Features of Structure for Analogy Retrieval
Diarmuid O'Donoghue2001, APPLIED INFORMATICS-PROCEEDINGS-
Sign up for access to the world's latest research
checkGet notified about relevant papers
checkSave papers to use in your research
checkJoin the discussion with peers
checkTrack your impact
Abstract
retrieving analogies from presented problem data is an important phase of analogical reasoning, influencing many related cognitive processes. Existing models have focused on semantic similarity, but structural similarity is also a necessary requirement of any analogical comparison. We present a new technique for performing structure based analogy retrieval. This is founded upon derived attributes that explicitly encode elementary structural qualities of a domains representation. Crucially, these attributes are unrelated to the semantic content of the domain information, and encode only its structural qualities. We describe a number of derived attributes and detail the computation of the corresponding attribute values. We examine our models operation, detailing how it retrieves both semantically related and unrelated domains. We also present a comparison of our algorithms performance with existing models, using a structure rich but semantically impoverished domain.
Related papers
Radar: Finding analogies using attributes of structure
Artificial Intelligence and Cognitive Science, 2002
RADAR is a model of analogy retrieval that employs the principle of systematicity as its primary retrieval cue. RADAR was created to address the current bias toward semantics in analogical retrieval models, to the detriment of structural factors. RADAR recalls 100% of structurally identical domains. We describe a technique based on "derived attributes" that captures structural descriptions of the domain's representation rather than domain contents. We detail their use, recall and performance within RADAR through empirical evidence. We contrast RADAR with existing models of analogy retrieval. We also demonstrate that RADAR can retrieve both semantically related and semantically unrelated domains, even without a complete target description, which plagues current models.
A Multi-Strategy Approach to Structural Analogy Making
Journal of Intelligent Information Systems, 2017
Analogy is the cognitive process of matching the characterizing features of two different items. This may enable reuse of knowledge across domains , which can help to solve problems. Indeed, abstracting the 'role' of the features away from their specific embodiment in the single items is fundamental to recognize the possibility of an analogical mapping between them. The analogical reasoning process consists of five steps: retrieval, mapping, evaluation , abstraction and re-representation. This paper proposes two forms of an operator that includes all these elements, providing more power and flexibility than existing systems. In particular, the Roles Mapper leverages the presence of identical descriptors in the two domains, while the Roles Argumentation-based Mapper removes also this limitation. For generality and compliance with other reasoning operators in a multi-strategy inference setting, they exploit a simple formalism based on First-Order Logic and do not require any background knowledge or meta-knowledge. Applied to the most critical classical examples in the literature, they proved to be able to find insightful analogies.
Analogy as a search procedure: A dimensional view
Journal of Experimental and Theoretical Artificial Intelligence , 2022
In this paper, we outline a comprehensive approach to composed analogies based on the theory of conceptual spaces. Our algorithmic model understands analogy as a search procedure and builds upon the idea that analogical similarity depends on a conceptual phenomena called 'dimensional salience.' We distinguish between category-based, property-based, event-based, and part-whole analogies, and propose computationally-oriented methods for explicating them in terms of conceptual spaces.
A Data-Driven Approach for Making Analogies
Cognitive Science, 2017
Making analogies is an important way for people to explain and understand new concepts. Though making analogies is natural for human beings, it is not a trivial task for a dialogue agent. Making analogies requires the agent to establish a correspondence between concepts in two different domains. In this work, we explore a data-driven approach for making analogies automatically. Our proposed approach works with data represented as a flat graphical structure, which can either be designed manually or extracted from Internet data. For a given concept from the base domain, our analogy agent can automatically suggest a corresponding concept from the target domain, and a set of mappings between the relationships each concept has as supporting evidence. We demonstrate the working of this algorithm by both reproducing a classical example of analogy inference and making analogies in new domains generated from DBPedia data.
Analogy retrieval and processing with distributed vector representations
Expert Systems, 2000
Holographic reduced representations (HRRs) are a method for encoding nested relational structures in fixed‐width vector representations. HRRs encode relational structures as vector representations in such a way that the superficial similarity of the vectors reflects both superficial and structural similarity of the relational structures. HRRs also support a number of operations that could be very useful in psychological models of human analogy processing: fast estimation of superficial and structural similarity via a vector dot‐product; finding corresponding objects in two structures; and chunking of vector representations. Although similarity assessment and discovery of corresponding objects both theoretically take exponential time to perform fully and accurately, with HRRs one can obtain approximate solutions in constant time. The accuracy of these operations with HRRs mirrors patterns of human performance on analog retrieval and processing tasks.
Elaborating analogies from conceptual models
1996
Abstract. This paper defines and analyses a computational model of similarity which detects analogies between objects based on conceptual descriptions of them, constructed from classification, generalization relations and attributes. Analogies are detected (elaborated) by functions which measure conceptual distances between objects with respect to these semantic modelling abstractions. The model is domain independent and operational upon objects described in non uniform ways.
Finding analogies within systems: Constraints on unsegmented matching
The complex structure and organization of knowledge in the human mind is one of the key facets of thought. One of the fundamental cognitive processes that operates over that structure is analogy. A typical computational model of analogy might juxtapose a source do- main and a target domain, such as the solar system and the Bohr-Rutherford (BR) model of an atom (Gentner, 1983). The goal is to find a correspondence mapping between these two domains. Determining a mapping between the source and target domains of a non-trivial size would be intractable without a set of constraints to restrict the set of correspondences that are considered by a human reasoner. Moreover, the mere presence of domains serve as a constraint on mapping. In this paper, we study an alternative problem called unsegmented mapping - correspondence without specification of domains. We show a series of three formal constraints that allow for analogical-like mappings without explicit segmentation. The result, correspondence is possible without domains, has implications for models of analogical reasoning as well as schema induction and inference.
How analogies are generated: The roles of structural and superficial similarity
Memory & Cognition, 2000
Laboratory studies of analogical reasoning have shown that subjects are mostly influenced by superficial similarity in the retrieval of source analogs. However, real-world investigations have demonstrated that people generate analogies based on deep structural features. We conducted three studies to determine why laboratory and real-world studies have yielded different results. In the first two studies, we used a "production paradigm" in which subjects were asked to generate sources for a given target. Results show the majority of analogies generated displayed low levels of superficial similarity with the target problem. Moreover, most analogies were based on complex underlying structures. The third study used a "reception paradigm" methodology. Participants had to retrieve predetermined sources instead of generating their own. In this case, retrieval was largely constrained by surface similarity. We conclude that people can use structural relations when given an appropriate task and that previous research on analogy has underestimated this ability.
Web-Based Representation and Visualization of Analogies
Humans use analogies to communicate, reason, and learn. But while the human brain excels at creating and understanding analogies, it does not easily recall useful analogies created or learned over time. General purpose tools and methods are needed that assist humans in representing, storing, and recalling useful analogies. Additionally, such tools must take advantage of the World Wide Web's ubiquity, global reach, and universal standards. We first identify commonly occurring patterns of analogy structure. Because understanding of instructional analogies is significantly improved when their structure is visualized, we develop a compact and general representation for analogies using XML, and demonstrate general methods for visualizing the structure of analogy expressions in Web-based environments.
An Approach to the Semantics of Analogical Relations1
2007
Whereas approaches for deductive and inductive reasoning are well-examined for decades, analogical reasoning seems to be a hard problem for machine intelligence. Although several models for computing analogies have been proposed, there is no uncontroversial theory of the semantics of analogies. In this paper, we will investigate semantic issues of analogical relations, in particular, we will specify a model theory of analogical transfers. The presented approach is based on Heuristic-Driven Theory Projection (HDTP) a framework that computes an analogical relation between logical theories describing a source and a target domain. HDTP establishes the analogy by an abstraction process in which formulas from both domains are generalized creating a theory that syntactically subsumes the original theories. We will show that this syntactic process can be given a sensible interpretation on the semantic level. In particular, given models of the source and the target domains, we will examine t...
Related papers
Efficient modeling of analogy
Computational Linguistics and Intelligent Text …, 2005
Analogical modeling (AM) is a memory based model with a documented performance comparable to other types of memory based learning. Known algorithms implementing AM have a computationally complexity of O(2 n ). We formulate a representation theorem on analogical modeling which is used for implementing a range of approximations to AM with a complexity starting as low as O(n).
Benchmarking Semantic Capabilities of Analogy Querying Algorithms
Lecture Notes in Computer Science, 2016
Enabling semantically rich query paradigms is one of the core challenges of current information systems research. In this context, due to their importance and ubiquity in natural language, analogy queries are of particular interest. Current developments in natural language processing and machine learning resulted in some very promising algorithms relying on deep learning neural word embeddings which might contribute to finally realizing analogy queries. However, it is still quite unclear how well these algorithms work from a semantic point of view. One of the problems is that there is no clear consensus on the intended semantics of analogy queries. Furthermore, there are no suitable benchmark dataset available respecting the semantic properties of real-life analogies. Therefore, in this, paper, we discuss the challenges of benchmarking the semantics of analogy query algorithms with a special focus on neural embeddings. We also introduce the AGS analogy benchmark dataset which rectifies many weaknesses of established datasets. Finally, our experiments evaluating state-of-theart algorithms underline the need for further research in this promising field.
Automatically Retrieving Explanatory Analogies from Webpages
Explanatory analogies make learning complex concepts easier by elaborately mapping a target concept onto a more familiar source concept. Solutions exist for automatically retrieving shorter metaphors from natural language text, but not for explanatory analogies. In this paper, we propose an approach to find webpages containing explanatory analogies for a given target concept. For this, we propose the use of a 'region of interest' (ROI) based on the observation that linguistic markers and source concept often co-occur with various forms of the word 'analogy'. We also suggest an approach to identify the source concept(s) contained in a retrieved analogy webpage. We demonstrate these approaches on a dataset created using Google custom search to find candidate web pages that may contain analogies.
A Creative Analogy Machine: Results and Challenges
2012
Are we any closer to creating an autonomous model of analogical reasoning that can generate new and creative analogical comparisons? A three-phase model of analogical reasoning is presented that encompasses the phases of retrieval, mapping and inference validation. The model of the retrieval phase maximizes its creativity by focusing on domain topology, combating the semantic locality suffered by other models. The mapping model builds on a standard model of the mapping phase, again making use of domain topology. A novel validation model helps ensure the quality of the inferences that are accepted by the model. We evaluated the ability of our tri-phase model to re-discover several hcreative analogies (Boden, 1992) from a background memory containing many potential source domains. The model successfully re-discovered all creative comparisons, even when given problem descriptions that more accurately reflect the original problem – rather than the standard (post hoc) representation of the analogy. Finally, some remaining challenges for a truly autonomous creative analogy machine are assessed.
Producing and Recognizing Analogical Relations
Journal of the Experimental Analysis of Behavior, 2009
Analogical reasoning is an important component of intelligent behavior, and a key test of any approach to human language and cognition. Only a limited amount of empirical work has been conducted from a behavior analytic point of view, most of that within Relational Frame Theory (RFT), which views analogy as a matter of deriving relations among relations. The present series of four studies expands previous work by exploring the applicability of this model of analogy to topography-based rather than merely selectionbased responses and by extending the work into additional relations, including nonsymmetrical ones. In each of the four studies participants pretrained in contextual control over nonarbitrary stimulus relations of sameness and opposition, or of sameness, smaller than, and larger than, learned arbitrary stimulus relations in the presence of these relational cues and derived analogies involving directly trained relations and derived relations of mutual and combinatorial entailment, measured using a variety of productive and selection-based measures. In Experiment 1 participants successfully recognized analogies among stimulus networks containing same and opposite relations; in Experiment 2 analogy was successfully used to extend derived relations to pairs of novel stimuli; in Experiment 3 the procedure used in Experiment 1 was extended to nonsymmetrical comparative relations; in Experiment 4 the procedure used in Experiment 2 was extended to nonsymmetrical comparative relations. Although not every participant showed the effects predicted, overall the procedures occasioned relational responses consistent with an RFT account that have not yet been demonstrated in a behavior-analytic laboratory setting, including productive responding on the basis of analogies.
Instance-based retrieval by analogy
2007
Abstract This work presents a method for retrieval in knowledge bases expressed in Description Logics, founded in the instance-based learning. The procedure implements the disjunctive version space approach exploiting a notion of semantic difference. The method can be employed both to answer to class-membership queries, even though the answers are not logically entailed by the knowledge base, eg there are some inconsistent assertions due to heterogeneous sources.
Analogy by Alignment: On Structure Mapping and Similarity
2004
This paper presents a novel domain independent algorithm for constructing analogies using relationship-based structure-mapping. This algorithm is used as a core component in a system that solves visual analogy IQ test problems.
Analogy in a general-purpose reasoning system
Cognitive Systems Research, 2009
This paper introduces the various forms of analogy in NARS, a generalpurpose reasoning system. NARS is an AI system designed to be adaptive and to work with insufficient knowledge and resources. In the system, multiple types of inference, including analogy, deduction, induction, abduction, comparison, and revision, are unified both in syntax and in semantics. The system can also carry out relational and structural analogy, in ways comparable to (though different from) that in some other models of analogy, such as Copycat and SME. The paper addresses several theoretical issues in the study of analogy, including the specification and justification of analogy, the context sensitivity of analogy, as well as the role analogy plays in intelligence and cognition.
LUDI: A model for geometric analogies using attribute matching
2000
Abstract We review the work of Evans on graphical proportional analogies, identifying the object mappings that underlie many such comparisons. The limitations of Evans ANALOGY model are investigated. We then establish the role of attributes (colour, shape, pattern etc) in such analogies and identify two distinct mapping algorithms that are required by different classes of geometric analogy problems. We identify the conditions under which the alternate algorithms are required to produce a" best" answer.
FAME: Flexible, Scalable Analogy Mappings Engine
arXiv (Cornell University), 2023
Analogy is one of the core capacities of human cognition; when faced with new situations, we often transfer prior experience from other domains. Most work on computational analogy relies heavily on complex, manually crafted input. In this work, we relax the input requirements, requiring only names of entities to be mapped. We automatically extract commonsense representations and use them to identify a mapping between the entities. Unlike previous works, our framework can handle partial analogies and suggest new entities to be added. Moreover, our method's output is easily interpretable, allowing for users to understand why a specific mapping was chosen. Experiments show that our model correctly maps 81.2% of classical 2x2 analogy problems (guess level=50%). On larger problems, it achieves 77.8% accuracy (mean guess level=13.1%). In another experiment, we show our algorithm outperforms human performance, and the automatic suggestions of new entities resemble those suggested by humans. We hope this work will advance computational analogy by paving the way to more flexible, realistic input requirements, with broader applicability.
Loading Preview
Sorry, preview is currently unavailable. You can download the paper by clicking the button above.