Semantic similarity

Semantic similarity

Semantic similarity, is a concept whereby a set of documents or terms within term lists are assigned a metric based on the likeness of their meaning / semantic content.

According to some opinions the concept of semantic similarity is different from semantic relatedness because semantic relatedness includes concepts as antonymy and meronymy, while similarity doesn't. However, much of the literature uses these terms interchangeably, along with terms like semantic distance. In essence, semantic similarity, semantic distance, and semantic relatedness all mean, "How much does term A have to do with term B?"

The answer to this question, as given by the many automatic measures of semantic similarity/relatedness, is usually a number, usually between -1 and 1, or between 0 and 1, where 1 signifies extremely high similarity/relatedness, and 0 signifies little-to-none.

An intuitive way of displaying terms according to their semantic similarity is by grouping together closer related terms and spacing more distantly related ones wider apart. This is common - if sometime subconscious - practice for mind maps and concept maps.

Concretely, this can be achieved for instance by defining a topological similarity, by using ontologies to define a distance between words (a naive metric for terms arranged as nodes in a directed acyclic graph like a hierarchy would be the minimal distance (in separating edges) between the two term nodes), or using statistical means such as a vector space model to correlate words and textual contexts from a suitable text corpus (co-occurrence).

EXAMPLE RESEARCH

1. Algorithmic Detection of Semantic Similarity
Automatic extraction of semantic information from text and links in Web pages is key to improving the quality of search
results. However, the assessment of automatic semantic measures is limited by the coverage of user studies, which
do not scale with the size, heterogeneity, and growth of the Web. Here we propose to leverage human-generated metadata — namely topical directories — to measure semantic relationships among massive numbers of pairs of Web pages or topics. The Open Directory Project classifies millions of URLs in a topical ontology, providing a rich source from which semantic relationships between Web pages can be derived. While semantic similarity measures based on taxonomies (trees) are well studied, the design of well-founded similarity measures for objects stored in the nodes of arbitrary ontologies (graphs) is an open problem. This paper defines an information-theoretic measure of semantic similarity that exploits both the hierarchical and non-hierarchical structure of an ontology. An experimental study shows
that this measure improves significantly on the traditional taxonomy-based approach. This novel measure allows us to
address the general question of how text and link analyses can be combined to derive measures of relevance that are in good agreement with semantic similarity. Surprisingly, the traditional use of text similarity turns out to be ineffective
for relevance ranking.


2.Roget’s Thesaurus and Semantic Similarity
A system that measures semantic similarity using a computerized 1987 Roget's Thesaurus, and evaluated it by
performing a few typical tests. We compare the results of these tests with those produced by WordNet-based similarity measures. One of the benchmarks is Miller and Charles’ list of 30 noun pairs to which human judges had assigned similarity measures. We correlate these measures with those computed by several NLP systems. The 30 pairs can be traced back to Rubenstein and Goodenough’s 65 pairs, which we have also studied. Our Roget’s-based system gets correlations of .878 for the smaller and .818 for the larger list of noun pairs; this is quite close to the .885 that Resnik obtained when he employed humans to replicate the Miller and Charles experiment. We further evaluate our measure by using Roget’s and WordNet to answer 80 TOEFL, 50 ESL and 300 Reader’s Digest questions: the correct synonym must be selected amongst a group of four words. Our system gets 78.75%, 82.00% and 74.33% of the questions respectively.

3.A new method to measure the semantic similarity of GO terms
Motivation: Although controlled biochemical or biological vocabularies, such as Gene Ontology (GO) (http://www.geneontology.org), address the need for consistent descriptions of genes in different data sources, there is still no effective method to determine the functional similarities of genes based on gene annotation information from heterogeneous data sources.
Results: To address this critical need, we proposed a novel method to encode a GO term's semantics (biological meanings) into a numeric value by aggregating the semantic contributions of their ancestor terms (including this specific term) in the GO graph and, in turn, designed an algorithm to measure the semantic similarity of GO terms. Based on the semantic similarities of GO terms used for gene annotation, we designed a new algorithm to measure the functional similarity of genes. The results of using our algorithm to measure the functional similarities of genes in pathways retrieved from the saccharomyces genome database (SGD), and the outcomes of clustering these genes based on the similarity values obtained by our algorithm are shown to be consistent with human perspectives. Furthermore, we developed a set of online tools for gene similarity measurement and knowledge discovery.

Reference:
wikipidia
www2005.org
Roget’s Thesaurus and Semantic Similarity
A new method to measure the semantic similarity of GO terms

CDMA technology

Multiple Access

The concept behind multiple access is to permit a number of users to share a common channel. The two traditional ways of multiple access are Frequency Division Multiple Access (FDMA) and Time Division Multiple Access (TDMA).

FDMA
In Frequency Division Multiple Access, the frequency band is divided in slots. Each user gets one frequency slot assigned that is used at will. It could be compared to AM or FM broadcasting radio where each station has a frequency assigned. FDMA demands good filtering.


TDMA
In Time Division Multiple Access, the frequency band is not partitioned but users are allowed to use it only in predefined intervals of time, one at a time. Thus, TDMA demands synchronization among the users.



CDMA
CDMA, for Code Division Multiple Access, is different than those traditional ways in that it does not allocate frequency or time in user slots but gives the right to use both to all users simultaneously. To do this, it uses a technique known as Spread Spectrum. In effect, each user is assigned a code which spreads its signal bandwidth in such a way that only the same code can recover it at the receiver end. This method has the property that the unwanted signals with different codes get spread even more by the process, making them like noise to the receiver.

Spread Spectrum
Spread Spectrum is a mean of transmission where the data occupies a larger bandwidth than necessary. Bandwidth spreading is accomplished before the transmission through the use of a code which is independent of the transmitted data. The same code is used to demodulate the data at the receiving end. The following figure illustrate the spreading done on the data signal x(t) by the spreading signal c(t) resulting in the message signal to be transmitted, m(t).


Originally for military use to avoid jamming (interference created on purpose to make a communication channel unusable), spread spectrum modulation is now used in personal communication systems for its superior performance in an interference dominated environment.

Other Info:
Spread Spectrum Techniques by Dr.Jack Glas, an excellent introduction to spread spectrum

Processing Gain
In spread spectrum, the data is modulated by a spreading signal which uses more bandwidth than the data signal. Since multiplication in the time domain corresponds to convolution in the frequency domain, a narrow band signal multiplied by a wide band signal ends up being wide band. One way of doing this is to use a binary waveform as a spreading function, at a higher rate than the data signal.


Here the three signals corresponds to x(t), c(t) and m(t) discussed above. The first two signals are multiplied together to give the third waveform.

Bits of the spreading signal are called chips. On the above figure, Tb represents the period of one data bit and Tc represents the period of one chip. The chip rate, 1/Tc, is often used to characterize a spread spectrum transmission system.

The Processing Gain or sometimes called the Spreading Factor is defined as the ratio of the information bit duration over the chip duration:

PG = SF = Tb / Tc

Hence, it represents the number of chips contained in one data bit. Higher Processing Gain (PG) means more spreading. High PG also means that more codes can be allocated on the same frequency channel (more on that later).

Other Info: tsp.ece.mcgill.ca

CDMA Technology
The world is demanding more from wireless communication technologies than ever before as more people around the world are subscribing to wireless. Add in exciting Third-Generation (3G) wireless data services and applications - such as wireless email, web, digital picture taking/sending, assisted-GPS position location applications, video and audio streaming and TV broadcasting - and wireless networks are doing much more than just a few years ago.

This is where CDMA technology fits in. CDMA consistently provides better capacity for voice and data communications than other commercial mobile technologies, allowing more subscribers to connect at any given time, and it is the common platform on which 3G technologies are built.

CDMA is a "spread spectrum" technology, allowing many users to occupy the same time and frequency allocations in a given band/space. As its name implies, CDMA (Code Division Multiple Access) assigns unique codes to each communication to differentiate it from others in the same spectrum. In a world of finite spectrum resources, CDMA enables many more people to share the airwaves at the same time than do alternative technologies.

The CDMA air interface is used in both 2G and 3G networks. 2G CDMA standards are branded cdmaOne and include IS-95A and IS-95B. CDMA is the foundation for 3G services: the two dominant IMT-2000 standards, CDMA2000 and WCDMA, are based on CDMA.

cdmaOne: The Family of IS-95 CDMA Technologies
cdmaOne describes a complete wireless system based on the TIA/EIA IS-95 CDMA standard, including IS-95A and IS-95B revisions. It represents the end-to-end wireless system and all the necessary specifications that govern its operation. cdmaOne provides a family of related services including cellular, PCS and fixed wireless (wireless local loop).

CDMA2000: Leading the 3G revolution
CDMA2000 represents a family of ITU-approved, IMT-2000 (3G) standards and includes CDMA2000 1X and CDMA2000 1xEV technologies. They deliver increased network capacity to meet growing demand for wireless services and high-speed data services. CDMA2000 1X was the world's first 3G technology commercially deployed (October 2000).

CDMA Deployments
CDMA is the fastest growing wireless technology and it will continue to grow at a faster pace than any other technology. It is the platform on which 2G and 3G advanced services are built.

Please visit the TIA website for more information on CDMA standards.

Other Info: CDG.ORG

4 Papers about Wireless RFID

Today search for the wireless-RFID paper from Google.There are several interested paper like these :-
(You can click the name to the source of links)
1. Wireless RFID Networks for Real-Time Customer Relationship Management.
A new system for real-time customer relationship management is based on deploying a network of RFID readers
throughout an environment. Information about the presence or lingering of participating
customers at different times of day is collected providing valuable
marketing information for better service provision. The implementation of the
proposed system includes a database management program and an intuitive
user interface allowing real-time access to the data acquired by the network.

In this paper the interested issue is Cost Comparison, about estimate the cost of deploying our wireless sensor network based
system and compare it with traditional systems. It had 2 tables comparison including
The major cost components in traditional systems and in the proposed technique and the costs are likely to be considerably less in the proposed system.

2.Cisco Case Study.
Full Name:-
Wireless Case Study: How Cisco Tracks RFID with Active RFID and Wireless LANs.

Active RFID tags and WLANs ensure compliance with corporate finance and government regulations.

This wireless case study describes an RFID project in Cisco India that evaluated how well the Cisco wireless LAN (WLAN) could detect equipment with active RFID tags, and how well it communicated data about equipment location to an internally developed asset tracking application.

This WLAN case study describes the best practices and benefits of the solution including:

* Identifying, in near-real time, the location of valuable equipment assets for consistent and centralized tracking
* Helping Cisco staff meet regulatory and audit requirements
* Applying Cisco IT’s existing investment in wireless networks

3. US Patent 7100835 - Methods and apparatus for wireless RFID cardholder signature and data entry

Inventor(s) * Selker, Edwin Joseph

About: A radio operated data card whose outer jacket forms a sealed protected housing for internal electrical components, including an RFID integrated circuit which incorporates data storage and a radio frequency transceiver, and one or more on-card antenna structures. Manually operated electrical switching elements, or antenna structures which are responsive to the positioning of conductive members, such as the human hand, at particular locations on or near the surface of the card, are connected to the on-card electronic circuitry. The switching elements or antenna elements are selectively operated by the cardholder who manipulates the card in predetermined ways to generate data signals that may be used to activate the card, store data in the card, or transmit data to the reader.

The Finally,RFID with Supply Chain Management...
4. Using RFID in Supply Chain Management for Customer Service
By:Hsiao-Tseng Lin; Wei-Shuo Lo; Chiao-Ling Chiang
Publish:Systems, Man and Cybernetics, 2006. SMC apos;06. IEEE International Conference on Volume 2, Issue , 8-11 Oct. 2006 Page(s):1377 - 1381
Digital Object Identifier 10.1109/ICSMC.2006.384908

Summary:Radio Frequency Identification (RFID) technology has been used since the Second World War, recent years it is widely employed in many areas such as highway tolls, in tracking livestock movements, in tracking air freight, medical care, air cargo operations, and in motor car manufacturing [1]-[3]. The year 2003 was crucial for RFID technology since both Wal-Mart and the Department of Defense of U.S.A. announced that they will be using RFID tags for pallets and cases in conducting business with their major suppliers. And even more, Wal-Mart suggested that it would extend the requirements to all of his suppliers by 2006 [4]. Thus, Wal-Mart could readily reduce their time of control and identification process for the suppliers and products. This may eventually lead to the accomplishment of their goal for Quick Response (QR) in their Supply Chain Management (SCM) and allow business to improve their customer relationship management (CRM) as well. In this paper, we discuss how the Radio Frequency Identification technology is used to solve the problems in supply chain management, the advantages of a business adopting RFID, and the relationship between RFID and CRM.