deliver successful results.

Do Language Models Perform Generalizable Commonsense Inference?

1 code implementation Findings (ACL) 2021 Peifeng Wang, Filip Ilievski, Muhao Chen, Xiang Ren

Inspired by evidence that pretrained language models (LMs) encode commonsense knowledge, recent work has applied LMs to automatically populate commonsense knowledge graphs (CKGs).

Knowledge Graphs

PaCo: Preconditions Attributed to Commonsense Knowledge

no code implementations18 Apr 2021 Ehsan Qasemi, Filip Ilievski, Muhao Chen, Pedro Szekely

To address this gap, we propose a novel challenge of reasoning with circumstantial preconditions.

Common Sense Reasoning

Dimensions of Commonsense Knowledge

no code implementations12 Jan 2021 Filip Ilievski, Alessandro Oltramari, Kaixin Ma, Bin Zhang, Deborah L. McGuinness, Pedro Szekely

Recently, the focus has been on large text-based sources, which facilitate easier integration with neural (language) models and application to textual tasks, typically at the expense of the semantics of the sources and their harmonization.

CSKG: The CommonSense Knowledge Graph

1 code implementation21 Dec 2020 Filip Ilievski, Pedro Szekely, Bin Zhang

Sources of commonsense knowledge support applications in natural language understanding, computer vision, and knowledge graphs.

Knowledge Graphs Natural Language Understanding

Knowledge-driven Data Construction for Zero-shot Evaluation in Commonsense Question Answering

1 code implementation7 Nov 2020 Kaixin Ma, Filip Ilievski, Jonathan Francis, Yonatan Bisk, Eric Nyberg, Alessandro Oltramari

Guided by a set of hypotheses, the framework studies how to transform various pre-existing knowledge resources into a form that is most effective for pre-training models.

Language Modelling Question Answering

Commonsense Knowledge in Wikidata

no code implementations18 Aug 2020 Filip Ilievski, Pedro Szekely, Daniel Schwabe

Our experiments reveal that: 1) albeit Wikidata-CS represents a small portion of Wikidata, it is an indicator that Wikidata contains relevant commonsense knowledge, which can be mapped to 15 ConceptNet relations; 2) the overlap between Wikidata-CS and other commonsense sources is low, motivating the value of knowledge integration; 3) Wikidata-CS has been evolving over time at a slightly slower rate compared to the overall Wikidata, indicating a possible lack of focus on commonsense knowledge.

Common Sense Reasoning Question Answering

Consolidating Commonsense Knowledge

no code implementations10 Jun 2020 Filip Ilievski, Pedro Szekely, Jingwei Cheng, Fu Zhang, Ehsan Qasemi

Commonsense reasoning is an important aspect of building robust AI systems and is receiving significant attention in the natural language understanding, computer vision, and knowledge graphs communities.

Common Sense Reasoning Knowledge Graphs +1

KGTK: A Toolkit for Large Knowledge Graph Manipulation and Analysis

1 code implementation29 May 2020 Filip Ilievski, Daniel Garijo, Hans Chalupsky, Naren Teja Divvala, Yixiang Yao, Craig Rogers, Rongpeng Li, Jun Liu, Amandeep Singh, Daniel Schwabe, Pedro Szekely

Knowledge graphs (KGs) have become the preferred technology for representing, sharing and adding knowledge to modern AI applications.

Knowledge Graphs

Large-scale Cross-lingual Language Resources for Referencing and Framing

no code implementations LREC 2020 Piek Vossen, Filip Ilievski, Marten Postma, Antske Fokkens, Gosse Minnema, Levi Remijnse

In this article, we lay out the basic ideas and principles of the project Framing Situations in the Dutch Language.

Combining Conceptual and Referential Annotation to Study Variation in Framing

no code implementations LREC 2020 Marten Postma, Levi Remijnse, Filip Ilievski, Antske Fokkens, Sam Titarsolej, Piek Vossen

The user can apply two types of annotations: 1) mappings from expressions to frames and frame elements, 2) reference relations from mentions to events and participants of the structured data.

The Profiling Machine: Active Generalization over Knowledge

no code implementations1 Oct 2018 Filip Ilievski, Eduard Hovy, Qizhe Xie, Piek Vossen

The human mind is a powerful multifunctional knowledge storage and management system that performs generalization, type inference, anomaly detection, stereotyping, and other tasks.

Anomaly Detection Management

Systematic Study of Long Tail Phenomena in Entity Linking

no code implementations COLING 2018 Filip Ilievski, Piek Vossen, Stefan Schlobach

In this paper we report on a series of hypotheses regarding the long tail phenomena in entity linking datasets, their interaction, and their impact on system performance.

Entity Linking

SemEval-2018 Task 5: Counting Events and Participants in the Long Tail

no code implementations SEMEVAL 2018 Marten Postma, Filip Ilievski, Piek Vossen

This paper discusses SemEval-2018 Task 5: a referential quantification task of counting events and participants in local, long-tail news documents with high ambiguity.

Word Sense Disambiguation

Semantic overfitting: what `world' do we consider when evaluating disambiguation of text?

no code implementations COLING 2016 Filip Ilievski, Marten Postma, Piek Vossen

Semantic text processing faces the challenge of defining the relation between lexical expressions and the world to which they make reference within a period of time.

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