SEMANTIC NETWORKS

By: Ankita Joshi & Triveni Pal M. Tech. CSE NIT Hamirpur, H.P.

How can we represent knowledge? We need to create a logical view of the data, based on how we want to process it 

Natural language is very descriptive, but doesn’t lend itself to efficient processing 

Semantic networks and search trees are promising techniques for representing knowledge 

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Representational Adequacy: the ability to represent all of the kind of knowledge that are needed in that domain. Inferential Adequacy: the ability to manipulate the representational structures in such a way as to derive new structures corresponding to new knowledge inferred from old.

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Inferential Efficiency : the ability to incorporate into the knowledge structure additional information that can be used to focus the attention of the inference mechanisms in the most promising directions. Acquisitional Efficiency : the ability to acquire new information easily. The simplest case involves direct insertion, by a person, of new knowledge into the database.

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A Semantic Network (SN) is a simple notation scheme for logical knowledge representation. A SN consists of a concepts and relations between concepts.

Representing a SN with a directed graph : o Vertices : denote concepts. o Edges : represent relation between concepts.  The graphical depiction associated with a SN is a significant reason for their popularity. 

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Semantic networks can oshow natural relationships between objects/concepts obe used to represent declarative/descriptive knowledge A node can represent a fact description ophysical object oconcept oevent

An arc (or link) represents relationships between nodes  Uses of Semantic Nets o Coding static world knowledge o Built-in fast inference method (inheritance) o Localization of information

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Such representations have had a long and distinguished history in Philosophy and Science:

 Porphyry’s tree (3rd AD)  Charles Peirce’s existential graphs (1890's) --philosophy/logic  O. Stelz's concept hierarchies (1920's) – psychology  Ross Quillian’s associative memory model (1966) -Psychology/Computer Science 

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first invented for computers by Richard H. Richens of the Cambridge Language Research Unit in 1956 as an "interlingua" for machine translation of natural languages by Robert F. Simmons at System Development Corporation in the early 1960s and later featured prominently in the work of Allan M. Collins and colleagues In the 1960s to 1980s the idea of a semantic link was developed within hypertext systems as the most basic unit, or edge, in a semantic network

Living Organism isa

isa

isa

Animal

Plant Locomotion

Fly

isa

isa

isa

Bird

Locomotion

…

Locomotion

Mammal

isa isa

isa isa

walk Eats

…

Cat family

Swim

Penguin

Eagle

Sparrow

isa

Eats House Cats

isa isa

Fred

Morris

isa

Mice

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“Ram is taller than Shyam .” 

Non appropriate scheme : Taller than

Ram 

Shyam

Appropriate scheme : Ram

Shyam

height

height Greater than

180

value

h1

h2

haspart Animal subclass subclass Reptile

head

Mammal

subclass livesin

Africa

Elephant

size

large

instance Nellie

likes

apples

By traversing network we can find: ◦ That Nellie has a head (by inheritance) ◦ That certain concepts related in certain ways (e.g., apples and elephants).  BUT: Meaning of semantic networks was not always well defined. ◦ Are all Elephants big, or just typical elephants? ◦ Do all Elephants live in the “same” Africa? ◦ Do all animals have the same head?  For machine processing these things must be defined. 

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For an appropriate scheme: ◦ Draw relations on the basic of primitives. ◦ Represent complicated relations with this primitives.

Basic primitive height

height Taller than

Greater than

h1

h2

The ISA (is-a) relation is often used to link instances to classes, classes to superclasses.  Some links (e.g. isPart) are inherited along ISA paths.  The semantics of a SN can be relatively very formal or informal.  often defined at the implementation level 

furniture is - a isPart

Chairs

Seat

is - a

Me is - a

Person

owner

My chair covering

Leather

color

Tan is - a

Brown

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Some times we had to override a relation for an inherited node (e.g travel by).

Eagle

is - a

Birds Panguin Travel by

Walk

is - a

is - a

Travel by

Fly

Animal

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VALVE

partOf

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ENGINE

partOf

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PERSON

Likes

ENGINE CAR

⇒ VALVE

BIRD

Likes

CAR

BIRD ⇒ PERSON

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partOf

WORM

Likes

WORM

Non-binary relationships can be represented by “turning the relationship into an object”  This is an example of what logicians call “reification”. ◦ consider an abstract concept to be real. ◦ We might want to represent the generic give event as a relation involving four things: an agent, a recipient, an object and an activation time. 

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Consider this : “Ravi gave Anil the book.” ◦ Abstract concept (gave) => real. Time

Past

Gave

Recipient

Anil

Agent

Ravi

is - a

Verb Object

The book

Inheritance is a key concept in semantic networks and can be represented naturally by following ISA links.  In general, if concept X has property P, then all concepts that are a subset of X should also have property P.  But exceptions are pervasive in the real world!  In practice, inherited properties are usually treated as default values. If a node has a direct link that contradicts an inherited property, then the default is overridden. 

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Multiple inheritance allows an object to inherit properties from multiple concepts.

Multiple inheritance can sometimes allow an object to inherit conflicting properties. Conflicts are potentially unavoidable, so conflict resolution strategies are needed.

“Every dogs has bitten a postman.”  Is equal to : 

X ( dog(X)

Y ( postman(Y) & bitten(X, Y)))

Represent SN for one (dog, postman).  Quantify the represented SN.  GS is the set of generilized statements that has been quanified. 

Dog

Bit

is - a

is - a

Agent

GS

d

is - a

g

Postman is - a

Object

b

p

 Form 30

John went downtown to deposit his money in the bank.

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Every batsman hit a ball.

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All Batsman like the umpire.

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Take the case of cricket player, create a complete semantic with problem definition and different queries.

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1. WordNet     

WordNet, a lexical database of English. It groups English words into sets of synonyms called synsets, provides short, general definitions, and records the various semantic relations between these synonym sets. Some of the most common semantic relations defined are  meronymy  holonymy  hyponymy  hypernymy  synonymy  antonymy

(A is part of B, i.e. B has A as a part of itself), (B is part of A, i.e. A has B as a part of itself), (or troponymy) (A is subordinate of B; A is kind of B), (A is superordinate of B), (A denotes the same as B) and (A denotes the opposite of B).

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WordNet properties have been studied from a network theory perspective and compared to other semantic networks created from Roget's Thesaurus and word association tasks. From this perspective the three of them are a small world structure. These have expressive power equal to or exceeding standard firstorder predicate logic. Unlike WordNet or other lexical or browsing networks, semantic networks using these representations can be used for reliable automated logical deduction. Some automated reasoners exploit the graph-theoretic features of the networks during processing.

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Gellish English with its Gellish English dictionary, is a formal language that is defined as a network of relations between concepts and names of concepts. Gellish English is a formal subset of natural English, just as Gellish Dutch is a formal subset of Dutch, whereas multiple languages share the same concepts. Other Gellish networks consist of knowledge models and information models that are expressed in the Gellish language. A Gellish network is a network of (binary) relations between things. Each relation in the network is an expression of a fact that is classified by a relation type. Each relation type itself is a concept that is defined in the Gellish language dictionary. Each related thing is either a concept or an individual thing that is classified by a concept. The definitions of concepts are created in the form of definition models (definition networks) that together form a Gellish Dictionary. A Gellish network can be documented in a Gellish database and is computer interpretable

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The Hindi WordNet is a system for bringing together different lexical and semantic relations between the Hindi words. It organizes the lexical information in terms of word meanings and can be termed as a lexicon based on psycholinguistic principles. In the Hindi WordNet the words are grouped together according to their similarity of meanings. Two words that can be interchanged in a context are synonymous in that context. For each word there is a synonym set, or synset, in the Hindi WordNet, representing one lexical concept. This is done to remove ambiguity in cases where a single word has multiple meanings. Synsets are the basic building blocks of WordNet. The Hindi WordNet deals with the content words, or open class category of words. Thus, the Hindi WordNet contains the following category of wordsNoun, Verb, Adjective and Adverb.

A Very-Large Semantic Network of Common Sense Knowledge Supports many practical textual-reasoning tasks over realworld documents right out-of-the-box (without additional statistical training) including topic-jisting (e.g. a news article containing the concepts, “gun,” “convenience store,” “demand money” and “make getaway” might suggest the topics “robbery” and “crime”), affect-sensing (e.g. this email is sad and angry), analogy-making (e.g. “scissors,” “razor,” “nail clipper,” and “sword” are perhaps like a “knife” because they are all “sharp,” and can be used to “cut something”), text summarization contextual expansion causal projection cold document classification and other context-oriented inference available in two versions: concise (200,000 assertions) and full (1.6 million assertions). Commonsense knowledge in ConceptNet encompasses the spatial, physical, social, temporal, and psychological aspects of everyday life.

 ArtsSemNet

is a lexical reference system for the fine arts terminology in Bulgarian and Russian  The terms are organized into a semantic network on the base of several important lexical relations: .Polysemy .Antonymy  Provides

.Homonymy .Hyponymy

.Synonymy

a specialised term browser for search and navigation between the terms and the corresponding terminological relations

used ArtsDict for the extraction of homonyms, synonyms and polysemous terms  For the extraction of hyponyms and antonyms we used two techniques:  We

o A formal technique for extraction of hyponyms sharing a common term-element (suffix/stem, affix, …) • Given a target term-element ArtsDict generates a list of terms from the dictionary that contain it • The list is manually examined afterwards

o A semantic technique (based on LSA) for extraction of hyponyms/antonyms with no common termelement

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Searching for terms: o Exact and inexact searching Browsing the terms: o Term glosses list o Homonyms list o Absolute synonyms list, relative synonyms list o Antonym chains o Hyponym chains (with target term hypernym or cohyponym) Supports changes between languages: o Russian and Bulgarian are supported

hasAuthor

Person term

ETD Metadata

occursInAbstract

hasAbstract

id

term

Abstract hasSubject Subject

occursInAbstract

describes ETD Doc id

hasParagraph hasChapter hasSection Paragraph Section Chapter

cites Section Paper id

…

term

term occursInSubject term

Paragrap h … term

SNS contains a bi-lingual (German/English) semantic network which consists of three components: the Environmental Thesaurus UMTHES® with more than 50,000 inter-networked terms. (Descriptors and NonDescriptors).  the Geo-Thesaurus-Environment (GTU) with more than 25,000 geographic names and the spatial intersections of all these places.  an Environmental Chronology containing more than 600 contemporary and historical events that affected the environment. 

Global Biodiversity Initiative Facility (GBIF)  MMI has long considered a semantic component critical to enabling the highest levels of data interoperability.  To that end, MMI has developed a set of guidance documents, worked with the marine science community to establish a set of best practices, and developed tools to allow users to work with semantic technologies  The resulting Semantic Framework facilitate data interoperability in the marine science community while providing linkages to the broader semantic web. 

Sentient Buildings that Sense, Think, and Adapt

A semantic network represents events and their dependencies in our everyday lives. (left)；The user interface of the ContextSense system shows how to guess user situation, intention, and activities in space. (right)

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The global environment is lying on trans-disciplinary fields, such as meteorology, hydrology, geology, geography, agriculture, biology, and so on. Measures of the global environmental problems, such as climate change, global warming, various disasters, and so on. One of the key issues is data interoperability arrangement under the transdisciplinary condition. a semantic network dictionary constructed for information sharing by using a Semantic MediaWiki, which helps to gather ontological information and associations for data interoperability among diversified and distributed data sources. There are a few key requirements of the semantic network dictionary: reliability, simple structure, and easy browsing and modification. Similar works SWEET (Semantic Web for Environment and Technology) by NASA (National Aeronautics and Space Administration) FAO (Food and Agriculture Organization of the United Nations) based on AGROVOC, that is a multilingual, structured and controlled vocabulary designed to cover the terminology of all subject fields in agriculture, forestry, fisheries, food and related domains .

The graph representation is developed by KeyGraph that is open source of Java library. XML data that is constructed in the Wiki is visualized with the result of information retrieval by the reverse dictionary. All the related terms from various ontologies and terminologies are represented at once.

One of the examples of graph representation is a term from land use classification schema in Thailand and Indonesia. The term “water body” land use class can be found in both countries. Apparently, both land use classes are the same, but the level of hierarchy is a bit different in each classification schema. In the case of Indonesian land use, “water body” does not include watercourses, but “water body” in Thailand includes all water-related geographical features. Consequently, graph representation proves a clear distinction between the two terms. Then, the new information such as the relations of “water body” in both countries can be created that “water body” class in Thailand is the same as “water” class in Indonesia. This kind of information is treated as newly-created ontological information, and is added through the Semantic MediaWiki. The ontological information can grow autonomously by adding relations, becoming more and more useful.

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Investigates techniques to automate the analysis of (Dutch) newspaper articles. Semantic Web and Natural Language Processing techniques to solve problems in communication, especially Political Communication such as newspaper coverage of election campaigns. This allow questions about, for example, the relation between media and politics and the objectivity of media to be answered more easily and more systematically. Training statistical models (possibly Maximum Entropy models) on a corpus of manually annotated newspaper articles that has been created in the past decade. In order to improve performance, number of features containing linguistic and background knowledge is included

The Axon Idea Processor is developed entirely in Visual Prolog, provides an environment that supports the thinking processes. Helps to create, communicate, explore, plan, draw, compose, design and learn. Axon also provides a variety of tools for working with ideas starting with a blank screen or using templates. Axon requires no special knowledge of particular techniques. Axon enables you to: oWork with ideas & concepts rather than words. oSee the Big Picture and not get lost in details. oAnalyze and solve more complex problems. oStimulate creativity and discovery. oEffectively amplify your mental potential. oFocus attention and minimize distractions. oReduce mental fatigue and writers' blocks.

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The knowledge structures created in Semantica are based on an adaptation of semantic network theory, which attempts to replicate the way that humans observe, organize and store knowledge mentally. All Semantica Knowledge Structures are composed of four basic primitive elements: Concepts: Basically any idea unit that can be described in language (person, place, thing, event, etc). Relation Types: An unambiguous, bi-directional relationship that connects any two related concepts. Relation types may be symmetric (the same in each direction), or Asymmetric (different in each direction). Triplets:Triplets are the building blocks of Semantica. They are a uniting Element formed when two Concepts are joined by a Relation Type. A triplet should be thought of as a sentence, whether it would make sense to pronounce it or not. Below is an example of a bi-directional sentence, as seen from the triplet's two reversed Graphic Frame views. The Relation Type ray's arrows extend from the Central Concept (Subject) to the Related Concept (Object). Note that the Relation Type is grammatically reversed, while the Concepts remain identical. Knowledge Objects: Any file or image on the visible computer screen can be easily dragged and attached as a knowledge object to any other element within Semantica. These can be text files, images or URL direct links to websites. Simply clicking on any icon will open the Knowledge Object or navigate to an active website.

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Visual and highly interactive framework for manipulating and analyzing data from multiple sources, whether structured databases or unstructured text documents. Semantica's abstract data model, based on semantic networks, provides powerful extended fusion and analytical capabilities through improved automation. This data model enables analysts to quickly perform sophisticated link and node analysis of vehicles, transport, cargo, people, places, organizations, etc. without spending hours on data transformation. Semantica provides an easy to use interface that helps analysts see the relationships among entities contained in information by layering Time and Space and Relationships between all entities of interest. The Semantica software enables access to disparate information sources that have not been brought together in a single analytical user-defined operating picture. Already fielded with defense/intelligence-related agencies and groups. This reduces both the risks and the time required to achieve successful field deployment.

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Tracking, storing, visualizing, and sharing information about aircraft, vessels, vehicles, monetary systems or other modes of physical or electronic transport, the commodities or cargo transported with them, and the individuals doing the transportation all rely on being able to store the information in a manner that helps analysts to quickly discover the relationships among the three. Ability to store information about suspected drug traffickers, their transportation routes, vehicles used, and the dates and times of the specific transactions for quite some time. has more recently been applied to tracking vessels, cargo, individuals, and organization that are related to each of the above. The tool can easily store and provide link analysis of many other kinds of cargo, people, locations, and organizations of interest. For each of the associated types of nodes, the system can also track all of the various pieces of metadata associated with those concepts. For cargo, as an example, the tool can capture what vessel the cargo container is on, what the contents of the container are, who shipped the cargo, who the planned recipient is, the date and time it was shipped, as well as the date and time the cargo was received. This would enable the analyst to have a visual display of the links or connections relating to the cargo container in question, as well as any of the associated information in just a mouse click or two. Using Semantica Pro's built in geo-spatial and temporal capabilities; analysts can quickly see their network on a map and show its changes over time. This is critical when looking for patterns that can only be revealed when watching how a network transforms over time and n relation to the specific places or regions on a map or other imagery.

There are also elaborate types of semantic networks connected with corresponding sets of software tools used for  lexical knowledge engineering, like the Semantic Network Processing System (SNePS) of Stuart C.Shapiro  the MultiNet paradigm of Hermann Helbig, especially suited for the semantic representation of natural language expressions and used in several NLP applications.

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The semantics behind a knowledge representation model depends on the way that it is used (implemented). Notation is irrelevant!

Whether a statement is written in logic or as a semantic network is not important -- what matters is whether the knowledge is used in the same manner. Most knowledge representation models can be made to be functionally equivalent. It is a useful exercise to try converting knowledge in one form to another form. From a practical perspective, the most important consideration usually is whether the KR model allows the knowledge to be encoded and manipulated in a natural fashion.

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Some types of properties are not easily expressed using a semantic network. For example: negation, disjunction, and general non-taxonomic knowledge. There are specialized ways of dealing with these relationships, for example partitioned semantic networks and procedural attachment. But these approaches are ugly and not commonly used. Negation can be handled by having complementary predicates (e.g., A and NOT A) and using specialized procedures to check for them. Also very ugly, but easy to do. If the lack of expressiveness is acceptable, semantic nets have several advantages: inheritance is natural and modular, and semantic nets can be quite efficient.

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As we stated before, semantic networks and frames are often used because inheritance is represented so naturally. But rule based systems can also be used to do inheritance!

Semantic networks (and frames) have an implementation advantage for inheritance because special-purpose algorithms can be used to follow the ISA links.

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Rules are appropriate for some types of knowledge, but do not easily map to others. Semantic nets can easily represent inheritance and exceptions, but are not well-suited for representing negation, disjunction, preferences, conditionals, and cause/effect relationships. Frames allow arbitrary functions (demons) and typed inheritance. Implementation is a bit more cumbersome.

We see hierarchical organizations in the real world all the time. They may not be "pure" hierarchies, but they're hierarchical in spirit at least. It might be easier to think of these things as "networks" instead of hierarchies. Take for example the common dictionary. At first glance, it looks like a very linear organization of the words in our language. But what a dictionary really specifies is a very complex and somewhat hierarchical map of the relationships between the words in our language.

PHILOSOPY

Frame system = Semantic Net + structured nodes + procedural attachment

INFERENCE PROCESSES:

◦ Inheritance ◦ Procedural attachment (demons) ◦ Frame Matching (a type of unification)

HISTORY ◦ ◦ ◦ ◦ ◦

Minsky, 1975 (first ideas) Bobrow & Winograd, 1977 (KRL) By 1980 in wide-spread use (FRL, SRL, Units) By 1985 in robust packaged form (CRL, KEE, FrameKit,…) By 1990 in general use for knowledge bases, and evolved into object-oriented data bases (OODBs)

FRAME

SLOT

FACET

FILLER

[PC

[isa [manufacturer [retail-price

[markup [owner

[value [type-r [puller [range-min [range-max [unit [value [type-r

COMPUTER]] COMPANY]] (* &markup &wholesale)] 500] 10000] USD]] 1.5]] LEGAL-ANIMATE]]]

[isa [manufacturer [processor [wholesale

[value [value [value [value

PC]] DELL]] pentium-4L]] 1400]]]

[DELL-150/L

 Semantic

vs. Episodic

◦ Events vs. Facts ◦ Temporal and Causal sequences ◦ Use Semantic memory as component

 Scripts

◦ Causally-connected event sequence ◦ Generalized by alternate paths:  Tree or DAG structure  Conditionals on branches

◦ Script-role generalization

 Constants Typed variables with restrictions  Climb a frame hierarchy

 Script

Application Process

◦ Match Trigger events, including roles ◦ Instantiate forwards and backwards ruling out alternate branches ◦ Interpolation inference (abduction) ◦ Extrapolation inference (prediction)

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Because the syntax is the same ◦ We can guess that Julia’s age is similar to Bryan’s

Graphical representation (a graph) ◦ Links indicate subset, member, relation, ...  Equivalent to logical statements (usually FOL) ◦ Easier to understand than FOL? ◦ Specialised SN reasoning algorithms can be faster  Example: natural language understanding ◦ Sentences with same meaning have same graphs ◦ e.g. Conceptual Dependency Theory (Schank) 

Disadvantages of a semantic network incomplete (no explicit operational/procedural knowledge)  lack of standards, ambiguity in node/link descriptions  not temporal (i.e. doesn't represent time or sequence) 

Coclusion 

semantic networks are mainly used as an aid to analysis to visually represent parts of the problem domain. The `knowledge' can be transformed into rules or frames for implementation