1,841 research outputs found
Current and Future Challenges in Knowledge Representation and Reasoning
Knowledge Representation and Reasoning is a central, longstanding, and active
area of Artificial Intelligence. Over the years it has evolved significantly;
more recently it has been challenged and complemented by research in areas such
as machine learning and reasoning under uncertainty. In July 2022 a Dagstuhl
Perspectives workshop was held on Knowledge Representation and Reasoning. The
goal of the workshop was to describe the state of the art in the field,
including its relation with other areas, its shortcomings and strengths,
together with recommendations for future progress. We developed this manifesto
based on the presentations, panels, working groups, and discussions that took
place at the Dagstuhl Workshop. It is a declaration of our views on Knowledge
Representation: its origins, goals, milestones, and current foci; its relation
to other disciplines, especially to Artificial Intelligence; and on its
challenges, along with key priorities for the next decade
Visual Domain Ontology using OWL Lite for Semantic Image Processing
In this paper, a visual domain ontology (VDO) is constructed using OWL-Lite Language. The VDO passes through two execution phases, namely, construction and inferring phases. In the construction phase, OWL classes are initialized, with reference to annotated scenes, and connected by hierarchical, spatial, and content-based relationships (presence/absence of some objects depends on other objects). In the inferring phase, the VDO is used to infer knowledge about an unknown scene. This paper aims to use a standard language, namely, OWL, to represent non-standard visual knowledge; facilitate straightforward ontology enrichment; and define the rules for inferring based on the constructed ontology. The OWL standardizes the constructed knowledge and facilitates advanced inferring because it is built on top of the first-order logic and description logic. The VDO then allows an efficient representation and reasoning of complex visual knowledge. In addition to representation, the VDO enables easy extension, sharing, and reuse of the represented visual knowledge
Standpoint Logic: A Logic for Handling Semantic Variability, with Applications to Forestry Information
It is widely accepted that most natural language expressions do not have precise universally agreed definitions that fix their meanings. Except in the case of certain technical terminology, humans use terms
in a variety of ways that are adapted to different contexts and perspectives. Hence, even when conversation participants share the same vocabulary and agree on fundamental taxonomic relationships (such as subsumption and mutual exclusivity), their view on the specific meaning of terms may differ significantly. Moreover, even individuals themselves may not hold permanent points of view, but rather adopt different semantics depending on the particular features of the situation and what they wish to communicate.
In this thesis, we analyse logical and representational aspects of the semantic variability of natural language terms. In particular, we aim to provide a formal language adequate for reasoning in settings where different agents may adopt particular standpoints or perspectives, thereby narrowing the semantic variability of the vague language predicates in different ways.
For that purpose, we present standpoint logic, a framework for interpreting languages in the presence of semantic variability. We build on supervaluationist accounts of vagueness, which explain linguistic indeterminacy in terms of a collection of possible interpretations of the terms of the language (precisifications).
This is extended by adding the notion of standpoint, which intuitively corresponds to a particular point of view on how to interpret vague terminology, and may be taken by a person or institution in a relevant context. A standpoint is modelled by sets of precisifications compatible with that point of view and does not need to be fully precise.
In this way, standpoint logic allows one to articulate finely grained and structured stipulations of the varieties of interpretation that can be given to a vague concept or a set of related concepts and also provides means to express relationships between different systems of interpretation.
After the specification of precisifications and standpoints and the consideration of the relevant notions of truth and validity, a multi-modal logic language for describing standpoints is presented. The language includes a modal operator for each standpoint, such that \standb{s}\phi means that a proposition is unequivocally true according to the standpoint --- i.e.\ is true at all precisifications compatible with .
We provide the logic with a Kripke semantics and examine the characteristics of its intended models.
Furthermore, we prove the soundness, completeness and decidability of standpoint logic with an underlying propositional language, and show that the satisfiability problem is NP-complete.
We subsequently illustrate how this language can be used to represent logical properties and connections between alternative partial models of a domain and different accounts of the semantics of terms.
As proof of concept, we explore the application of our formal framework to the domain of forestry, and in particular, we focus on the semantic variability of `forest'. In this scenario, the problematic arising of the assignation of different meanings has been repeatedly reported in the literature, and it is especially relevant in the context of the unprecedented scale of publicly available geographic data, where information and databases, even when ostensibly linked to ontologies, may present substantial semantic variation, which obstructs interoperability and confounds knowledge exchange
Semantic web approach for italian graduates' surveys: the AlmaLaurea ontology proposal
Il crescente sviluppo e la promozione della trasparenza dei dati
nell’ambito della pubblica amministrazione copre molteplici aspetti, fra cui
l’educazione universitaria. Attualmente sono difatti numerosi i dataset rilasciati in
formato Linked Open Data disponibili a livello nazionale ed internazionale. Fra le
informazioni pubblicamente disponibili spiccano concetti riguardo l’occupazione e
la numerosità dei laureati. Nonostante il progresso riscontrato, la mancanza di una
metodologia standard per la descrizione di informazioni statistiche sui laureati rende
difficoltoso un confronto di determinati fatti a partire da differenti sorgenti di dati.
Sul piano nazionale, le indagini AlmaLaurea colmano il gap informativo
dell’eterogeneità delle fonti proponendo statistiche centralizzate su profilo dei
laureati e relativa condizione occupazionale, aggiornate annualmente. Scopo del
progetto di tesi è la realizzazione di un’ontologia di dominio che descriva diverse
peculiarità dei laureati, promuovendo allo stesso tempo la definizione strutturata dei
dati AlmaLaurea e la successiva pubblicazione nel contesto Linked Open Data. Il
progetto, realizzato con l’ausilio delle tecnologie del Web Semantico, propone infine la creazione di un endpoint SPARQL e di una interfaccia web per l'interrogazione e
la visualizzazione dei dati strutturati
Optimization and inference under fuzzy numerical constraints
Εκτεταμένη έρευνα έχει γίνει στους τομείς της Ικανοποίησης Περιορισμών με
διακριτά (ακέραια) ή πραγματικά πεδία τιμών. Αυτή η έρευνα έχει οδηγήσει σε
πολλαπλές σημασιολογικές περιγραφές, πλατφόρμες και
συστήματα για την περιγραφή σχετικών προβλημάτων με επαρκείς βελτιστοποιήσεις.
Παρά ταύτα, λόγω της ασαφούς φύσης
πραγματικών προβλημάτων ή ελλιπούς μας γνώσης για αυτά, η σαφής μοντελοποίηση
ενός προβλήματος ικανοποίησης περιορισμών δεν είναι πάντα ένα εύκολο ζήτημα ή
ακόμα και η καλύτερη προσέγγιση. Επιπλέον, το πρόβλημα της μοντελοποίησης και
επίλυσης ελλιπούς γνώσης είναι ακόμη δυσκολότερο. Επιπροσθέτως, πρακτικές
απαιτήσεις μοντελοποίησης και μέθοδοι βελτιστοποίησης του χρόνου αναζήτησης
απαιτούν συνήθως ειδικές πληροφορίες για το πεδίο εφαρμογής,
καθιστώντας τη δημιουργία ενός γενικότερου πλαισίου βελτιστοποίησης ένα
ιδιαίτερα δύσκολο πρόβλημα. Στα πλαίσια αυτής της εργασίας θα μελετήσουμε το
πρόβλημα της μοντελοποίησης και αξιοποίησης σαφών, ελλιπών ή ασαφών
περιορισμών, καθώς και πιθανές στρατηγικές βελτιστοποίησης. Καθώς τα
παραδοσιακά προβλήματα ικανοποίησης περιορισμών λειτουργούν βάσει συγκεκριμένων
και προκαθορισμένων κανόνων και σχέσεων, παρουσιάζει ενδιαφέρον η διερεύνηση
στρατηγικών και βελτιστοποιήσεων που θα επιτρέπουν το συμπερασμό νέων ή/και
αποδοτικότερων περιορισμών. Τέτοιοι επιπρόσθετοι κανόνες θα μπορούσαν να
βελτιώσουν τη διαδικασία αναζήτησης μέσω της εφαρμογής αυστηρότερων περιορισμών
και περιορισμού του χώρου αναζήτησης ή να προσφέρουν χρήσιμες πληροφορίες στον
αναλυτή για τη φύση του προβλήματος που
μοντελοποιεί.Extensive research has been done in the areas of Constraint Satisfaction with
discrete/integer
and real domain ranges. Multiple platforms and systems to deal with these kinds
of domains have been developed and appropriately optimized. Nevertheless, due
to the incomplete and possibly vague nature of real-life problems, modeling a
crisp and adequately strict satisfaction problem may not always be easy or even
appropriate. The problem of modeling incomplete
knowledge or solving an incomplete/relaxed representation of a problem is a
much harder issue to tackle. Additionally, practical modeling requirements and
search optimizations require specific domain knowledge in order to be
implemented, making the creation of a more generic optimization framework an
even harder problem.In this thesis, we will study the problem of modeling and
utilizing incomplete and fuzzy constraints, as well as possible optimization
strategies. As constraint satisfaction problems usually contain hard-coded
constraints based on specific problem and domain knowledge, we will investigate
whether strategies and generic heuristics exist for inferring new constraint
rules. Additional rules could optimize the search process by implementing
stricter constraints and thus pruning the search space or even provide useful
insight to the researcher concerning the nature of the investigated problem
Dwelling on ontology - semantic reasoning over topographic maps
The thesis builds upon the hypothesis that the spatial arrangement of topographic
features, such as buildings, roads and other land cover parcels, indicates how land is
used. The aim is to make this kind of high-level semantic information explicit within
topographic data. There is an increasing need to share and use data for a wider range of
purposes, and to make data more definitive, intelligent and accessible. Unfortunately,
we still encounter a gap between low-level data representations and high-level concepts
that typify human qualitative spatial reasoning. The thesis adopts an ontological
approach to bridge this gap and to derive functional information by using standard
reasoning mechanisms offered by logic-based knowledge representation formalisms. It
formulates a framework for the processes involved in interpreting land use information
from topographic maps. Land use is a high-level abstract concept, but it is also an
observable fact intimately tied to geography. By decomposing this relationship, the
thesis correlates a one-to-one mapping between high-level conceptualisations
established from human knowledge and real world entities represented in the data.
Based on a middle-out approach, it develops a conceptual model that incrementally
links different levels of detail, and thereby derives coarser, more meaningful
descriptions from more detailed ones. The thesis verifies its proposed ideas by
implementing an ontology describing the land use ‘residential area’ in the ontology
editor Protégé. By asserting knowledge about high-level concepts such as types of
dwellings, urban blocks and residential districts as well as individuals that link directly
to topographic features stored in the database, the reasoner successfully infers instances
of the defined classes. Despite current technological limitations, ontologies are a
promising way forward in the manner we handle and integrate geographic data,
especially with respect to how humans conceptualise geographic space
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