Karam Abdulahhad

dblp:49/9580 · DBLP profile ↗
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5ranked-venue papers
5as first author
0since 2021 · last 2018
0000-0002-0041-7047ORCID · corroborated

Domains — the database's venue-derived domains; a paper can count in several

Databases, data management, data science and information retrieval · 4 · 4 first-authorArtificial intelligence and machine learning · 2 · 2 first-authorApplied, interdisciplinary, general and emerging computing · 1 · 1 first-author

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Databases, data mining, and information retrieval
1 paper
Information retrieval · 100%

Topics — the 3 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Information retrieval › retrieval models
logic-based retrieval models
0.212013
Is uncertain logical-matching equivalent to conditional probability? · SIGIR 2013
Information retrieval
retrieval models
0.212013
Is uncertain logical-matching equivalent to conditional probability? · SIGIR 2013
Information retrieval › retrieval models
probabilistic retrieval model
0.012013
Is uncertain logical-matching equivalent to conditional probability? · SIGIR 2013

Methods — techniques the papers use, named apart from their topics

propositional logic · 0.2lattice theory · 0.2degree of implication · 0.2
YearPublicationVenuePosition
2018 Concept Embedding for Information Retrieval
Karam Abdulahhad
ECIR1
2017 Logics, Lattices and Probability: The Missing Links to Information Retrieval
abstract
Logic-based information retrieval (IR) models represent the retrieval decision as an implication d→q between a document d and a query q, where d and q are logical sentences. However, d→q is generally a binary decision, thus we need a measurement to estimate the degree to which d implies q, denoted U(d→q)⁠. Most of the existing logic-based IR models either do not precisely define the implication d→q or use non-classical definitions. Some models also define the uncertainty U in informal ways. More importantly, they use two non-related frameworks to define d→q and its uncertainty U, even though the two notions are intrinsically related. The goal of this study is to propose a new logic-based IR model, which overcome these shortcomings. To this end, we first propose to replace the implication d→q by the validity of material implication⊨d⊃q⁠. Second, we redefine and adapt the mathematical relationship between logics, lattices and probability. Our new IR model presents a possible formalism for van Rijsbergen's intuition about replacing U(d→q) by P(q∣d)⁠.
Karam Abdulahhad, Jean-Pierre Chevallet, Catherine Berrut
Comput. J.1
2013 Revisiting the Term Frequency in Concept-Based IR Models
Karam Abdulahhad, Jean-Pierre Chevallet, Catherine Berrut
DEXA (1)1
2013 Is uncertain logical-matching equivalent to conditional probability?
abstract
Logic-based Information Retrieval (IR) models represent the retrieval decision as a logical implication d->q between a document d and a query q, where d and q are logical sentences. However, d->q is a binary decision, we thus need a measure to estimate the degree to which d implies q, denoted P(d->q). In this study, we revisit the Van Rijsbergen's assumptions about: 1- the logical implication ->' is not the material one, and 2- P(d->q) could be estimated by the conditional probability P(q|d). More precisely, we claim that the material implication is an appropriate implication for IR, and also we mathematically prove that replacing P(d->q) by P(q|d) is a correct choice. In order to prove the Van Rijsbergen's assumption, we use the Propositional Logic and the Lattice theory. We also exploit the notion of degree of implication that is proposed by Knuth.
Karam Abdulahhad, Jean-Pierre Chevallet, Catherine Berrut
SIGIR1
2012 The Effective Relevance Link between a Document and a Query
Karam Abdulahhad, Jean-Pierre Chevallet, Catherine Berrut
DEXA (1)1