Semantic Perception: Converting Sensory ... - Semantic Scholar

Context-Aware Computing

Semantic Perception: Converting Sensory Observations to Abstractions An abstraction is a representation of an environment derived from sensor observation data. Generating an abstraction requires inferring explanations from an incomplete set of observations (often from the Web) and updating these explanations on the basis of new information. This process must be fast and efficient. The authors’ approach overcomes these challenges to systematically derive abstractions from observations. The approach models perception through the integration of an abductive logic framework called Parsimonious Covering Theory with Semantic Web technologies. The authors demonstrate this approach’s utility and scalability through use cases in the healthcare and weather domains.

Cory Henson, Amit Sheth, and Krishnaprasad Thirunarayan Kno.e.sis, Wright State University

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ontext represents the salient aspects of the environment in which a statement is uttered or an observation is made.1,2 Context helps us correctly interpret the statement or observation — for example, the statement “I’m hot” can be interpreted differently, depending on whether the speaker is in a doctor’s office or on a beach. Related to context is the concept of abstraction, which acts as a surrogate for a set of related concepts or a recurring pattern. In perceptual theory, an abstraction conceptualizes some entity in the world that’s known only through sensory detection of its observable qualities. Thus, an abstraction is a type of context derived through observation. The physical environment that an abstraction represents can be a physical entity,

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an object, or an event in the world. Observable qualities are properties that sensors (people or machines) can detect or measure. Consider the case of a medical diagnosis as an abstraction. The environment to represent is a patient’s condition regarding some disorder, known through observing the patient’s symptoms and administering lab tests. The diagnosis leads to actionable intelligence for appropriate healthcare. For example, a patient with a sore throat and a stuffy nose might be diagnosed with influenza and prescribed an antiviral medication. In this article, we present an approach to systematically generate abstractions from observation data, grounded in an abductive logic framework called Parsimonious Covering Theory.3 PCT uses

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Converting Sensory Observations to Abstractions

Related Work in Semantic Scalability through Abstraction

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lthough advances in the digitization of health records are progressing, ensuring the security and privacy of such records is a major challenge, making access to health-related data difficult. Therefore, as a proof-of-concept demonstration, we apply our approach to the weather domain and show how abstraction scales with the number of observations. We used the approach described in the main article to translate background knowledge and observations encoded in the Semantic Sensor Network (SSN) ontology to our encoding of Parsimonious Covering Theory (PCT) in the Web Ontology Language (PCT-OWL), and we derive appropriate abstractions. From 1 April to 6 April 2003, a major blizzard hit the state of Nevada. Weather stations collected environmental data within the area. We then encoded this data with the Resource Description Framework (RDF) and made it accessible on the Web as linked data.1 For every two-hour interval and each weather station within a 400-mile radius, we derived an abstraction. In this experiment, an abstraction was a representation of a weather condition (blizzard, flurry, rain storm, clear, and so forth) and was derived from observations such as precipitation, temperature, and wind speed. For 72 time intervals and 516 weather stations, we generated a total of 37,152 abstractions, which found the clear condition 70 percent of the time, and the blizzard condition less than 1 percent of the time. In the digital age, information is generated at an extraordinary pace. More data has been created in the past three years than in the previous 40,000 years. Around 2008, this rate of expansion surpassed the generation rate of storage capacity,

leading to a future in which data overwhelms storage capacity. Within the next few years, the amount of sensor data on the Web will surpass social data and become the dominant source of information.2 Thus, an efficient, scaled representation and interpretation of sensor data has become an important area of research. 3 Abstractions could provide a solution. Consider the example of a weather-alert service that provides an alert in the event of a severe weather condition such as a blizzard. For this application, only the alert-generating abstractions are necessary. To explore the storage benefits provided through the generation of abstractions, we defined and compared five storage configurations useful for different applications. Figure A shows the amount of data generated by each configuration during this experiment. The relevant abstractions include all weather conditions except clear, and the relevant observations include all observations used to derive a relevant abstraction. References 1. H. Patni, C. Henson, and A. Sheth, “Linked Sensor Data,” Proc. Int’l Symp. Collaborative Technologies and Systems (CTS 10), IEEE Press, 2010, pp. 362–370. 2. S. Higginbotham, “Sensor Networks Top Social Networks for Big Data,” GigaOM, 13 Sept. 2010; http://cloud.gigaom.com/2010/09/13/sensor-networkstop-social-networks-for-big-data. 3. C. Henson, K. Thirunarayan, and A. Sheth, “An Ontological Approach to Focusing Attention and Enhancing Machine Perception on the Web,” Applied Ontology, vol. 6, no. 4, 2011, pp. 345–376.

All observations Amount of data generated (number of observations, number of abstractions)

All abstractions All abstractions and all observations Relevant abstractions Relevant abstractions and relevant observations 0

20,000

40,000

60,000

80,000

100,000

120,000

140,000

160,000

Figure A. Amount of data, observations, and abstractions generated in the weather experiment. Notice the order-of-magnitude difference between the number of relevant abstractions and the total number of observations.

domain-specific background knowledge to determine the best explanation for a set of observations. The evolution of cyberphysical systems is enabling more access to sensor observations on the Web. To take advantage of this trend, we show how to approximate PCT via the Web Ontology Language (OWL) to derive abstractions from observations on the Web using background knowledge

and the inference capabilities of an off-the-shelf reasoner. We demonstrate our approach’s utility through a use case in the healthcare domain. (In addition, the “Related Work in Semantic Scalability through Abstraction” sidebar presents a use case in the weather domain, in which we also show how abstraction scales as the number of observations grows.)

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Motivation

In 2009, President Barack Obama and the US Congress passed the Health Information Technology for Economic and Clinical Health (HITECH) Act (http://bit.ly/k-HITECH), which aims to digitize all health records in the US by 2014. Electronic health records (EHRs) promise to be an invaluable tool for patient care by providing doctors with Web-based access and maintenance of their patients’ medical histories (including observations of patient symptoms). With the digitization of health records, we’re now entering a new era, Health 2.0, with machines capable of ubiquitously and unobtrusively monitoring a patient’s health, automatically diagnosing symptoms, and suggesting proper treatment. For example, IBM recently turned the attention of Watson (www.ibm.com/ innovation/us/watson), the computer program that in February 2011 resoundingly defeated two world champions in the game show Jeopardy!, to these tasks. In an initial demonstration, Watson wowed doctors by correctly diagnosing a patient with Lyme disease, identifying a rare link between symptom and disorder.4 This year, the X-Prize Foundation, in collaboration with Qualcomm, is sponsoring a competition to create the first functional tricorder (www.xprize.org/prize-development/life -sciences) — a mobile device that can inexpensively diagnose patients by combining expert systems and medical point-of-care data — awarding US$10 million to the first team to turn a cell phone into a digital doctor. Clearly, technologies such as Watson aren’t meant to replace human doctors, but rather to aid them in diagnosing and providing better patient care. So, what benefits can we gain from using this technology? In a word, context. A machine that can provide an intelligent, initial diagnosis gives doctors a good starting point on which to build. For instance, machines can use blood glucose level indicators (such as fastingblood-sugar or glycosylated-hemoglobin test results) to diagnose and monitor type 2 diabetes and determine the proper medication dosage. They can then use this diagnosis to determine acceptable cholesterol and blood pressure thresholds to minimize risk factors. This type of contextual knowledge would assist doctors considerably by freeing up precious time. Additionally, it could help patients themselves to better handle health-related concerns. (An interesting, 28


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in-depth look at the next few years of innovation in medicine is available at http://bit.ly/ k-Kraft.) Although this article focuses on medical diagnosis, the concept of abstraction can apply to various domains.

Generating Abstractions

Let’s say a patient walks into a doctor’s office with a sore throat and a stuffy nose. On the basis of these symptoms, the doctor might diagnose this sickness as influenza and prescribe an antiviral medication. This could be a valid diagnosis, because influenza would explain the observed qualities. In general, we can say that an entity explains an observed quality, and an observed quality provides evidence for an entity. Before rushing to the pharmacy, however, consider that the common cold could also be a valid diagnosis, because it too would explain both symptoms. We define an abstraction as an entity that explains a set of observed qualities. The process of taking a set of observed qualities and deriving an abstraction is called perception. Given some background knowledge (a set of explanatory relations between entities and qualities) and a set of observed qualities, the perception process identifies a set of entities that explain the set of observed qualities. For an implementation of perception to be useful for real-world situations, such as disease diagnosis, it must meet the following requirements: 1. A n entity represented as an abstraction isn’t implied by the set of observed qualities, but rather is a hypothetical explanation of the observations. For instance, although influenza can explain the observed quality of a sore throat, this doesn’t necessarily imply the existence of the virus. Thus, perception isn’t a deductive process (in the first-orderlogic sense of the term), but rather an abductive process, meaning an inference to the best explanation. 2. Even with an incomplete set of observed qualities, the perception process should still identify a set of explanatory entities. For instance, the doctor was able to provide a diagnosis, as either influenza or the common cold, without knowing the patient’s temperature. This property is referred to as graceful degradation with incomplete information, and it’s necessary when observing all possible IEEE INTERNET COMPUTING

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qualities is impractical (or expensive, due to complex laboratory testing, for example). 3. With additional observations, the set of explanatory entities (that is, abstractions) should be minimized. More information about the patient’s condition should enable a more accurate diagnosis. For instance, the doctor could choose influenza and rule out the common cold, or vice versa. Thus, perception is an antimonotonic process. As the set of observed qualities grows larger, the set of viable explanations grows smaller, with additional information reducing incompleteness. 4. Abstractions should be generated efficiently. Although the term “efficient” is subjective, we define it to mean algorithmically tractable: the process should compute a solution in polynomial time. 5. Finally, the perception process must generate abstractions from observations encoded in Web languages. Much sensor data is encoded in standard Web formats, is made accessible on the Web, and is increasingly annotated with a sensor ontology. With the increased use of digital EHRs, medical observations will soon also be accessible on the Web in these standard formats. Generating abstractions through perception, which overcomes the challenges just described, is an important step toward the vision of Health 2.0. One approach, which we discuss here, integrates Semantic Web technologies with PCT.

Parsimonious Covering Theory PCT is an abductive logic framework. Given some background knowledge and a set of observations, an abductive reasoner computes a set of best explanations. In general, abduction is formalized as Σ ∧ Δ ⊧ Γ, where background knowledge Σ and observations Γ are given, and an explanation Δ is computed (⊧ refers to the first-order logic consequence relation). PCT provides a formal model of diagnostic reasoning that represents knowledge as a network of binary relations. The goal of PCT is to account for observed symptoms (qualities) with plausible explanatory hypotheses (entities). PCT has predominantly been used in medical disease diagnosis. Reasoning in PCT uses a hypothesizeand-test inference process and is driven by background knowledge modeled as a bipartite graph relating entities to qualities.

PCT divides diagnostic reasoning into two parts: coverage and parsimony. The coverage criterion describes how to generate a set of explanations such that each observation is accounted for by an entity in the explanation (where an observation is a quality that has been observed). To reduce the set of explanations to a reasonable size, the parsimony criterion describes how to select the best explanations. Researchers have advanced many different parsimony criteria: minimum cardinality criterion, subset minimality (irredundancy) criterion, and so on. 3 The single-disorder assumption is a simple yet effective parsimony criterion that has proved popular for medical disease diagnosis. It states that explanations may contain only a single disorder. To formalize our approach, consider the process of perception in which background knowledge Σ = 〈E, Q, X〉, observations Γ are given, and abstractions Δ are to be inferred. Specifically, a perception problem P (in PCT) is a 4-tuple 〈E, Q, X, Γ〉, in which E is a finite set of entities, Q is a finite set of qualities, X : E → Powerset(Q) is the explanatory function that maps an entity to the corresponding set of qualities it explains, and Γ ⊆ Q is the set of observations. A Δ ⊆ E is an abstraction of Γ for a problem P = 〈E, Q, X, Γ〉 if and only if Δ covers Γ and satisfies a given parsimony criterion. (Details are available elsewhere.5) Thus, an abstraction is a cover if, for each observation, there is an explains relationship within the background knowledge from an entity contained in the abstraction to the observed quality. (We are implicitly using the one-to-one correspondence between a function over E → Powerset(Q) and its equivalent rendering as a relation over E × Q.) An abstraction is parsimonious (the best) if it contains only a single entity. Thus, an abstraction is a parsimonious cover if it contains only a single entity that explains all observations. The formalization of perception as a PCT problem leads to a solution that embodies the first three requirements discussed in the previous section: it’s abductive, it can degrade gracefully with incomplete information, and it can minimize explanations on the basis of new observations. For example, consider the graphical representation of disorder–symptom relationships in Figure 1. If the set of observations includes {sore throat,

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Entity

Explains

Quality Fever Headache Extreme exhaustion

Flu

Severe ache and pain Mild ache and pain Stuffy nose Sneezing

Cold

Sore throat Severe cough Mild cough

Figure 1. Background knowledge relating a flu and a cold to their symptoms. Notice that both flu and cold explain the qualities sore throat and stuffy nose; however, only flu explains the quality fever. (Data for this figure was obtained from WebMD; www.webmd.com/cold-and-flu/is-it-a-cold-or-flu.)

stuffy nose}, then both a flu and a cold are parsimonious covers. In this case, the doctor hasn’t observed all relevant qualities, but a valid result is still generated, so requirement 2 is fulfilled. If the doctor subsequently observes a fever, then the parsimonious cover is refined to {flu}. Thus, after he or she observes a new quality, the set of explanations is minimized, and requirement 3 is fulfilled. By definition, PCT is an abductive logic framework, thus fulfilling requirement 1.

Semantic Sensor Web The Semantic Web, as described by the W3C Semantic Web Activity (www.w3.org/2001/sw), is an evolving extension of the World Wide Web that aims to formally define the meaning of information on the Web. In practice, the Semantic Web defines several machine-processable languages, including the Resource Description Framework (RDF; www.w3.org/RDF) and OWL (www.w3.org/TR/owl2-primer). RDF is a graph-based language that allows for linking data through named relationships. OWL adds a logical foundation to Semantic Web data. OWL is based on a tractable subset of first-order logic called description logic. The emergence of linked data (http://linkeddata.org) represents significant progress in realizing the 30



vision of the Semantic Web. Linked data aims to enable people and organizations to share and connect structured data on the Web as easily as they share and connect documents today. There is a large and growing collection of interlinked public datasets encoded in RDF, spanning diverse areas such as life sciences, government, geography, and entertainment. The Semantic Sensor Web (SSW) is a marriage of sensor and Semantic Web technologies.6 The encoding of sensor descriptions and sensor observation data with Semantic Web languages enables more expressive representation, advanced access, and formal analysis of sensor resources. Toward this goal, the W3C has developed the Semantic Sensor Network (SSN) ontology,7,8 which can model sensor devices, systems, processes, and observations. Figure 2 provides an overview of this ontology’s main concepts and structure. Using the formal specification of the SSN ontology, we created LinkedSensorData (http:// wiki.knoesis.org/index.php/LinkedSensorData), a dataset comprising descriptions of 20,000 sensors across the US and roughly 160 million sensor observations (about 1.7 billion RDF statements).

Integrating PCT with the Semantic Sensor Web Using RDF and OWL to represent information on the Web — and employing OWL reasoners to infer new information — is gaining support. For this reason, and given the increasing number of observations on the Web, it makes sense to explore using these languages to model the perception process. However, OWL isn’t designed for representing abductive inference. So, existing OWL ontologies have limited ability to formalize perception and derive abstractions. Nevertheless, OWL does provide some of the expressivity required to derive abstractions from observations, and we've developed a suitable encoding of PCT in OWL called PCT-OWL. Translating PCT into OWL lets us use sensor data in standard Semantic Sensor Web format by performing the needed abductive inference with an OWL reasoner. However, scalability considerations might require reconsidering this decision and building custom PCT reasoners that use the semantic sensor data format as an exchange format. IEEE INTERNET COMPUTING

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Observation, O&M inCondition Resolution

MeasurementRange FieldOfView

Event

Frequency Observation

MeasurementCapability

Accuracy

featureOfInterest

Latency

procedure ...

Sensor, SensorML Sensor

hasMeasurementCapability

hasOutput

observationSamplingTime

System hasInput

observationResult

Concept

hasSubsystem

Process

DL role

Device Sensing

SubConcept

implements

SensingDevice

UoM, time, domain concepts, and so on come from other ontologies.

Included will be physical properties, power use, connectors, lifetime, and so on (of devices and systems), mobility, availability, operational ranges, and callibration.

Figure 2. W3C Semantic Sensor Network (SSN) ontology. The ontology models sensor devices, systems, processes, and observations. DL: description logic, SensorML: sensor model language; O&M: observations & measurements; UoM: unit of measure. (From the Semantic Sensor Network XG Final Report7; reprinted with permission.)

Translating PCT to OWL. Researchers have explored integrating OWL with abductive reasoning.9 However, this integration would require modifying OWL syntax or an OWL inference engine. Here, we demonstrate that OWL provides some of the expressivity needed to approximate perception — without extending its syntax or semantics — by outlining a suitable encoding of PCT in OWL.5 Note, however, that the OWL representation discussed only approximates PCT, because OWL inference doesn’t support a hypothesize-and-test inference process. The task of representing PCT in OWL involves encoding the background knowledge Σ and the set of observations Γ in an OWL ontology such that an OWL reasoner can compute abstractions Δ that satisfy both the coverage and parsimony

criteria. To translate the set of entities E, we create a class Entity, and for all e ∈ E, we create an individual instance of type Entity by asserting Entity(e). To translate the set of qualities Q, we create a class Quality, and for all q ∈ Q, we create an individual instance of type Quality by asserting Quality(q). Finally, to translate the set of explains relation instances X, we create an object property explains; and, for all entities in the domain of X and for each q ∈ X(e), we create an explains fact by asserting explains(e, q). To translate the set of observations Γ into OWL, we first select an observation q1 ∈ Γ and create an existentially quantified property restrict ion for the explains relation, $explains.{q1}. For each additional observation qi ∈ Γ (i = 2, ..., n), we create an additional existentially

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quantified property restriction for the explains relation and conjoin it to the previous restriction: $explains.{q1} ⊓ … ⊓ $explains.{qn}. Finally, we create a class Abstraction and define it to be equivalent to the conjunction of restrictions, Abstraction ≡ $explains.{q1} ⊓ … ⊓ $explains. {qn}. To generate abstractions Δ, we execute a query for all individual instances of type Abstraction as Abstraction(?x). Abstraction(e) is a result of this query if and only if {e} is a parsimonious cover. The resulting knowledge base lies in the tractable EL profile of OWL 2. Theorem. Given a PCT problem P = 〈E, Q, X, Γ〉 and its translation o(P) into OWL, Δ = {e} is a PCT explanation if and only if Abstraction(e) is deduced by an OWL-DL reasoner — that is, if and only if o(P) ⊧ Abstraction(e).

Proof. (⇒) If {e} is a parsimonious cover of Γ = {q1, …, qn}, then, by definition, Γ ⊆ X(e). By construction of explains in o(P), e : $explains. {q1} ⊓ … ⊓ $explains.{qn}. Hence, by definition of Abstraction, o(P) ⊧ Abstraction(e) holds. (⇐) To justify our claim that this OWL representation approximates PCT, we verify that all query results satisfy both the coverage and parsimony criteria. To satisfy the coverage criterion, each binding of ?x for the query Abstraction(?x) must be an entity that explains all the observations in Γ. This follows from the definition of Abstraction ≡ $explains.{q1} ⊓ … ⊓ $explains.{qn}. That is, Abstraction(e) implies explains(e, q1) ⊓ … ⊓ explains(e, qn). To satisfy the parsimony criterion, each binding of ?x must be a single disorder. This criterion holds, given that each disorder that binds to ?x is a single individual. Translating SSN to PCT-OWL. As sensor data emerges on the Web, both background knowledge and observations will be encoded in RDF and will conform to the SSN ontology. Similar to the formalization of perception in PCT, the SSN ontology defines concepts for entity (dul:Entity) (where dul is the prefix used for concepts from Dolce Ultralite, www.loa-cnr.it/ ontologies/DUL.owl) and quality (dul:Quality), and it defines a relation between an entity and a quality (dul:hasQuality), borrowing from the Dolce Ultralite foundational ontology. Given the similarity in structure, we can use a set of dul:hasQuality relations as background knowledge through several simple equivalence 32



mappings between SSN and PCT-OWL: dul: Quality ≡ pct:Quality (where pct is the prefix used for concepts from PCT-OWL, described earlier), dul:Entity ≡ pct:Entity, and dul:hasQuality ≡ pct:explains. Thus, the RDF statement dul: hasQuality(flu, sore-throat) can be interpreted as pct:explains(flu, sore-throat). This mapping enables a simple translation of background knowledge from SSN to PCT-OWL. The SSN ontology defines an observation as a situation that describes an observed feature, an observed property, a sensor, the method of sensing used, and a value for the observed property. Currently, however, an ssn:Observation can’t directly yield an abstraction. To accomplish this, we must translate the ssn:Observations to PCT-OWL. Consider the following example, in which a doctor detects sore throat and stuffy nose qualities, and a computer program generates the corresponding SSN observations (where ssn:observedProperty is a relation between an observation and the observed quality): ssn:Observation(obs-1) ssn:observedProperty(obs-1, sore-throat) ssn:Observation(obs-2) ssn:observedProperty(obs-2, stuffy-nose)

Reca l l t hat t he set of obser vat ion s Γ = {q1 … qn}, when translated to PCT-OWL, are encoded as pct:Abstraction ≡ $pct:explains. {q1} ⊓ … ⊓ $pct:explains.{qn}. Given these two observations, the following OWL class is generated to represent the class of abstractions that explains the observations: pct:Abstraction ≡ $pct:explains. {sore-throat } ⊓ $pct:explains. {stuffy-nose}

Assuming the background knowledge from Figure 1 has also been translated into PCTOWL, after executing the query Abstraction(?x), an OWL reasoner will infer the abstractions, cold and flu, as explanations: pct:Abstraction(cold) pct:Abstraction(flu)

Although this is a valid result (that is, both a cold and a flu are parsimonious covers), the doctor might not be satisfied and might want to distinguish IEEE INTERNET COMPUTING

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Converting Sensory Observations to Abstractions Table 1. Background knowledge, observations, and abstractions. Concept

SSN *

PCT

PCT-OWL

Entity

dul:Entity(flu)

E = {flu, cold}

pct:Entity(flu)

dul:Entity(cold) Quality

pct:Entity(cold)

dul:Quality(sore-throat)

Q = {sore-throat, stuffy-nose, fever}

dul:Quality(stuffy-nose) dul:Quality(fever) Relation from entity to quality

Observation

pct:Quality(sore-throat) pct:Quality(stuffy-nose) pct:Quality(fever)

dul:hasQuality(flu, sore-throat) dul:hasQuality(flu, stuffy-nose) dul:hasQuality(flu, fever)…

ssn:Observation(obs-1) ssn:observedProperty(obs-1, sore-throat)

X = {E(flu) = {sorethroat, stuffy-nose, fever, ...}, E(cold) = {sore-throat, stuffynose, ...}}

pct:explains(flu, sore-throat)

Γ = {sore-throat, stuffy-nose, fever}

pct:Abstraction ≡

pct:explains(flu, stuffy-nose) pct:explains(flu, fever) …

∃ pct:explains.{sore-throat}⊓ ∃ pct:explains.{stuffy-nose}⊓

ssn:Observation(obs-2)

$ pct:explains.{fever}

ssn:observedProperty(obs-2, stuffy-nose) ssn:Observation(obs-3) ssn:observedProperty(obs-3, fever) Abstraction

Δ = {flu}

pct:Abstraction(flu)

* SSN: Semantic Sensor Network; PCT: Parsimonious Covering Theory; PCT-OWL: PCT encoded into the Web Ontology Language

between these two explanations. Suppose the doctor takes the patient’s temperature and detects a fever. The corresponding SSN observation is created: ssn:Observation(obs-3) ssn:observedProperty(obs-3, fever)

The translation of this observation, along with the previous two observations, into PCT-OWL results in the following OWL class: pct:Abstraction ≡ $pct:explains. {sore-throat} ⊓ $pct:explains. {stuffy-nose} ⊓ $pct:explains. {fever}

This time, after executing the query Abstraction(?x), an OWL reasoner will infer the abstraction, flu, as the sole explanation: pct:Abstraction(flu)

From this example, we see that the translation of SSN observations to PCT-OWL embodies the final two requirements for a systematic

implementation of perception: it computes solutions in a reasonable time because PCT-OWL lies in the tractable EL profile of OWL 2, and it can use background knowledge and observations encoded in Semantic Web languages. Table 1 provides a more concise example of background knowledge, observations, and abstractions represented in SSN, PCT, and PCT-OWL.

T

he utility of context as a means of representing an environment to properly interpret statements is well-established. Our implementation of perception for generating abstractions satisfies all five of the requirements that we outlined earlier. Furthermore, our approach provides a systematic, domain-independent method for deriving context from sensor data. We’re applying this approach in the domain of healthcare, and we’re working with health professionals to integrate this approach with EHRs. The process of converting a large number of observations into relevant abstractions is a key enabler of our vision of computing for human experience.10 This process provides the context needed to overcome information overload and

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facilitate more natural human-machine interactions within physical-cyber-social systems. Health 2.0 provides a demonstrable example, in which abstraction plays a vital role in aiding overburdened healthcare professionals. Acknowledgments This research was supported in part by US National Science Foundation award no. 1143717 (III: EAGER — Expressive Scalable Querying over Integrated Linked Open Data) and US Air Force Research Lab, Dayton Area Graduate Studies Institute research topic SN08-8 (Architectures for Secure Semantic Sensor Networks for Multilayered Sensing).

References 1. J. McCarthy, “Notes on Formalizing Context,” Proc. 13th Int’l Joint Conf. Artificial Intelligence (IJCAI 93), vol. 1, Morgan Kaufmann, 1993, pp. 555–560. 2. R. Guha, Contexts: A Formalization and Some Applications, doctoral dissertation, Dept. of Computer Science, Stanford University, 1991. 3. J.A. Reggia and Y. Peng, “Modeling Diagnostic Reasoning: A Summary of Parsimonious Covering Theory,” Proc. Ann. Symp. Computer Application in Medical Care, vol. 25, IEEE Press, 1986, pp. 17–29. 4. P. Murray, “Just Months after Jeopardy!, Watson Wows Doctors with Medical Knowledge,” 6 June 2011; http:// bit.ly/k-Watson.

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5. C. Henson et al., “Representation of Parsimonious Covering Theory in OWL-DL,” Proc. 8th Int’l Workshop OWL: Experiences and Directions (OWLED 11), CEUR-WS.org, 2011; www.knoesis.org/library/resource. php?id=1546. 6. A. Sheth, C. Henson, and S.S. Sahoo, “Semantic Sensor Web,” IEEE Internet Computing, vol. 12, no. 4, 2008, pp. 78–83. 7. L. Lefort et al., eds., Semantic Sensor Network XG Final Report, W3C Incubator Group Report, June 2011; www. w3.org/2005/Incubator/ssn/XGR-ssn-20110628. 8. M. Compton et al., “The SSN Ontology of the W3C Semantic Sensor Network Incubator Group," J. Web Semantics, to appear, 2012. 9. C. Elsenbroich, O. Kutz, and U. Sattler, “A Case for Abductive Reasoning over Ontologies,” Proc. 3rd Int’l Workshop OWL: Experiences and Directions (OWLED 06), CEUR-WS.org, 2011; http://ceur-ws.org/Vol-216/ submission_25.pdf. 10. A. Sheth, “Computing for Human Experience: Semantics-Empowered Sensors, Services, and Social Computing on the Ubiquitous Web,” IEEE Internet Computing, vol. 14, no. 1, 2010, pp. 88–91. Cory Henson is a researcher and PhD candidate in the Ohio Center of Excellence in Knowledge-Enabled Computing (Kno.e.sis) at Wright State University. His research interests include knowledge representation and machine perception to analyze sensor data and manage situation awareness. Henson has a BS in computer science and a BS in cognitive science, both from the University of Georgia. Contact him at [email protected]; http:// knoesis.org/researchers/cory. Amit Sheth is the LexisNexis Ohio Eminent Scholar and director of Kno.e.sis at Wright State University. His research interests include Web 3.0 (including the Semantic Web), semantics-empowered social Web, sensor Web, the Web of things, mobile computing, and cloud computing. Sheth has a PhD in computer and information science from Ohio State University. He’s a fellow of IEEE. Contact him at [email protected]; http://knoesis.org/amit. Krishnaprasad Thirunarayan is a professor in Kno.e.sis at Wright State University. His research interests include trusted Semantic Sensor Web, analysis of social media, and information extraction and retrieval (especially knowledge representation and reasoning). Thirunarayan has a PhD in computer science from the State University of New York at Stony Brook. He’s a member of IEEE and the ACM. Contact him at tkprasad@knoesis. org; http://knoesis.org/tkprasad.

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