Information extraction is systematic process of
extracting structured information from documents which has
both unstructured and semi structured data set. Data available
over the web is unstructured which is processed and delivered that
may be challenging due to massive data over web. Bigdata
analytics approach is used in the computation field where massive
data is managed and processed as information. Data from various
sources like industries, institutes are processed using algorithms
in efficient means employing web of things or Internet of things
used to mine such a large data. Bio inspired algorithms have
evolved from application of heuristic approaches to
meta-heuristic and hyper-heuristic methodologies. Bio inspired
techniques are categorized into human inspired algorithms,
Swarm Intelligence algorithms, evolutionary algorithms and
ecology based algorithms. Genetic algorithms are purely heuristic
in nature and are employed for computation and extracting
information and from big data. This improves the computation
speed effectively for extracting web related information as
evolutionary algorithm resolves information extraction problems.
The Ant colony and Particle Swarm Intelligence algorithms are of
meta-heuristic in nature. The Cuckoo search, Artificial Bee
Colony, Firefly algorithm and Bat algorithms are of hyper
heuristic in nature i.e., they employ a combination of methods.
Web information extraction using bio inspired concepts and
genetic operators increases efficiency, capability to search
particular information in massive data in web. Some of the tools
that are available for data extraction and mining are DataMelt,
Apache Mahout, Weka, Orange and Rapid Miner for enhancing
web data extraction efficiency. This survey on bio inspired
methodologies can be extended to parameter tuning and
controlling is another big strategy that can be implemented, in
addition to convergence speed up.