This paper evaluates the efficiency of a number of popular corpus-based distributional models in ... more This paper evaluates the efficiency of a number of popular corpus-based distributional models in performing discovery on very large document sets, including online collections. Literature-based discovery is the process of identifying previously unknown connections from text, often published literature, that could lead to the development of new techniques or technologies. Literature-based discovery has attracted growing research interest ever since Swanson's serendipitous discovery of the therapeutic effects of fish oil on Raynaud's disease in 1986. The successful application of distributional models in automating the identification of indirect associations underpinning literature-based discovery has been heavily demonstrated in the medical domain. However, we wish to investigate the computational complexity of distributional models for literature-based discovery on much larger document collections, as they may provide computationally tractable solutions to tasks including, predicting future disruptive innovations. In this paper we perform a computational complexity analysis on four successful corpus-based distributional models to evaluate their fit for such tasks. Our results indicate that corpus-based distributional models that store their representations in fixed dimensions provide superior efficiency on literaturebased discovery tasks.
Topic Modeling algorithms are rarely used to support the qualitative content analysis process. Th... more Topic Modeling algorithms are rarely used to support the qualitative content analysis process. The main contributing factors for the lack of mainstream adoption can be attributed to the perception that Topic Modeling produces topics of poor quality and that content analysts do not trust the derived topics because they are unable to supply domain knowledge and interact with the algorithm. In this paper, interactive Topic Modeling algorithms namely Dirichlet-Forrest Latent Dirichlet Allocation and Penalised Non-negative Matrix Factorisation, are evaluated with respect to their ability to aid qualitative content analysis. More specifically, the relationship between interactivity, interpretation, topic coherence and trust in interactive content analysis is examined. The findings indicate that providing content analysts with the ability to interact with Topic Modeling algorithms produces topics that are directly related to their research questions. However, a number of improvements to these algorithms were also identified which have the potential to influence future algorithm development to better meet the requirements of qualitative content analysts.
The question of under what conditions conceptual representation is compositional remains debatabl... more The question of under what conditions conceptual representation is compositional remains debatable within cognitive science. This paper proposes a well developed mathematical apparatus for a probabilistic representation of concepts, drawing upon methods developed in quantum theory to propose a formal test that can determine whether a specific conceptual combination is compositional, or not. This test examines a joint probability distribution modeling the combination, asking whether or not it is factorizable. Empirical studies indicate that some combinations should be considered non-compositionally.
Human memory experiments appear to be generating "nonlocal" effects (Nelson & McEvoy 2007). In th... more Human memory experiments appear to be generating "nonlocal" effects (Nelson & McEvoy 2007). In this paper the possibility that words might be entangled in human semantic space is seriously entertained. This approach leads to a very natural picture of the way in which context might affect word association via the standard interpretation of quantum measurement. Two possible scenarios for testing such a hypothesis are suggested, both based upon potential violations of the CHSH inequality.
Changes introduced as a result of publishing processes such as copy-editing and formatting may no... more Changes introduced as a result of publishing processes such as copy-editing and formatting may not be reflected in this document.
Compositionality is a frequently made assumption in linguistics, and yet many human subjects reve... more Compositionality is a frequently made assumption in linguistics, and yet many human subjects reveal highly non-compositional word associations when confronted with novel concept combinations. This article will show how a non-compositional account of concept combinations can be supplied by modelling them as interacting quantum systems.
Many successful query expansion techniques ignore information about the term dependencies that ex... more Many successful query expansion techniques ignore information about the term dependencies that exist within natural language. However, researchers have recently demonstrated that consistent and significant improvements in retrieval effectiveness can be achieved by explicitly modelling term dependencies within the query expansion process. This has created an increased interest in dependency-based models. State-of-the-art dependency-based approaches primarily model term associations known within structural linguistics as syntagmatic associations, which are formed when terms co-occur together more often than by chance. However, structural linguistics proposes that the meaning of a word is also dependent on its paradigmatic associations, which are formed between words that can substitute for each other without effecting the acceptability of a sentence. Given the reliance on word meanings when a user formulates their query, our approach takes the novel step of modelling both syntagmatic and paradigmatic associations within the query expansion process based on the (pseudo) relevant documents returned in web search. The results demonstrate that this approach can provide significant improvements in web retrieval effectiveness when compared to a strong benchmark retrieval system.
International ACM SIGIR Conference on Research and Development in Information Retrieval, 2013
ABSTRACT The article focuses on how the information seeker makes decisions about relevance. It wi... more ABSTRACT The article focuses on how the information seeker makes decisions about relevance. It will employ a novel decision theory based on quantum probabilities. This direction derives from mounting research within the field of cognitive science showing that decision theory based on quantum probabilities is superior to modelling human judgements than standard probability models [2, 1]. By quantum probabilities, we mean decision event space is modelled as vector space rather than the usual Boolean algebra of sets. In this way,incompatible perspectives around a decision can be modelled leading to an interference term which modifies the law of total probability. The interference term is crucial in modifying the probability judgements made by current probabilistic systems so they align better with human judgement. The goal of this article is thus to model the information seeker user as a decision maker. For this purpose, signal detection models will be sketched which are in principle applicable in a wide variety of information seeking scenarios.
Many successful query expansion techniques ignore information about the term dependencies that ex... more Many successful query expansion techniques ignore information about the term dependencies that exist within natural language. However, researchers have recently demonstrated that consistent and significant improvements in retrieval effectiveness can be achieved by explicitly modelling term dependencies within the query expansion process. This has created an increased interest in dependency-based models. State-of-the-art dependency-based approaches primarily model term associations known within structural linguistics as syntagmatic associations, which are formed when terms co-occur together more often than by chance. However, structural linguistics proposes that the meaning of a word is also dependent on its paradigmatic associations, which are formed between words that can substitute for each other without effecting the acceptability of a sentence. Given the reliance on word meanings when a user formulates their query, our approach takes the novel step of modelling both syntagmatic and paradigmatic associations within the query expansion process based on the (pseudo) relevant documents returned in web search. The results demonstrate that this approach can provide significant improvements in web retrieval effectiveness when compared to a strong benchmark retrieval system.
Applying Belief Revision logic to model adaptive information retrieval is appealing since it prov... more Applying Belief Revision logic to model adaptive information retrieval is appealing since it provides a rigorous theoretical foundation to model partiality and uncertainty inherent in any information retrieval (IR) processes. In particular, a retrieval context can be formalised as a belief set and the formalised context is used to disambiguate vague user queries. Belief revision logic also provides a robust computational mechanism to revise an IR system's beliefs about the users' changing information needs. In addition, information flow is proposed as a text mining method to automatically acquire the initial IR contexts. The advantage of a beliefbased IR system is that its IR behaviour is more predictable and explanatory. However, computational efficiency is often a concern when the belief revision formalisms are applied to large real-life applications. This paper describes our beliefbased adaptive IR system which is underpinned by an efficient belief revision mechanism. Our initial experiments show that the belief-based symbolic IR model is more effective than a classical quantitative IR model. To our best knowledge, this is the first successful empirical evaluation of a logic-based IR model based on large IR benchmark collections.
The context-sensitivity of cognition has been demonstrated across a wide range of cognitive funct... more The context-sensitivity of cognition has been demonstrated across a wide range of cognitive functions such as perception, memory, judgement and decision making. A related term, ‘contextuality’, has appeared from the field of quantum cognition, with mounting empirical evidence demonstrating that cognitive phenomena are sometimes contextual. Contextuality is a subtle notion that influences how we must view the properties of the cognitive phenomenon being studied. This article addresses the questions: What does it mean for a cognitive phenomenon to be contextual? What are the implications of contextuality for probabilistic models of cognition? How does contextuality differ from context-sensitivity? Starting from George Boole’s “conditions of possible experience”, we argue that a probabilistic model of a cognitive phenomenon is necessarily subject to an assumption of realism. By this we mean that the phenomenon being studied is assumed to have cognitive properties with a definite value ...
A large number of studies in cognitive science have revealed that probabilistic outcomes of certa... more A large number of studies in cognitive science have revealed that probabilistic outcomes of certain human decisions do not agree with the axioms of classical probability theory. The field of Quantum Cognition provides an alternative probabilistic model to explain such paradoxical findings. It posits that cognitive systems have an underlying quantum-like structure, especially in decision-making under uncertainty. In this paper, we hypothesise that relevance judgement, being a multidimensional, cognitive concept, can be used to probe the quantum-like structure for modelling users' cognitive states in information seeking. Extending from an experiment protocol inspired by the Stern-Gerlach experiment in Quantum Physics, we design a crowd-sourced user study to show violation of the Kolmogorovian probability axioms as a proof of the quantum-like structure, and provide a comparison between a quantum probabilistic model and a Bayesian model for predictions of relevance.
Deep learning models have achieved high performances across different domains, such as medical de... more Deep learning models have achieved high performances across different domains, such as medical decision-making, autonomous vehicles, decision support systems, etc. Despite this success, the internal mechanisms of these models are opaque because their internal representations are too complex for a human to understand. This makes it hard to understand the how or the why of the predictions of deep learning models. There has been a growing interest in model-agnostic methods that can make deep learning models more transparent and explainable to a user. Some researchers recently argued that for a machine to achieve a certain degree of human-level explainability, this machine needs to provide human causally understandable explanations, also known as causability. A specific class of algorithms that have the potential to provide causability are counterfactuals. This paper presents an in-depth systematic review of the diverse existing body of literature on counterfactuals and causability for explainable artificial intelligence. We performed an LDA topic modeling analysis under a PRISMA framework to find the most relevant literature articles. This analysis resulted in a novel taxonomy that considers the grounding theories of the surveyed algorithms, together with their underlying properties and applications in real-world data. This research suggests that current model-agnostic counterfactual algorithms for explainable AI are not grounded on a causal theoretical formalism and, consequently, cannot promote causability to a human decision-maker. Our findings suggest that the explanations derived from major algorithms in the literature provide spurious correlations rather than cause/effects relationships, leading to sub-optimal, erroneous, or even biased explanations. This paper also advances the literature with new directions and challenges on promoting causability in model-agnostic approaches for explainable artificial intelligence.
We argue that web service discovery technology should help the user navigate a complex problem sp... more We argue that web service discovery technology should help the user navigate a complex problem space by providing suggestions for services which they may not be able to formulate themselves as (s)he lacks the epistemic resources to do so. Free text documents in service environments provide an untapped source of information for augmenting the epistemic state of the user and hence their ability to search effectively for services. A quantitative approach to semantic knowledge representation is adopted in the form of semantic space models computed from these free text documents. Knowledge of the user’s agenda is promoted by associational inferences computed from the semantic space. The inferences are suggestive and aim to promote human abductive reasoning to guide the user from fuzzy search goals into a better understanding of the problem space surrounding the given agenda. Experimental results are discussed based on a complex and realistic planning activity.
Web Services Meet Marketplaces As web services technologies mature, and commercial-scale, service... more Web Services Meet Marketplaces As web services technologies mature, and commercial-scale, service-oriented architectures shift from early adoption to mainstream development, a new revolution of service orientation is emerging. Beyond the orchestration of web services in multi-party business processes, a dedicated treatment for procuring web services into different markets is coming into focus. The first beneficiaries of open procurements of web services are ventures having successfully overcome the dotcom-burst such as Salesforce, StrikeIron, and GrandCentral. 1 These companies leverage XML-based technology to consolidate enterprise application portfolios built by independent software developers for the small to medium markets. Their early successes are paving the way to long-anticipated Amazon/eBay-style marketplaces for web services. For procurement of services in wider scale, traditional assumptions no longer hold. In larger marketplaces, consumers need a richer exposure of semantics in service descriptions and support of fuzzier search goals. In other words, if services are to be delivered independently, their non-functional properties, such as geospatial and temporal availability, methods of charging and payment, security, trust, rights, and penalties, need to be described in a systematic and precise manner. Furthermore, emerging web service usage scenarios, especially in marketplaces, are pushing the boundaries of coordination and the involvement of intermediaries and reusable service delivery components in endto-end transactions. Thus, automated assistance will be required by prospective web service suppliers to integrate "instantly" their services with other services, with intermediaries, and with service delivery components. This is to name just a few of the challenges posed by the new frontier of service-orientation. This paper presents a vision for the next generation of service-oriented systems, which we term web service ecosystems. The paper reviews early manifestations of these systems and identifies challenges that will need to be addressed on the road to the web service ecosystems vision The Rise Of Web Service Ecosystems Service-oriented architecture (SOA) has gained mainstream acceptance as a strategy for consolidating and repurposing legacy applications to be combined with new applications in more dynamic environments, through self-contained, reusable, and configurable services. As fostered through the web services standards stack, services, once in place, can interoperate with other services, be composed into long-running business processes, spanning intra-and inter-organizational boundaries, and be procured through different business domains and market sectors. As web services are exposed and connected with one another, they give rise to service ecosystems. In these ecosystems, services are deployed, published, discovered, delivered to different business channels through specialist intermediaries (e.g., payment, authentication, and mediation services), and monitored. A groundswell of web service ecosystems is emerging on the "dotcom" front, and targeting enterprise applications for the small to medium sector. Following the successes of Amazon and eBay goods marketplaces, the first significant efforts of bringing together web services using more open procurement models are precipitating the long-anticipated internet service marketplace concept. Salesforce.com, with around a half million subscribers, recently announced AppExchange 2 to expand its CRM software base into a full business software portfolio. To achieve this, testing and deployment
This paper presents a Graph Inference retrieval model that integrates structured knowledge resour... more This paper presents a Graph Inference retrieval model that integrates structured knowledge resources, statistical information retrieval methods and inference in a unified framework. Key components of the model are a graph-based representation of the corpus and retrieval driven by an inference mechanism achieved as a traversal over the graph. The model is proposed to tackle the semantic gap problem-the mismatch between the raw data and the way a human being interprets it. We break down the semantic gap problem into five core issues, each requiring a specific type of inference in order to be overcome. Our model and evaluation is applied to the medical domain because search within this domain is particularly challenging and, as we show, often requires inference. In addition, this domain features both structured knowledge resources as well as unstructured text. Our evaluation shows that inference can be effective, retrieving many new relevant documents that are not retrieved by state-of-the-art information retrieval models. We show that many retrieved documents were not pooled by
Following an early claim by Nelson & McEvoy [19] suggesting that word associations can display 's... more Following an early claim by Nelson & McEvoy [19] suggesting that word associations can display 'spooky action at a distance behaviour', a serious investigation of the potentially quantum nature of such associations is currently underway. This paper presents a simple quantum model of a word association system. It is shown that a quantum model of word entanglement can recover aspects of both the Spreading Activation model and the Spooky model of word association experiments.
This paper evaluates the efficiency of a number of popular corpus-based distributional models in ... more This paper evaluates the efficiency of a number of popular corpus-based distributional models in performing discovery on very large document sets, including online collections. Literature-based discovery is the process of identifying previously unknown connections from text, often published literature, that could lead to the development of new techniques or technologies. Literature-based discovery has attracted growing research interest ever since Swanson's serendipitous discovery of the therapeutic effects of fish oil on Raynaud's disease in 1986. The successful application of distributional models in automating the identification of indirect associations underpinning literature-based discovery has been heavily demonstrated in the medical domain. However, we wish to investigate the computational complexity of distributional models for literature-based discovery on much larger document collections, as they may provide computationally tractable solutions to tasks including, predicting future disruptive innovations. In this paper we perform a computational complexity analysis on four successful corpus-based distributional models to evaluate their fit for such tasks. Our results indicate that corpus-based distributional models that store their representations in fixed dimensions provide superior efficiency on literaturebased discovery tasks.
Topic Modeling algorithms are rarely used to support the qualitative content analysis process. Th... more Topic Modeling algorithms are rarely used to support the qualitative content analysis process. The main contributing factors for the lack of mainstream adoption can be attributed to the perception that Topic Modeling produces topics of poor quality and that content analysts do not trust the derived topics because they are unable to supply domain knowledge and interact with the algorithm. In this paper, interactive Topic Modeling algorithms namely Dirichlet-Forrest Latent Dirichlet Allocation and Penalised Non-negative Matrix Factorisation, are evaluated with respect to their ability to aid qualitative content analysis. More specifically, the relationship between interactivity, interpretation, topic coherence and trust in interactive content analysis is examined. The findings indicate that providing content analysts with the ability to interact with Topic Modeling algorithms produces topics that are directly related to their research questions. However, a number of improvements to these algorithms were also identified which have the potential to influence future algorithm development to better meet the requirements of qualitative content analysts.
The question of under what conditions conceptual representation is compositional remains debatabl... more The question of under what conditions conceptual representation is compositional remains debatable within cognitive science. This paper proposes a well developed mathematical apparatus for a probabilistic representation of concepts, drawing upon methods developed in quantum theory to propose a formal test that can determine whether a specific conceptual combination is compositional, or not. This test examines a joint probability distribution modeling the combination, asking whether or not it is factorizable. Empirical studies indicate that some combinations should be considered non-compositionally.
Human memory experiments appear to be generating "nonlocal" effects (Nelson & McEvoy 2007). In th... more Human memory experiments appear to be generating "nonlocal" effects (Nelson & McEvoy 2007). In this paper the possibility that words might be entangled in human semantic space is seriously entertained. This approach leads to a very natural picture of the way in which context might affect word association via the standard interpretation of quantum measurement. Two possible scenarios for testing such a hypothesis are suggested, both based upon potential violations of the CHSH inequality.
Changes introduced as a result of publishing processes such as copy-editing and formatting may no... more Changes introduced as a result of publishing processes such as copy-editing and formatting may not be reflected in this document.
Compositionality is a frequently made assumption in linguistics, and yet many human subjects reve... more Compositionality is a frequently made assumption in linguistics, and yet many human subjects reveal highly non-compositional word associations when confronted with novel concept combinations. This article will show how a non-compositional account of concept combinations can be supplied by modelling them as interacting quantum systems.
Many successful query expansion techniques ignore information about the term dependencies that ex... more Many successful query expansion techniques ignore information about the term dependencies that exist within natural language. However, researchers have recently demonstrated that consistent and significant improvements in retrieval effectiveness can be achieved by explicitly modelling term dependencies within the query expansion process. This has created an increased interest in dependency-based models. State-of-the-art dependency-based approaches primarily model term associations known within structural linguistics as syntagmatic associations, which are formed when terms co-occur together more often than by chance. However, structural linguistics proposes that the meaning of a word is also dependent on its paradigmatic associations, which are formed between words that can substitute for each other without effecting the acceptability of a sentence. Given the reliance on word meanings when a user formulates their query, our approach takes the novel step of modelling both syntagmatic and paradigmatic associations within the query expansion process based on the (pseudo) relevant documents returned in web search. The results demonstrate that this approach can provide significant improvements in web retrieval effectiveness when compared to a strong benchmark retrieval system.
International ACM SIGIR Conference on Research and Development in Information Retrieval, 2013
ABSTRACT The article focuses on how the information seeker makes decisions about relevance. It wi... more ABSTRACT The article focuses on how the information seeker makes decisions about relevance. It will employ a novel decision theory based on quantum probabilities. This direction derives from mounting research within the field of cognitive science showing that decision theory based on quantum probabilities is superior to modelling human judgements than standard probability models [2, 1]. By quantum probabilities, we mean decision event space is modelled as vector space rather than the usual Boolean algebra of sets. In this way,incompatible perspectives around a decision can be modelled leading to an interference term which modifies the law of total probability. The interference term is crucial in modifying the probability judgements made by current probabilistic systems so they align better with human judgement. The goal of this article is thus to model the information seeker user as a decision maker. For this purpose, signal detection models will be sketched which are in principle applicable in a wide variety of information seeking scenarios.
Many successful query expansion techniques ignore information about the term dependencies that ex... more Many successful query expansion techniques ignore information about the term dependencies that exist within natural language. However, researchers have recently demonstrated that consistent and significant improvements in retrieval effectiveness can be achieved by explicitly modelling term dependencies within the query expansion process. This has created an increased interest in dependency-based models. State-of-the-art dependency-based approaches primarily model term associations known within structural linguistics as syntagmatic associations, which are formed when terms co-occur together more often than by chance. However, structural linguistics proposes that the meaning of a word is also dependent on its paradigmatic associations, which are formed between words that can substitute for each other without effecting the acceptability of a sentence. Given the reliance on word meanings when a user formulates their query, our approach takes the novel step of modelling both syntagmatic and paradigmatic associations within the query expansion process based on the (pseudo) relevant documents returned in web search. The results demonstrate that this approach can provide significant improvements in web retrieval effectiveness when compared to a strong benchmark retrieval system.
Applying Belief Revision logic to model adaptive information retrieval is appealing since it prov... more Applying Belief Revision logic to model adaptive information retrieval is appealing since it provides a rigorous theoretical foundation to model partiality and uncertainty inherent in any information retrieval (IR) processes. In particular, a retrieval context can be formalised as a belief set and the formalised context is used to disambiguate vague user queries. Belief revision logic also provides a robust computational mechanism to revise an IR system's beliefs about the users' changing information needs. In addition, information flow is proposed as a text mining method to automatically acquire the initial IR contexts. The advantage of a beliefbased IR system is that its IR behaviour is more predictable and explanatory. However, computational efficiency is often a concern when the belief revision formalisms are applied to large real-life applications. This paper describes our beliefbased adaptive IR system which is underpinned by an efficient belief revision mechanism. Our initial experiments show that the belief-based symbolic IR model is more effective than a classical quantitative IR model. To our best knowledge, this is the first successful empirical evaluation of a logic-based IR model based on large IR benchmark collections.
The context-sensitivity of cognition has been demonstrated across a wide range of cognitive funct... more The context-sensitivity of cognition has been demonstrated across a wide range of cognitive functions such as perception, memory, judgement and decision making. A related term, ‘contextuality’, has appeared from the field of quantum cognition, with mounting empirical evidence demonstrating that cognitive phenomena are sometimes contextual. Contextuality is a subtle notion that influences how we must view the properties of the cognitive phenomenon being studied. This article addresses the questions: What does it mean for a cognitive phenomenon to be contextual? What are the implications of contextuality for probabilistic models of cognition? How does contextuality differ from context-sensitivity? Starting from George Boole’s “conditions of possible experience”, we argue that a probabilistic model of a cognitive phenomenon is necessarily subject to an assumption of realism. By this we mean that the phenomenon being studied is assumed to have cognitive properties with a definite value ...
A large number of studies in cognitive science have revealed that probabilistic outcomes of certa... more A large number of studies in cognitive science have revealed that probabilistic outcomes of certain human decisions do not agree with the axioms of classical probability theory. The field of Quantum Cognition provides an alternative probabilistic model to explain such paradoxical findings. It posits that cognitive systems have an underlying quantum-like structure, especially in decision-making under uncertainty. In this paper, we hypothesise that relevance judgement, being a multidimensional, cognitive concept, can be used to probe the quantum-like structure for modelling users' cognitive states in information seeking. Extending from an experiment protocol inspired by the Stern-Gerlach experiment in Quantum Physics, we design a crowd-sourced user study to show violation of the Kolmogorovian probability axioms as a proof of the quantum-like structure, and provide a comparison between a quantum probabilistic model and a Bayesian model for predictions of relevance.
Deep learning models have achieved high performances across different domains, such as medical de... more Deep learning models have achieved high performances across different domains, such as medical decision-making, autonomous vehicles, decision support systems, etc. Despite this success, the internal mechanisms of these models are opaque because their internal representations are too complex for a human to understand. This makes it hard to understand the how or the why of the predictions of deep learning models. There has been a growing interest in model-agnostic methods that can make deep learning models more transparent and explainable to a user. Some researchers recently argued that for a machine to achieve a certain degree of human-level explainability, this machine needs to provide human causally understandable explanations, also known as causability. A specific class of algorithms that have the potential to provide causability are counterfactuals. This paper presents an in-depth systematic review of the diverse existing body of literature on counterfactuals and causability for explainable artificial intelligence. We performed an LDA topic modeling analysis under a PRISMA framework to find the most relevant literature articles. This analysis resulted in a novel taxonomy that considers the grounding theories of the surveyed algorithms, together with their underlying properties and applications in real-world data. This research suggests that current model-agnostic counterfactual algorithms for explainable AI are not grounded on a causal theoretical formalism and, consequently, cannot promote causability to a human decision-maker. Our findings suggest that the explanations derived from major algorithms in the literature provide spurious correlations rather than cause/effects relationships, leading to sub-optimal, erroneous, or even biased explanations. This paper also advances the literature with new directions and challenges on promoting causability in model-agnostic approaches for explainable artificial intelligence.
We argue that web service discovery technology should help the user navigate a complex problem sp... more We argue that web service discovery technology should help the user navigate a complex problem space by providing suggestions for services which they may not be able to formulate themselves as (s)he lacks the epistemic resources to do so. Free text documents in service environments provide an untapped source of information for augmenting the epistemic state of the user and hence their ability to search effectively for services. A quantitative approach to semantic knowledge representation is adopted in the form of semantic space models computed from these free text documents. Knowledge of the user’s agenda is promoted by associational inferences computed from the semantic space. The inferences are suggestive and aim to promote human abductive reasoning to guide the user from fuzzy search goals into a better understanding of the problem space surrounding the given agenda. Experimental results are discussed based on a complex and realistic planning activity.
Web Services Meet Marketplaces As web services technologies mature, and commercial-scale, service... more Web Services Meet Marketplaces As web services technologies mature, and commercial-scale, service-oriented architectures shift from early adoption to mainstream development, a new revolution of service orientation is emerging. Beyond the orchestration of web services in multi-party business processes, a dedicated treatment for procuring web services into different markets is coming into focus. The first beneficiaries of open procurements of web services are ventures having successfully overcome the dotcom-burst such as Salesforce, StrikeIron, and GrandCentral. 1 These companies leverage XML-based technology to consolidate enterprise application portfolios built by independent software developers for the small to medium markets. Their early successes are paving the way to long-anticipated Amazon/eBay-style marketplaces for web services. For procurement of services in wider scale, traditional assumptions no longer hold. In larger marketplaces, consumers need a richer exposure of semantics in service descriptions and support of fuzzier search goals. In other words, if services are to be delivered independently, their non-functional properties, such as geospatial and temporal availability, methods of charging and payment, security, trust, rights, and penalties, need to be described in a systematic and precise manner. Furthermore, emerging web service usage scenarios, especially in marketplaces, are pushing the boundaries of coordination and the involvement of intermediaries and reusable service delivery components in endto-end transactions. Thus, automated assistance will be required by prospective web service suppliers to integrate "instantly" their services with other services, with intermediaries, and with service delivery components. This is to name just a few of the challenges posed by the new frontier of service-orientation. This paper presents a vision for the next generation of service-oriented systems, which we term web service ecosystems. The paper reviews early manifestations of these systems and identifies challenges that will need to be addressed on the road to the web service ecosystems vision The Rise Of Web Service Ecosystems Service-oriented architecture (SOA) has gained mainstream acceptance as a strategy for consolidating and repurposing legacy applications to be combined with new applications in more dynamic environments, through self-contained, reusable, and configurable services. As fostered through the web services standards stack, services, once in place, can interoperate with other services, be composed into long-running business processes, spanning intra-and inter-organizational boundaries, and be procured through different business domains and market sectors. As web services are exposed and connected with one another, they give rise to service ecosystems. In these ecosystems, services are deployed, published, discovered, delivered to different business channels through specialist intermediaries (e.g., payment, authentication, and mediation services), and monitored. A groundswell of web service ecosystems is emerging on the "dotcom" front, and targeting enterprise applications for the small to medium sector. Following the successes of Amazon and eBay goods marketplaces, the first significant efforts of bringing together web services using more open procurement models are precipitating the long-anticipated internet service marketplace concept. Salesforce.com, with around a half million subscribers, recently announced AppExchange 2 to expand its CRM software base into a full business software portfolio. To achieve this, testing and deployment
This paper presents a Graph Inference retrieval model that integrates structured knowledge resour... more This paper presents a Graph Inference retrieval model that integrates structured knowledge resources, statistical information retrieval methods and inference in a unified framework. Key components of the model are a graph-based representation of the corpus and retrieval driven by an inference mechanism achieved as a traversal over the graph. The model is proposed to tackle the semantic gap problem-the mismatch between the raw data and the way a human being interprets it. We break down the semantic gap problem into five core issues, each requiring a specific type of inference in order to be overcome. Our model and evaluation is applied to the medical domain because search within this domain is particularly challenging and, as we show, often requires inference. In addition, this domain features both structured knowledge resources as well as unstructured text. Our evaluation shows that inference can be effective, retrieving many new relevant documents that are not retrieved by state-of-the-art information retrieval models. We show that many retrieved documents were not pooled by
Following an early claim by Nelson & McEvoy [19] suggesting that word associations can display 's... more Following an early claim by Nelson & McEvoy [19] suggesting that word associations can display 'spooky action at a distance behaviour', a serious investigation of the potentially quantum nature of such associations is currently underway. This paper presents a simple quantum model of a word association system. It is shown that a quantum model of word entanglement can recover aspects of both the Spreading Activation model and the Spooky model of word association experiments.
Much of our understanding of human thinking is based on probabilistic models. This innovative boo... more Much of our understanding of human thinking is based on probabilistic models. This innovative book by Jerome R. Busemeyer and Peter D. Bruza argues that, actually, the underlying mathematical structures from quantum theory provide a much better account of human thinking than traditional models. They introduce the foundations for modelling probabilistic-dynamic systems using two aspects of quantum theory. The first, “contextuality,” is away to understand interference effects found with inferences and decisions under conditions of uncertainty. The second, “quantum entanglement,” allows cognitive phenomena to be modelled in non-reductionist ways. Employing these principles drawn from quantum theory allows us to view human cognition and decision in a totally new light. Introducing the basic principles in an easy-to-follow way, this book does not assume a physics background or a quantum brain and comes complete with a tutorial and fully worked-out applications in important areas of cognition and decision
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Papers by Peter Bruza