Semantic Perception: Converting Sensory Observations to Abstractions

Cont

ext-

Awar

e Co

mpu

ting

26 Published by the IEEE Computer Society 1089-7801/12/$31.00 © 2012 IEEE IEEE INTERNET COMPUTING

C ontext represents the salient aspects of the environment in which a state-ment 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 abstrac-tion conceptualizes some entity in the world that’s known only through sen-sory detection of its observable qualities. Thus, an abstraction is a type of con-text derived through observation. The physical environment that an abstrac-tion represents can be a physical entity,

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 envi-ronment to represent is a patient’s con-dition regarding some disorder, known through observing the patient’s symp-toms 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 Par-simonious Covering Theory.3 PCT uses

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 ThirunarayanKno.e.sis, Wright State University

Semantic Perception: Converting Sensory Observations to Abstractions

IC-16-02-Hens.indd 26 2/10/12 10:29 AM

Converting Sensory Observations to Abstractions

MARCH/APRIL 2012 27

domain-specific background knowledge to deter-mine the best explanation for a set of observa-tions. 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 obser-vations on the Web using background knowledge

and the inference capabilities of an off-the-shelf reasoner. We demonstrate our approach’s util-ity through a use case in the healthcare domain. (In addition, the “Related Work in Semantic Scal-ability 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.)

Related Work in Semantic Scalability through Abstraction

A 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 back-ground knowledge and observations encoded in the Semantic Sensor Network (SSN) ontology to our encoding of Parsimoni-ous 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 environmen-tal data within the area. We then encoded this data with the Resource Description Framework (RDF) and made it acces-sible 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 repre-sentation 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 inter-vals 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 extraordi-nary 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 nec-essary. To explore the storage benefits provided through the generation of abstractions, we defined and compared five stor-age 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 obser-vations include all observations used to derive a relevant abstraction.

References1. H. Patni, C. Henson, and A. Sheth, “Linked Sensor Data,” Proc. Int’l Symp. Collab-

orative 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-networks-

top-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

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

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

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.

IC-16-02-Hens.indd 27 2/10/12 10:29 AM

Context-Aware Computing

28 www.computer.org/internet/ IEEE INTERNET COMPUTING

MotivationIn 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) prom-ise 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 unobtru-sively monitoring a patient’s health, automati-cally 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 Jeop-ardy!, 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 col-laboration with Qualcomm, is sponsoring a competition to create the first functional tri-corder (www.xprize.org/prize-development/life -sciences) — a mobile device that can inexpen-sively 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 fasting-blood-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 deter-mine acceptable cholesterol and blood pressure thresholds to minimize risk factors. This type of contextual knowledge would assist doctors con-siderably by freeing up precious time. Addition-ally, it could help patients themselves to better handle health-related concerns. (An interesting,

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

Generating AbstractionsLet’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 diag-nose 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, con-sider 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 deriv-ing an abstraction is called perception. Given some background knowledge (a set of explana-tory 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 implemen-tation of perception to be useful for real-world situations, such as disease diagnosis, it must meet the following requirements:

1. An 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 influ-enza 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-order-logic sense of the term), but rather an abduc-tive 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 tempera-ture. This property is referred to as graceful degradation with incomplete information, and it’s necessary when observing all possible

IC-16-02-Hens.indd 28 2/10/12 10:29 AM


MARCH/APRIL 2012 29

qualities is impractical (or expensive, due to complex laboratory testing, for example).

3. With additional observations, the set of explan-atory entities (that is, abstractions) should be minimized. More information about the patient’s condition should enable a more accurate diagnosis. For instance, the doc-tor 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 via-ble explanations grows smaller, with additional information reducing incompleteness.

4. Abstractions should be generated effi-ciently. Although the term “efficient” is subjective, we define it to mean algorithmi-cally 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, inte-grates Semantic Web technologies with PCT.

Parsimonious Covering TheoryPCT is an abductive logic framework. Given some background knowledge and a set of obser-vations, 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 net-work 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 hypothesize- and-test inference process and is driven by back-ground knowledge modeled as a bipartite graph relating entities to qualities.

PCT divides diagnostic reasoning into two parts: coverage and parsimony. The coverage cri-terion describes how to generate a set of expla-nations 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 reason-able size, the parsimony criterion describes how to select the best explanations. Researchers have advanced many different parsimony crite-ria: minimum cardinality criterion, subset min-imality (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 embod-ies the first three requirements discussed in the previous section: it’s abductive, it can degrade gracefully with incomplete informa-tion, 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,

IC-16-02-Hens.indd 29 2/10/12 10:29 AM



stuffy nose}, then both a flu and a cold are parsimonious covers. In this case, the doc-tor 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 ful-filling requirement 1.

Semantic Sensor WebThe 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 Seman-tic 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

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 mar-riage of sensor and Semantic Web technolo-gies.6 The encoding of sensor descriptions and sensor observation data with Semantic Web languages enables more expressive representa-tion, 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 mil-lion sensor observations (about 1.7 billion RDF statements).

Integrating PCT with the Semantic Sensor WebUsing RDF and OWL to represent information on the Web — and employing OWL reasoners to infer new information — is gaining sup-port. For this reason, and given the increas-ing 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.

Entity Explains Quality

Fever

Headache

Extreme exhaustion

Severe ache and pain

Mild ache and pain

Stuffy nose

Sneezing

Sore throat

Severe cough

Mild cough

Cold

Flu

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.)

IC-16-02-Hens.indd 30 2/10/12 10:29 AM


MARCH/APRIL 2012 31

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 approxi-mates PCT, because OWL inference doesn’t sup-port 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

inCondition

Resolution FieldOfView

MeasurementCapability

hasMeasurementCapability

MeasurementRange Event

Observation, O&M

Sensor, SensorML

procedure

featureOfInterest

observationResult

observationSamplingTimeSensor

System

Device

SensingDevice

Sensing

Process

hasInputhasOutput

implements

hasSubsystemConcept

SubConcept

DL role

Frequency

Accuracy

Latency

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.

Observation

...

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.)

IC-16-02-Hens.indd 31 2/10/12 10:29 AM



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 knowl-edge and observations will be encoded in RDF and will conform to the SSN ontology. Simi-lar 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

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 statem ent dul: hasQuality(flu, sore-throat) can be interpreted as pct:explains(flu, sore-throat). This mapping enables a simple translation of background knowl-edge 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 prop-erty. 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 gener-ates 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)

Recal l that the set of obser vat ions 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 PCT-OWL, 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

IC-16-02-Hens.indd 32 2/10/12 10:29 AM


MARCH/APRIL 2012 33

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 transla-tion of SSN observations to PCT-OWL embod-ies the final two requirements for a systematic

implementation of perception: it computes solu-tions 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 rep-resented in SSN, PCT, and PCT-OWL.

T he utility of context as a means of represent-ing an environment to properly interpret

statements is well-established. Our implemen-tation of perception for generating abstrac-tions 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 pro-fessionals 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

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) pct:Entity(cold)

Quality dul:Quality(sore-throat) Q = {sore-throat, stuffy-nose, fever}

pct:Quality(sore-throat)

dul:Quality(stuffy-nose) pct:Quality(stuffy-nose)

dul:Quality(fever) pct:Quality(fever)

Relation from entity to quality

dul:hasQuality(flu, sore-throat) X = {E(flu) = {sore-throat, stuffy-nose, fever, ...}, E(cold) = {sore-throat, stuffy-nose, ...}}

pct:explains(flu, sore-throat)

dul:hasQuality(flu, stuffy-nose) pct:explains(flu, stuffy-nose)

dul:hasQuality(flu, fever)… pct:explains(flu, fever) …

Observation ssn:Observation(obs-1) Γ = {sore-throat, stuffy-nose, fever}

pct:Abstraction ≡

∃ pct:explains.{sore-throat}⊓

∃ pct:explains.{stuffy-nose}⊓

$ pct:explains.{fever}

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

ssn:Observation(obs-2)

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

IC-16-02-Hens.indd 33 2/10/12 10:29 AM



facilitate more natural human-machine inter-actions within physical-cyber-social systems. Health 2.0 provides a demonstrable example, in which abstraction plays a vital role in aiding overburdened healthcare professionals.

AcknowledgmentsThis 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).

References1. J. McCarthy, “Notes on Formalizing Context,” Proc.

13th Int’l Joint Conf. Artifi cial Intelligence (IJCAI 93),

vol. 1, Morgan Kaufmann, 1993, pp. 555–560.

2. R. Guha, Contexts: A Formalization and Some Applica-

tions, doctoral dissertation, Dept. of Computer Science,

Stanford University, 1991.

3. J.A. Reggia and Y. Peng, “Modeling Diagnostic Reason-

ing: 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.

5. C. Henson et al., “Representation of Parsimonious

Covering Theory in OWL-DL,” Proc. 8th Int’l Work-

shop 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 Comput-

ing (Kno.e.sis) at Wright State University. His research

interests include knowledge representation and machine

perception to analyze sensor data and manage situa-

tion 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, sen-

sor 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). Thirunara-

yan has a PhD in computer science from the State Uni-

versity 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.

Registration opens mid-December! http://conferences.computer.org/vr/2012/

4-8 March 2012

Costa Mesa, California, USA

IEEE VR 2012 IEEE Virtual Reality 2012

IC-16-02-Hens.indd 34 2/10/12 10:29 AM

Semantic Perception: Converting Sensory Observations to Abstractions

Documents

Transcript of Semantic Perception: Converting Sensory Observations to Abstractions